Extinction and End Games

Recently Jeff Moss gave an introduction to the opening of Black Hat DC 2009, in which he essentially asked "is there any problem in security that has been definitively crushed or completely eradicated? Is there a problem from 10 years ago that is no longer a concern?" Specific instances of problems have been eradicated but the families of problems that persist include computer viruses, buffer overflows, cross site scripting (XSS), SQL injection (SQLi), etc. Computer viruses have existed since 1971, buffer overflows were popularized in 1996, XSS has been around since about 1997, and SQLi has been present since 1998.

Managers and security professionals are often looking for that silver bullet for solving all of the information security issues that an organization may have. Vendors of security products are often willing to demonstrate that their single or integrated security solution will provide all of the protection that an enterprise needs against emerging threats, the next generation of attacks, etc.

As information security is engaged in a Red Queen race or an evolutionary arms race, there should be no expectation that a single or multiple strategies can always ensure the survival of an organization. The security controls that are put in place will act as selection pressures on their adversaries to ensure that only the successful exploitation strategies are passed on to the next generation of attacks. The security controls are going to ensure that attackers and malware authors continue to escalating their exploitation strategies against the implemented security solutions to ensure their survival. This escalatory relationship is akin to the evolutionary arms race between predator and prey.

There are multiple outcomes for predator and prey resulting from an evolutionary arms race (Evolutionary Biology, 3rd Edition, Futuyma);

• The first outcome is that neither side gains the advantage. In this situation, the evolutionary arms race continues with each side escalating their strategies (Richard Dawkins and J. R. Krebs, Arms Races between and within Species, 1979). Within an escalatory arms race, both the predator's weapons and the prey's defenses become more effective than previous generations, but neither has an advantage (G.J. Vermeij, Evolution and Escalation, 1999). More simply stated, as time passes a predator's weapons become more refined, and in response to the evolution of these better weapons a prey species evolves better defenses. The end result is neither side makes any progress, but a modern predator would be able to better exploit an ancestral prey than a predator from that period.
• The second outcome is that as the evolutionary costs for continuing the escalation increase, a set of strategies employed by both sides causes an equilibrium to be established. This equilibrium can form what is referred to as an Evolutionarily Stable System (ESS). In an ESS, a point is reached where the system is stable and resistant to invasion from outside strategies based on the costs associated for each strategy. ESSs are detailed in Evolution and the Theory of Games, by John Maynard Smith, 1982 and in the Selfish Gene by Richard Dawkins.
• The third outcome is that the system suffers from continual or periodic changes as a new strategy is employed and a counter-strategy is evolved and then deployed. This is similar to disease/parasite and host relationships, in which a disease or parasite invades a host. The population takes time to develop resistance or immunity to the invasive disease/parasite. For a period of time the population may be quite successful at repelling the disease/parasite, but eventually the disease/parasite can develop a strategy to overcome the factor that was keeping them out of the host. This is commonly seen as the over use of antibiotics has caused various strains of antibiotic immune diseases to develop; such as Methicillin-resistant Staphylococcus aureus (MRSA) or Extensively Drug-Resistant Tuberculosis (XDR-TB).
• Lastly the outcome of an evolutionary arms race can result in one or both of the species going extinct. One of the sides of the evolutionary arms race evolves an adaptation which allows it to fully exploit or evade exploitation from the other species in a way that it cannot adapt before becoming extinct. Conversely, if the predator was entirely focused on exploiting a single prey species, with the extinction of the prey, the predator species may also collapse.

Ideally, the goal of information security is to seek the last outcome of an evolutionary arms race, in which the opponent becomes extinct. Although this is the goal, currently within the malware and anti-malware Red Queen race, it appears that the reality of the situation is that the race is in the first outcome (continued escalation) or the third outcome (cyclic strategy and counter-strategy development). The race will continue to persist in one of these states for the foreseeable future. The cost of the evolutionary arms race is still asymmetric between defenders and attackers. The methods and strategies employed to evade Anti-Virus scanners with Free/Open Source Software (FOSS) tools such as the Metasploit Framework are still fairly effective, despite the strategies begin implemented prior to March 2008.

In order to cause an extinction of predator strategies (or in the case of information security an attacker's or malware author's strategies), it is not necessary to wipe out an entire population in a single event. Within evolutionary biology, an estimate of effective population size is given by the following equation; Pi = P0 * exp([b-d]*t), where Pi is the population size in the future, P0 is the initial effective population size, t is the time, b is the birth rate, and d is the death rate. As long as the birth rate is higher than the death rate, the population size will grow exponentially. If the death rate is higher than the birth rate, the population is shrinking. The birth and death rates are typically associated with environmental factors such as competition for available resources and types of selection pressures. Essentially the environment only has to change faster than the opponent's strategies can adapt.

By inspecting the rate of growth for malware, it appears that the "birth" rate is higher than the "death" rate. The effective malware population (based on the number of unique samples) is growing exponentially. The costs for malware populations have not reached their carrying capacity on the environment. Within evolutionary biology and ecology, the carrying capacity is the population size that a given environment can support based on the available resources. If a population is increasing in size, then the carrying capacity has not been reached as more resource are available to support the growth. As the population approaches the carrying capacity, the population growth decreases as available resources are more difficult to access. If the population exceeds the carrying capacity, the population will reduce in size as selection works against the population and the entities which are not able to extract enough resources to survive.

