Artificial Intelligence Methods invirus Detection & Recognition
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1 Artificial Intelligence Methods in Virus Detection & Recognition Introduction to heuristic scanning Wojciech Podgórski October 16, 2008
2 Presentation outline 1 Introduction Fundamentals of malware Metaheuristics in Virus Detection & Recognition 2 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement 3 Panda s Technology Evolution Genetic Heuristic Engine - Nereus 4 5
3 Fundamentals of malware Metaheuristics in Virus Detection & Recognition Malware Malware (malicious software) is software designed to infiltrate or damage a computer system without the owner s informed consent. Source:
4 Malware types Fundamentals of malware Metaheuristics in Virus Detection & Recognition We can distinguish quite few malicious software types. It is important to be aware that nevertheless all of them have similar purpose, each one behave differently. Viruses Worms Wabbits Trojan horses Exploits/Backdoors Spyware/Scumware/Stealware/Parasiteware/Adware Rootkits Keyloggers/Dialers Hoaxes
5 Malware Viruses Fundamentals of malware Metaheuristics in Virus Detection & Recognition Due to different behaviour, each malware group uses alternative ways of being undetected. This forces anti-virus software producers to develop numerous solutions and countermeasures for computer protection. This presentation focuses on methods used especially for virus detection, not necessarily effective against other types of malicious software.
6 Infection strategies Fundamentals of malware Metaheuristics in Virus Detection & Recognition To better understand how viruses are detected and recognized, it is essential to divide them by their infection ways. Nonresident viruses The simplest form of viruses which don t stay in memory, but infect founded executable file and search for another to replicate. Resident viruses More complex and efficient type of viruses which stay in memory and hide their presence from other processes. Kind of TSR apps. Fast infectors Type which is designed to infect as many files as possible. Slow infectors Using stealth and encryption techniques to stay undetected outlast.
7 Infection strategies Fundamentals of malware Metaheuristics in Virus Detection & Recognition To better understand how viruses are detected and recognized, it is essential to divide them by their infection ways. Nonresident viruses The simplest form of viruses which don t stay in memory, but infect founded executable file and search for another to replicate. Resident viruses More complex and efficient type of viruses which stay in memory and hide their presence from other processes. Kind of TSR apps. Fast infectors Type which is designed to infect as many files as possible. Slow infectors Using stealth and encryption techniques to stay undetected outlast.
8 Infection strategies Fundamentals of malware Metaheuristics in Virus Detection & Recognition To better understand how viruses are detected and recognized, it is essential to divide them by their infection ways. Nonresident viruses The simplest form of viruses which don t stay in memory, but infect founded executable file and search for another to replicate. Resident viruses More complex and efficient type of viruses which stay in memory and hide their presence from other processes. Kind of TSR apps. Fast infectors Type which is designed to infect as many files as possible. Slow infectors Using stealth and encryption techniques to stay undetected outlast.
9 Infection strategies Fundamentals of malware Metaheuristics in Virus Detection & Recognition To better understand how viruses are detected and recognized, it is essential to divide them by their infection ways. Nonresident viruses The simplest form of viruses which don t stay in memory, but infect founded executable file and search for another to replicate. Resident viruses More complex and efficient type of viruses which stay in memory and hide their presence from other processes. Kind of TSR apps. Fast infectors Type which is designed to infect as many files as possible. Slow infectors Using stealth and encryption techniques to stay undetected outlast.
10 Fundamentals of malware Metaheuristics in Virus Detection & Recognition Metaheuristic Metaheuristic is a heuristic method for solving a very general class of computational problems by combining user-given black-box procedures in a hopefully efficient way. Metaheuristics are generally applied to problems for which there is no satisfactory problem-specific algorithm or heuristic. Source: Heuristic Heuristic is a method to help solve a problem, commonly an informal method. It is particularly used to rapidly come to a solution that is reasonably close to the best possible answer, or optimal solution... Source:
11 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
12 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
13 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
14 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
15 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
16 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
17 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
18 General metaheuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition It is important to remember that metaheuristics are only ideas to solve a problem not a specific way to do that. List below shows main metaheuristics used for virus detection and recognition: Pattern matching Automatic learning Environment emulation Neural networks Data mining Bayes networks Hidden Markov models and other...
19 Concrete heuristics Fundamentals of malware Metaheuristics in Virus Detection & Recognition Specific heuristics practically used in virus detection and recognition, are naturally inherited from metaheuristics. And so, for example concrete method for virus detection using neural networks can be implementation of SOM (Self Organizing Map). Neural Networks (metaheuristic) SOM (heuristic) The most popular, and one of most efficient heuristic used by anti-virus software is technique called Heuristic Scanning.