Ideally, security professionals would like to see the current situation change from being a continually escalating arms race or a cyclic strategy/counter-strategy to that of the extinction of attacker/malware strategies. By changing the selection pressures that are applied against these invasive strategies, it could be argued that extinction can be triggered. A set of selection pressures could be implemented such that nothing could survive or the selection pressures of the environment change so quickly that the invasive strategy does not have time to evolve successful adaptations. Another solution could involve changing local environmental selection pressures independent of the global selection pressures such that only specific strategies can thrive in specific "regions." This strategy is similar to having an organization switch to a different operating system and/or browser, so the commonly employed exploit strategies fail on the organization.

One of the main problems with implementing a strategy to solve the issue drastically changing the environment is that the environment has to change quickly, more quickly than the invasive strategy can evolve adaptations. The current computing environment is not conducive to drastic changes implemented through out the entire infrastructure. Virtualization is often proposed as a security solution, but to implement this solution globally would take years to decades. Most users are not going to upgrade to a virtualized operating system, unless they are going to acquire a new computer. Typically computers are not replaced or even upgraded annually. This represents a significant period of time in which attackers and malware author's can update their strategies and adapt to the new environment. As previously discussed, attackers and malware have the advantage when the environment changes due to their smaller size.

Another method for improving the situation within the Red Queen race that is occurring within information security, would be the attempt to convert the situation into an ESS. In an ESS, there is an equilibrium reached that is resistant to invasion by outside strategies. If this occurred attackers and malware would achieve a balance with the security professionals in which new infections are cleaned at approximately the same rate as they are occurring.

Instead of focusing on the extinction of malware in the near term, another strategy would be to focus on the infectious nature of malware and reducing the associated virulence. In dealing with the interactions between diseases/parasites and their hosts, the virulence of the disease/host tends to be associated with how it is transmitted between the hosts. A disease or parasite that is transmitted from parent to offspring is said to be vertically transmitted though a population. Diseases and parasites that are vertically transmitted tend to have a lower virulence, or exhibit avirulent behavior. If the disease or parasite reduces the host's fitness too much, then they will not be able to propagate to its offspring after/during reproduction, since no offspring will be produced. Horizontally transmitted diseases/parasites jump from host to host in a population through a variety of different mechanisms; direct contact, the environment or a pathogen vector (such as a mosquito in the case of Malaria). As the virulence of the disease/parasite is not dependent on the survival of the host to reproduce, only the contact with other vulnerable hosts, it is capable of reaching a much higher virulence and significantly reducing the fitness of the host.

There are a number of different ways that an evolutionary arms race can play out; it can continue to escalate, it can continue to escalate until the costs associated with the escalation cause the system to stabilize into an ESS, it can develop in cyclic phases such as the case in the interactions between diseases/parasites and hosts with their immune responses, or one of the interacting entities can go extinct as it is no longer able to adapt to the environment. With the rate that the malware population is increasing, it does not appear that the evolutionary arms race has stabilized into an ESS or that malware will go extinct in the near future, so either the escalatory nature of the race will continue or the cyclic interplay between strategy and counter-strategy will continue for the foreseeable future. The strategies employed by attackers and malware authors rely on small easily adaptable applications, which in terms of evolutionary biology means that the can more readily adapt to environmental selection pressures. Instead of causing malware to go extinct, perhaps a way can be found to tie it to the host, and force it to adopt a more avirulent or beneficial behavior by being vertically transmitted through a computer population instead of horizontally transmitted.

Reducing the Time for Adaptation

Periodically security professionals and security vendors tout the idea that reducing the reaction time between an event and employing a counter strategy can potentially resolve the evolutionary arms races within information security. This idea is similar to an Observe, Orient, Decide and Act (OODA) loop.

In strategy, there is Boyd's OODA loop which emphasizes the idea that reducing the time required for planning and reacting faster than an opponent will provide an advantage and subsequently enhances the likelihood of the opponent making a mistake. By deceasing the time that is required to react appropriately to a situation, the initiative is maintained and consequently an opponent is always responding to the situation. The more time an opponent spends reacting, the less time they have to observe and plan; increasing the likelihood that a mistake will be made. This concept has been raised recently on the panel discussions at the CATCH 2009 conference. References to this particular type of strategy, arise periodically from malware vendors in that if the time between the release of malware and the release of generally available anti-malware signatures can be reduced, it could help to solve or alleviate the malware threat.

Applying the OODA loop or simply reducing the reaction time could potentially go a long way towards helping to alleviate the malware threat. But, it should be considered that malware will always be able to evolve more quickly than an operating system, a web application, a database or even the anti-malware tool as it has the initiative and malware is typically smaller in size and less complex. Looking at this strategy from an evolutionary biology perspective, it is similar to the Red Queen hypothesis that occurs between diseases/parasites and their hosts. It is also similar to the evolutionary arms race between malware and the rest of the information security community (anti-virus,browsers, office automation applications, operating systems, application services, etc). Viruses have genomes on the order of 10^4 base pairs, bacteria have genomes on the order of 2x10^6 base pairs, and humans have genomes on the order of 6.6x10^9 base pairs (Evolution 3rd Edition, Ridley). Modern operating systems have about 40 - 55 million lines of code (equating to 2.5 - 4 GB installed), while most malware is a few orders of magnitude smaller, approximately 119 - 134 KB in the case of Conficker.

As is the case with viruses and other more complex organisms within the real world, smaller organisms are capable of evolving at a much faster rate than large complex organisms. Consider the case of RNA viruses which have a mutation rate of about 1 mutation/generation. While bacteria have about 10^-3 mutations/generation, and humans have about 200 mutations/generation (Evolution 3rd Edition, Ridley and Evolutionary Biology 3rd Edition, Futuyma). Some diseases mutate frequently enough that every replication event experiences the likelihood that the disease will have changed. Although humans have a much higher mutation rate than diseases (such as viruses and bacteria), the generation span of a human is much longer than that of most diseases. The generation lifespan on a human is on the order of 15 - 30 years, while diseases typically have generation lifespans on the order of seconds to minutes. Per unit time diseases (e.g. viruses and bacteria) can evolve much more rapidly, and yet large complex organisms are able to survive as they have strategies which allow them to combat these adaptations. Despite the rate at which diseases are capable of evolving, they do not always win. Influenzavirus has the potential of being fatal but in most cases it is not considered life threatening.

Large complex organisms have multiple methods for allowing them to survive in an environment where diseases can rapidly evolve. Entities with smaller genomes have effectively less space in which to maintain a set of strategies which they can use to exploit their environment, while larger more complex organisms have more space in which they can record their survival strategies. Some bacteria use enzymes to protect against viral infections. Eukaryotes employ even more defenses against infection, while entities like vertebrates have evolved immune systems which are capable of responding to infection by disease. One segment of the Human genome, the Major Histocompatibility Complex (MHC) contains approximately 3.6 million base pairs or 140 genes which control a portion of the human immunological system. As of October 2004, the Immunogenetic Related Information Source (IRIS) database estimates the percentage of the human genome that controls the human immune system is approximately 7%, or 1562 genes. Although the percentage of the human genome related to the human immune system seems small, it is important to consider that a significant portion of the human genome is inactive. It is estimated that 25% of the genome is attributed to diseases which have inserted their genetic code into our genome and are now inactive, while other sections contain pseudogenes which are no inactive version of ancient genes. The percentage of the human active genome which relates to the immune system could be substantially higher than currently theorized. The cost of surviving in an evolutionary arms race can be high, as significant resources are required to defend an organism from infection by diseases and parasites.

Recently researchers, such as Banerjee in An Immune System Inspired Approach to Automated Program Verification, have looked at applying some of the methods that the immune system uses for protecting itself from disease by investigating an Automated Immune System (AIS) which can be implemented in information systems.

Implementing an immune system to handle rapidly evolving threats does not eradicate the threat. Immune systems will act as a selection pressure that will cause only those diseases which are capable of adapting to survive. Some adaptations can include methods for remaining undetected by the immune system, while others can include methods for exploiting the immune system and subverting it for its own use. In essence, these systems represent another vector in which disease can exploit a host. Human Immunodeficiency Virus (HIV) actively exploits the immune system; even at the cost of its own reproductive fitness to remain active in the host to survive when anti-HIV drugs are administered. Similarly with anti-malware products, flaws in these systems have allowed malware to exist and even spread in the form of computer worms. Malicious code routinely attempts to disable anti-virus before downloading and installing malicious components. In order to remain undetected, some malware will re-enable the anti-virus products to prevent the user from noticing anything conspicuous. Anti-virus software is complex enough that it has its own vulnerabilities which may be exploited by malware. In 2006, Symantec Anti-virus had a vulnerability (CVE-2006-2630) which allowed for a privilege escalation that was exploited by the W32.Rinbot.L worm.

Simply reducing the response time will not eradicate the threat. It will provide an advantage but it will not solve the problem. In order to respond to diseases which are able to quickly adapt to host evolutionary responses, large complex organisms have had to evolve complex responses that do not rely on a single strategy to ensure their survival. The cost of ensuring survival in an evolutionary arms race can be high, as numerous strategies need to be available to counter act the threat of disease and parasites.

Risk Management with an Evolutionary Perspective

Evolutionary biology can provide useful insights into the risk management process that is used in information security. The current risk management process as described in NIST Special Publications 800-30, Risk Management Guide for Information Technology Systems and 800-39 Rev 1 (Draft), Managing Risk from Information Systems, could be summarized simply as: 1) identify the risks present in the environment, 2) counter/mitigate the risks that have been identified, and 3) repeat. This cycle is ultimately reactive in nature as flaws are only uncovered when vulnerabilities or new attacks are announced. SP 800-39 Rev 1 (Draft) is more focused on categorizing a system, and applying a set of requirements based on the system's categorization and security control customization based on tailoring. This methodology requires that the system's sensitivity rating has been appropriately determined and the predefined security controls appropriately address the threat environment.

If the assumption is made that information security is indeed a system which operating under the rules of a Red Queen hypothesis and that the security controls that are implemented are acting as selection pressures on our adversaries, the risk management process appears to be lacking as that there is nothing that takes into account how an adversary will respond to the environmental selection pressures (i.e. implemented security controls).

When a risk is identified, there are a number of ways that it can be handled within the current risk management framework. A risk can be corrected, accepted, mitigated, or transferred/insured. Each of these methods for dealing with an identified risk can be treated as one of the three types of selection pressures: directional, disruptive or stabilizing.

• Corrected risks can act as disruptive selection pressures. It is a disruptive selection pressure in the sense that the risk has been removed; an adversary must abandon the strategy that could be used to exploit the system. The adversary will be forced to evolve a new strategy if they are going to continue to exploit the system.
• Accepted risks can act as a stabilizing selection pressure. It is a stabilizing selection pressure in that it encourages an adversary to continue to use the existing exploitation strategy and discourages the use of other strategies in that they will cost resources to evolve and develop (which could be used elsewhere). Some would argue that an entity can also deny that a risk exists in the first place, if so then by default they are accepting the risk and treated it as an accepted risk.
• Mitigated risks can act as either a disruptive or directional selection pressure. If the mitigation causes an adversary to abandon their exploitation strategy it will be a disruptive selection pressure on the adversary. If the mitigation simply causes the adversary to modify their existing strategy it will act like a directional selection pressure.
• Transferred/Insured risks can act as a stabilizing selection pressure. Like accepted risks, transferred/insured risks will not exert a selection pressure on an adversary's strategy which causes them to either modify or abandon their existing strategy. If the risk is transferred or insured, it should be noted that it does not transfer the risk of an incident occurring. Just as when car insurance is purchased, the insurance company does not actually assume the risk of getting into an accident, the driver still carries that and the insurer carries the risk of having to payout out after an incident.

Each type of selection pressure exerts evolutionary costs in response. When multiple methods for dealing with a risk are identified, the evolutionary cost of the adversary to overcome the strategy should be considered in addition to the organization's cost for implementing (or not implementing as the case may be) a strategy. In general, disruptive selection pressures will exert the highest evolutionary cost on an adversary, while stabilizing selection pressures will tend to exert a minimal or non-existent evolutionary cost on an adversary.

• Disruptive selection pressures are the most likely method to extract the highest evolutionary cost from an adversary in that they will force them to not only evolve/develop a new exploitation strategy, but also waste the effort of continuing to maintain a strategy that may not succeed.
• Directional selection pressures will tend to exert minimal evolutionary costs on an adversary, as they must only modify an existing exploitation strategy to continue to be successful. The adversary does not need to abandon their existing strategy or develop a new strategy, just refine an existing one. There is an evolutionary cost associated with this but it will be less than if they had to abandon their current strategy and evolve a new one.
• Stabilizing selection pressures will tend to cost an adversary the least, as they do not need to modify their current strategies therefore experiencing no change in to their evolutionary costs. There may be evolutionary costs associated with stabilizing selection pressures as the maintenance of an adversary's strategies may have a cost associated with them. Stabilizing selection pressures are not likely to force an adversary to incur any additional evolutionary costs as they have already adapted to the environment, but even then an adversary may be able to reduce their costs further by evolving a more efficient method for existing in the environment.
  

Using the principles of selection pressures and evolutionary costs from evolutionary biology, the risk management process can be updated to anticipate how an adversary will respond to the survival strategies of a system. When responses to a risk are proposed, they should be investigated to see how an adversary could respond. In the case of SSH brute forcing, the rules act as a directional selection pressure which caused the attacker to modify but not abandon their strategy. With the implementation of virtualization throughout an environment, it can act as either a disruptive or stabilizing selection pressure on malware. Depending on the potential costs associated with an adversary's response, the one that is likely to inflict the highest evolutionary cost should be chosen as the solution. If a solution is chosen has little or no impact evolutionary cost on an adversary to over come, it will not be long before an attacker has compromised the system.

Predicting the resultant strategies is not trivial, but understanding the selection pressures involved may make the situation more manageable. In the case of the SSH brute force and the adoption of Virtualization some strategies can be determined based on the attributes of the strategy implemented. Inspecting the strategies that are found within the natural environment could provide additional insight into how an adversary could respond. Any of the following could also be potential responses.

• Some organisms have developed adaptations which advertise to others that they are something they are not, or they are poisonous. An adversary could mimic the behavior of the system employed. Malware such as Anti-Virus 2009 or Anti-Virus 360 appears to be anti-virus software which protects a user from attacks on the Internet when instead it is actually a Trojan.
  
• Like parasites subverting the central nervous systems of hosts, an adversary could exploit the strategy that is used to help the system survive. Malware can attempt to exploit vulnerabilities in anti-virus software to attack a system, as anti-malware software usually operates as a privileged service making it a priority target since it has access to the entire system in addition to protecting the system.
  
• Some animals have developed better camouflage to help mask there presence in the environment. A smaller and less noisy profile means that an attacker is less likely to detect their presence. Malware is moving to HTTP command and control channels to help mask its presence in the traffic being sent across the network.
• Another response is to completely abandon the current strategy, and develop a new strategy which catches an organism unprepared. As part of an experiment in evolutionary biology, a predatory lizard was introduced onto several islands which were inhabited by Anoles. Initially the average length of their legs increased, which allowed them to survive by running faster to evade their predators. Eventually the average leg length decreased as the Anoles were able to avoid their predators entirely by spending more time in the trees. Malware can react to countermeasures by simply avoiding the countermeasures entirely or attacking an information system at different layers. A worm can be written to exploit web applications instead of targeting flaws in the operating system.
  
• Sometimes the best response is not to respond to the strategy employed. If the counter strategy will only be infrequently encountered, it is often more cost effective to ignore it. In the case of the natural environment some predators that interact with prey populations interact so infrequently that it is more effective to not response as a population then to evolve a response. Malware authors should be aware that almost all analysis of their binaries will be conducted in a virtualized environment, yet not all malware encountered is able to detect when it is operating in a virtualized environment.
  

Each of different responses carries an associated evolutionary cost. Some of these like abandoning a strategy and evolving a new strategy can be high as the cost of evolve and develop a strategy are discarded and a new strategy must be evolved and developed. Other strategies can carry no additional evolutionary costs such as ignoring the threat and not modifying the current survival strategies.

The current risk management process has weaknesses when it is applied to an environment which evolving. The basic process is reactionary in nature and gives all of the initiative to the adversary and requires that the adversary first advertise their latest strategy before it could be countered. Instead of waiting for an adversary to attack an information system, the risk management methodology should include steps which attempt to determine how the current security strategies will force an adversary to adapt. Based the types of selection pressures that are applied to counter an adversary's strategy, anticipated actions can be made as to how an adversary will be forced to respond. When selecting among several different counter strategies, preference should be given to those strategies which have the highest evolutionary costs to counter (e.g. most likely disruptive selection pressures).

Monoculture/Heterogeneous Computing and Resource Exploitation

Using the same baseline, configuration, or technologies throughout an industry can reduce costs through ease of maintenance and deployment. It can foster information sharing as all parties involved communicate using the same formats and standards. Within cryptography, using standards and certified products allows others to gain a level of assurance regarding the trustworthiness of an algorithm or product. Reliance on the same computing platform/practices is referred to as monoculture. Despite the benefits of operating within a monoculture, there are a significant number of risks associated with it.

Looking at monocultures in information security from an evolutionary biology perspective, there are significant risks. In terms of evolutionary biology, a monoculture represents a large population which is composed of the same characters and utilizes the same strategies for survival. This represents either a lack of genetic diversity or genetic variability within the population. Genetic diversity represents the number of characters that are present within a population, while genetic variability represents the individual tendency of individual characters to vary from one another. Variation of characters within a population is one of the four conditions that is required for natural selection to operate (Evolution, 3rd Edition by Ridley and Evolutionary Biology, 3rd Edition by Futuyma). Without any variability in a population, it follows that when selection operates on a population it will either select against the entire population or select for the entire population. There is no intermediate state without variability. This does not mean that every selection event will cause the population to go extinct, but the potential for an event exists.

An entire population that is dependent on the same survival strategy is vulnerable to exploitation. If an entity is capable of finding a way to exploit the strategy used, then it has found a method which is capable of exploiting the entire population. If the population of hosts can either be easily accessed by the attacker or the hosts are in frequent contact with on another, the attacker leverages the exploit effectively such that it can spread rapidly through the entire population before a counter measure can be developed. As this entire population is employing the strategies, it can take a significant period of time before the entire population is inoculated against the exploit.

Currently the 'Cavendish' banana population is at risk from the fungus Fusarium oxysporum (i.e. Panama Disease or Agent Green) due the monoculture environment in which it is cultivated. Panama Disease already caused the collapse of the previous 'Gros Michel' crop in the 1960s. Originally the Cavendish banana population was resistant to the Panama Disease, but in 1993 a new strain (referred to as Tropical Race 4) emerged and has since contributed to the collapse of the Cavendish population of bananas in Southeast Asia. This is not the only case of a monoculture impacting a food crop. Previous to the banana monoculture, there was a potato monoculture in Ireland. In the early 1800s, Ireland was dependent on the potato crop to feed their population. Potatoes were essential clones of one another, and eventually the mold Phytophthora infestans exploited and destroyed a majority of the 1845 potato crop and one and a half million of people died from starvation.

Currently there exists a monoculture environment within computing associated with Microsoft Windows operating system; the dominant operating system in the market. A majority of the attacks on the Internet have focused on this operating system, as there is an abundant population which can be exploited.

The alternative to monoculture in information technology is a heterogeneous computing environment where there are different operating systems and applications are in use. The result is a diversified environment in which a single strategy is incapable of compromising the entire environment by exploiting the operating system or applications. Monocultures within the information technology are not just limited to the operating system. The heterogeneous computing environment associated with cell phones and mobile devices is seen as providing protection from malicious software despite their being 3x the number of mobile Internet-capable devices connected to the Internet as compared to computers.

The risks associated with a monoculture are present at all levels of computing where the same resources and standards are used. Monocultures can exist at other levels such as network architectures, office automation applications, email services/clients, web browsers, application/web servers, web application frameworks, and databases. In addition to the possible application level monocultures, hardware and standard/protocol level monocultures exists. Common protocol monocultures found in networking and the Internet include: IP, TCP, HTTP and DNS.

At BlackHat USA 2008, a DNS flaw which had been discovered earlier in the year was released to the general community. This flaw took advantage of the DNS standard and since most implementations followed all of the recommendations in the standard, they were vulnerable to exploitation from this flaw.

Although web applications can differ in their implementation, their reliance on the same back-end database technology (and a lack of input validation) allowed a large number of sites to be compromised by a SQL injection worm. The worm targeted websites which used Microsoft SQL Server as their database.

Monocultures pose a risk to information systems when they exist at any level. A system may have different web browsers deployed in its environment, but if the browsers are all running on the same operating system, exploits can target the operating system and bypass the heterogeneous browser level. As far back as 2004, there have been vulnerabilities announced which can successfully attack the underlying operating system even if different web browsers are interpreting the data.

Applying evolutionary biology to information security with respect to monocultures, it can be seen that relying on an environment of monoculture can be dangerous. Monoculture environments have little genetic variability which allows them to survive selection events, and they are vulnerable to invasion from diseases which can devastate the entire population. The implementation of a heterogeneous computing environment allows an information system more resistance and increases the likelihood of surviving an attack as an attack is not capable of exploiting an architectural or implementation flaw present entire population.

Disruptive/Stabilizing Selection Pressures and Virtualization

In evolutionary biology disruptive selection pressures are commonly seen when there is a radical change in the environment in which an entity is attempting to survive in. The more drastic the environmental change, the stronger the selection pressure that will be applied to the population. Sometimes the changes will be drastic enough that the population goes extinct, while in other cases the population will be able to evolve and adapt to the new environment. In information security, an emerging potentially drastic change is the application of virtualization through out the computing environment.

There have been a number of suggestions and even implemented systems which use Virtualization as a security measure. Some systems even treat it as the ultimate solution to malware propagation on the Internet. Aside from the increased overall complexity of the resulting system and requirements for management, using virtualization as a security measure will be a game changing event, but not one which solves the malware issue. Looking at the implementation of virtualization as a security mechanism from an evolutionary biology point of view, this virtualization strategy will act as both a disruptive and stabilizing selection pressure in the co-evolutionary system of information security.

Disruptive selection pressures cause an entity to abandon their current strategy and pursue a different strategy. These pressures select against those who employ a specific strategy. In the case of stabilizing selection, pressures act on an entity to reinforce their current strategy and selects against employing other strategies. There are two ways in which malware can respond to the wide spread adoption of virtualization. It can either abandon the items being virtualized or it can exploit virtualization to its advantage.

• In the first case, malware abandons operating in the virtualized layers of the operating system and applications. Virtualization acts as a disruptive selection pressure in which malware evolves to exploit the layers above and below the virtualized layers.
• In the second case, malware evolves to exploit the new virtualized environment. Virtualization has made new exploitable resources available and will act as a stabilizing selection pressure as malware beings to evolve strategies which exploits virtualization.

In the case of disruptive selection, malware's response will move out of the virtualized layers of the information system (e.g. the operating system and possibly the application environment) and into the layers either above or below the virtualization. The layers above the virtualization would be considered to operate within a browser environment. Virtualization can even be applied to specific applications such that if one is exploited, it will not affect the host operating system. Despite this fact, virtualization will not protect the system against attacks such as Cross Site Scripting (XSS), Cross Site Request Forgery (CRSF), Phishing, Sidejacking, and SQL Injection. It will not protect against attacks which exploit the user through social engineering and still allows malicious scripts to ex-filtrate private and/or sensitive information from the system.

Also, this disruptive selection pressure can cause malware to move down through layers towards the BIOS, firmware, and hardware of an information system. Generally virtualization will be able to protect an information system as data is being processed or once it has already been processed. If an attack ignores these layers, it can exploit the system without being detected. Fundamentally, virtualization trusts the hardware in which is it operating and this trust relationship can be exploited. There are a large number of places in which malware can hide on a system besides at the application and operating systems layers such as in BIOS, Firmware (e.g. a NIC) or even within the processor.

Evolutionary biologists have previously conducted experiments which focused on evolutionary adaptation of bacteria which demonstrated that given a resource limited environment bacteria can evolve by selection to fully exploit environmental changes. A population of E. coli was placed under controlled environmental conditions which allowed the organism to survive and maintain population levels. The bacteria essentially had a disruptive selection pressure applied to its main method of harvesting resources from the environment. The new environment contained resources which if a few changes were made to the metabolic process of the E. coli organism, it would all it to utilize the new resources which it would otherwise not be able to use. The bacteria's progress was measured throughout the experiment, and eventually the right mutations occurred and the bacteria's population grew exponentially as it was able to harvest additional resources in the environment.

Virtualization can act as a stabilizing selection pressure on the evolution of malware. Instead of causing malware to move to other layers of the system, virtualization offers new resources which malware may be able to exploit. Presently there is a significant number of malware that are capable of detecting virtualization but this detection exists to only prevent it from executing as most malware analysis workstations inspect malware inside a virtualized environment. Escapes from a virtualized environment have already been demonstrated, as have VM exploits. If virtualization becomes common through out the environment, malware will be able to evolve its strategies such that it can survive in this environment.

The widespread adoption of virtualization as an information security counter-strategy will in some cases provide no selection pressure on an attacker's strategy. Virtualization will also not address a number of exploitation strategies which exploit the interconnections between systems. It will not be able to provide a defense against man-in-the-middle attacks or attacks which focus on the protocols which are used to connect information systems together.

Lastly, it will take time to make a virtualized solution common in the environment. In the short term, the virtualized clients will have an advantage in that they occupy a small portion of the entire population, but as time passes the likelihood that malware can exploit this new virtualized strategy will increase. Like with the example of E. coli adapting to an environment which initially severely limits its fitness, eventually malware will be evolve to exploit its new environment. Rolling out virtualization to the entire population of computers will not be done over-night and it will take a few years. Unlike the E. coli which was suddenly exposed to an environment which hampered its fitness, malware will be more gradually exposed to virtualized environments. Despite the time difference in the exposure to the emergence of a selection pressure, just like the E. coli malware will be forced to change by its environment, allowing it to evolve the necessary adaptations which will allow it to survive. It is not a question of can it evolve, but rather how long it will take to evolve.

Simply using virtualization as a defense does not mean that a system is instantly protected against all existing malware strategies. It will stop some exploitation strategies but it is not a complete defense and can even increase the risk to the environment as virtualization adds software which must be secured in addition to the increased complexity to the system in its operation and management.

There are a number of directions in which using Virtualization as a common defense could force malware strategies to evolve. Malware could evolve under stabilizing selection pressures which would cause it to evolve strategies for escaping and exploiting the very software which is used to protect the system. Malware could also evolve under disruptive selection pressures and evolve strategies to target the hardware which has traditionally been assumed to be trusted. Attacks against Firmware, BIOS, CPU, NICs, and even the Trusted Platform Modules have been successfully demonstrated. Although virtualization is not the only selection pressure in causing the creation of hardware attacks, it will increase the selection pressure and force these attack strategies to move in that direction. Already there have been discussions and demonstrations about implementing System Management Mode (SMM) rootkits by poisoning the system's cache. Beyond that, using virtualization as an information security measure will not protect a system from scripted attacks, social engineering or man-in-the-middle attacks.

Directional Selection Pressures in SSH Brute Forcing

A practical application of evolutionary biology in information security is found looking specifically at the evolution of a common Internet attack. Selection pressures were previously examined here at a high level, but SSH brute force attacks provide a more practical example of directional selection pressures. Directional selection pressures act to move a character or strategy in a specific direction.

SSH attacks are simply result of taking a list of accounts with common passwords and trying all of the username/password combinations to see if any of them allow access into a system. Early attempts simply tried to supply all of the combinations as fast as possible to determine if there was a valid combination present. Applications such as denyhosts, fail2ban, and sshguard exist to detect brute force attempts and ban those IP addresses from trying to access the server.

At a high level the counter strategy employed to prevent successful brute force attempts on a system implements a rule similar to the following: If a number of unsuccessful login attempts are detected within a short period of time; block all connection attempts from that address for an extended period. This rule acts as a directional selection pressure in that it forces attackers that are using the brute force strategy in a specific direction by controlling the login attempt frequency and number of source IP addresses.

Beginning in May of 2008 through December 2008 and into January of this year, there were reports of a newer Slow/Low-key Brute force attempts from various BotNets. With these newer attacks, the attack strategies were modified such that they are occurring at a much slower rate and occurring from various source addresses. In deed, upon inspection of the rules that were implemented to counter the attack, as they were acting as a directional selection pressures, it should have been expected to see a response in the attack strategies as they evolved in reaction to the selection pressures.

The SSH brute force detection rules have two principle components which act on the attack in as a selection pressure in a directional manner; the number of failed attempts per period and the source IP addresses. Only attacks which slowed their rate (in response to the failed attempts per period directional selection pressure) and distributed their attacks (in response to the source IP address selection pressure) could be expected to have a reasonable chance of being able to get through their account/password dictionaries.

It is possible that an attacker could have modified their strategy in only one direction to continue their attacks. If the attacker simply distributed their attack and failed to throttle the login attempts, all of the hosts which were participating in the attack would have been banned fairly quickly. If the attacker just used a single host and throttled their attack, it would take a substantial amount of time to iterate through the account/password dictionary.

If the attack strategy is inspected further, to find that the account list that is attempted is in alphabetical order and is synchronized across the BotNet. By making use of these additional characters, the strategy employed to block these attacks could continue to evolve.

A counter-strategy could be employed to include tracking the addresses that are using brute forcing by seeing if they are supplying accounts alphabetically. This counter-strategy has the weakness in that the attacker would only need to modify the order in which the accounts are tracked to a random sequence. This would get around the alphabet test but at the cost of additional resources to track the combination of usernames and passwords which have been attempted. Without tracking the attempted combinations, the BotNet would eventually starting using previously supplied combinations which are known to have failed and count as wasted attempts (and resources). Or the attacker could simply increase the number of bots that were participating in the attack such that only one bot supplies an account/password combination. This would require a large number of bots to participate in the attack, and also have the cost of requiring additional coordination through out the BotNet. By increasing the number of bots participating in the attack, it also exposes the attacker to additional risk in that it would allow a researcher to learn the identity of more of the bots in their network.

By devising a counter strategy which targets the synchronization of the accounts across the BotNet, a new strategy could be used as a basis for augmenting the firewall rule set by keeping a list of accounts that were attempted recently. If another address attempts to use that account, it would automatically drop the connection and block further connection attempts from that address.

Another counter-strategy could be implemented which borrows from Conficker/Downadup's attack strategy. Conficker scans for infect-able hosts on the same network, as they are typically all configured in a similar way (in the enterprise there are GPO policies which are frequently pushed out and for the home user they are almost always left in the default configuration). Making use of this information, instead of blacklisting just the host which is attempting to brute force the system, the attacker's network could instead be blacklisted.

The server could simply nullify the ability of the attacker's brute force attempts by requiring a form of multifactor authentication.

To the researcher who conducted further analysis of the attack, it appeared that the Slow/Low-key SSH brute force attempts began to modify their strategy further to avoid the OpenBSD machines that they were monitoring.

SSH brute forcing provides an easy way to compromise a host, as no exploit is needed and a host running SSH is designed to be remotely administered. Since the strategy employed to detect SSH brute force attempts acted as a directional selection pressure, the attacker was able to modify their strategy to avoid detection for an unknown period until the total number of failed login attempts rose to the level in which administrators and researchers noticed. Eventually the attacker further modified their strategy to avoid the OpenBSD machines that were being used all together.

Evolutionary Costs and the Life/Dinner Principle

As illustrated previously, the time it takes to evolve strategies and/or the ability to exploit existing environments (or a population) is important. An additional factor that should be considered when examining exploits are the associated costs. Within evolution these items are referred to as the evolutionary costs.

There are costs associated with utilizing a strategy, evolving a new strategy, and neglecting the use of an existing strategy.

• In utilizing a strategy, an entity must pay the costs of maintaining that strategy. It should be recognized that in employing or retaining the capability of a strategy consumes resources that could have been spent elsewhere.
• Evolving a new strategy also consumes resources, and those resources have to be taken from another source. They are going to come from resources that could have been spent to refine another strategy, develop a different strategy or continuing the usage of a existing strategy (i.e. allowing a current strategy to atrophy).
• Lastly neglecting the use of an existing strategy could have the cost of preventing an organism from surviving from the fact the organism may have misspent resources. Not using an existing strategy could adversely affect an entity in that the resources consumed during development a new strategy could have been used elsewhere to form a necessary new strategy (and are considered to have been wasted in this effort).

Evolution and development are two different concepts within evolutionary biology. In a simplistic form, evolving refers to the process of creating a strategy through natural selection. Development is the process of creating a strategy for an individual entity. To more clearly illustrate the difference; birds as a class have evolved wings but while they are individuals in the egg as embryos they develop wings.

When dealing with the costs of employing strategies for survival, it should be noted that the costs for all entities involved are not shared equally. This potential asymmetry is summed up in the life/dinner evolutionary principle (as popularized in both the Selfish Gene and the Extended Phenotype written by Richard Dawkins, but originated by M. Slatkin in Models of Coevolution). Slatkin uses the rabbit and fox from one of Aesop's fables to illustrate the basic idea of the asymmetrical costs in association with life/dinner principle.

Consider the case when a rabbit is being chased by the fox. The rabbit is running for its life, while the fox is only running for its dinner. The cost of failing is different for those involved. For the rabbit, if it fails it looses its life, while for the fox; if it fails it only looses its dinner. So the rabbit is going to be willing to spend more to ensure its survival in a given race, because if it is unsuccessful there will not be another generation of rabbits produced (at least from this rabbit's germ line). The fox can afford to lose this specific race; as if it fails it will have an opportunity to pursue another rabbit in the future.

It could be argued that if after several of these races and the fox remains unable to catch a rabbit, then it could very well be facing its final race too. This is true, but if you compare the costs associated with a single race, the rabbit is still going to face the more severe cost of failure.

Within this co-evolutionary race, the rabbit/fox race can pursue a number of different strategies to ensure their survival. The simplest way to continue the race would be that the fox can attempt to run faster as well as the rabbit can attempt to run faster. It is important to consider that this is not the only strategy that the rabbit can pursue; it could also develop better camouflage, better sensory systems to learn of the foxes presence before he comes too close, or even become more maneuverable so that if the fox does pursue him he can out maneuver the fox and escape, or the rabbit can just produce so many rabbits that in general the likelihood of a single individual becoming dinner is small. In general which ever method becomes more prominent in the rabbit population, the fox will have to escalate his attacks to deal with these new strategies.

Although the co-evolution is occurring in the rabbit/fox competition, there are costs and trade offs associated with each of these potential advancements. Obviously as we do not see rabbits that can run arbitrarily fast (out side of cartoons and comics). In order to evolve a strategy, there are costs associated with this development. The development takes resources that could have been devoted to creating or even just maintaining something. In the security field there are trade offs which must be considered, and the penalties for not maintaining the proper balance of strategies can be just as severe for an information system as it is for a rabbit.

The penalty asymmetry is commonly seen in the development of an information system. When a system is designed, it has to address all of the threats that will be present in its environment, but an attacker only needs to find one successful strategy to compromise the system. An attacker also has additional advantages;

• They do not have the expectation that they are not going to compromise every system they encounter. If they were unsuccessful in exploiting the initial target, they can move on to another system. It is built into their strategy, that they will not compromise every system they encounter only just enough to find dinner.
  
• They have time to attempt multiple strategies against the system and continue using different combinations of strategies until they find one that works.
• They do not have to play by the rules. Even more than that they have no expectation that they are going to stay within the design requirements of the system.

Unlike the general case in evolutionary biology in which if any animal's strategy fails, it pays for the costs of that failure directly. While within information security those who fail do not necessarily pay the costs for the failure. For example, spear phishing (e.g. targeted phishing) and whaling targets an individual within an organization to gain access to its resources and information. When an individual opens an email that contains a targeted attack, although they are the cause of the failure, it is the organization which pays the cost of the failure. Another cost to consider in information security is who pays the cost of failure.

Although there are response lags to develop or deploy new strategies and it takes time to exploit other resources. There are costs for developing, maintaining and even using evolutionary strategies. These evolutionary costs are also not necessarily paid evenly by all involved in the red queen race.