20 Lacks in specific detection I Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Great deal of modern viruses are only slightly changed versions of few conceptions developed years ago. Specific detection methods like signature scanning became very efficient ways of detecting known threats. Finding specific signature in code allows scanner to recognize every virus which signature has been stored in built-in database. BB?2 B ? ?2 83 C3 04 E2 F2 FireFly virus signature(hexadecimal)
21 Lacks in specific detection II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Problem occurs when virus source is changed by a programmer or mutation engine. Signature is being malformed due to even minor changes. Virus may behave in an exactly same way but is undetectable due to new, unique signature. BB?2 B ?2 81 A1 D3?2 01 C3 04 E2 F2 Malformed signature(hexadecimal)
22 Heuristic scanning conception I Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Q: How to recognize a virus without any knowledge about its internal structure?
23 Heuristic scanning conception I Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Q: How to recognize a virus without any knowledge about its internal structure? A: By examining its behaviour and characteristics.
24 Heuristic scanning conception II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Heuristic scanning in its basic form is implementation of three metaheuristics: 1 Pattern matching 2 Automatic learning 3 Environment emulation Of course modern solutions provide more functionalities but principle stays the same.
25 Heuristic scanning conception II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Heuristic scanning in its basic form is implementation of three metaheuristics: 1 Pattern matching 2 Automatic learning 3 Environment emulation Of course modern solutions provide more functionalities but principle stays the same.
26 Heuristic scanning conception II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Heuristic scanning in its basic form is implementation of three metaheuristics: 1 Pattern matching 2 Automatic learning 3 Environment emulation Of course modern solutions provide more functionalities but principle stays the same.
27 Heuristic scanning conception II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Heuristic scanning in its basic form is implementation of three metaheuristics: 1 Pattern matching 2 Automatic learning 3 Environment emulation Of course modern solutions provide more functionalities but principle stays the same.
28 Heuristic scanning conception III Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement The basic idea of heuristic scanning is to examine assembly language instruction sequences(step-by-step) and qualify them by their potential harmfulness. If there are sequences behaving suspiciously, program can be qualified as a virus. The phenomenon of this method is that it actually detects threats that aren t yet known!
29 Recognizing potential threat I Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement In real anti-virus software, heuristic scanning is implemented to recognize threats by following built-in rules, e.g. if program tries to format hard drive its behaviour is highly suspicious but it can be only simple disk utility. Singular suspicion is never a reason to trigger the alarm. But if the same program also tries to stay resident and contains routine to search for executables, it is highly probable that it s a real virus. AV software very often classifies sequences by their behaviour granting them a flag. Every flag has its weight, if total values for one program exceeds a predefined threshold, scanner regards it as virus.
30 Heuristic Scanning as artificial neuron Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Figure: Single-layer classifier with threshold From [1]
31 Recognizing potential threat II Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Figure: TbScan 6.02 heuristic flags From [3] For instance Jerusalem/PLO virus would raise FRLMUZ flags.
32 Malware evolves Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement After presenting specific scanning to AV software, malware authors were obligated to introduce new techniques of being undetected. Beside of polymorphism and mutation engines viruses started to use various stealth techniques which basically hooked interrupts and took control over them. This allowed them to be invisible for traditional scanner. Moreover, most of them started using real-time encryption which made them look like totally harmless program. Figure: Virus evolution chain [Source
33 Pattern matching is not enough Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Mixing stealth techniques with encryption and anti-heuristic sequences (code obfuscated by meaningless instructions) allowed viruses to be unseen even by signature and heuristic scanning combined together. It was obvious that new solution was needed. The idea came from VM conceptions. Why not to create artificial runtime environment to let the virus do its job? Such approach found implementation in environment emulation engines, which became standard AV software weapon.
34 Virtual reality Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement The idea of environment emulation is simple. Anti-virus program provides a virtual machine with independent operating system and allows virus to perform its routines. Behaviour and characteristics are being continuously examined, while virus is not aware that is working on a fake system. This leads to decryption routines and revealment of its true nature. Also stealth techniques are useless because whole VM is monitored by AV software.
35 False positives & automatic learning Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement HS as a heuristic method is only reasonably close to the best possible answer. In this case we can imagine that heuristic scanning will blame innocent programs for being potential threats. Such behaviour is called false positive We must be aware that program is right when rising alarm, because scanned app posses suspicious sequences, we can t blame scanner for failure. So what can be done to avoid false positives?
36 False positives & automatic learning Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement HS as a heuristic method is only reasonably close to the best possible answer. In this case we can imagine that heuristic scanning will blame innocent programs for being potential threats. Such behaviour is called false positive We must be aware that program is right when rising alarm, because scanned app posses suspicious sequences, we can t blame scanner for failure. So what can be done to avoid false positives? A: Automatic learning!
37 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy I Some applications, especially non-commercial ones, can raise false positive, because of their suspicious routines. e.g. UnHash v1.0 through its encryption functionalities (used for finding hash collisions) almost every time is qualified as virus. What we can do to prevent it, is to: 1 Let Monitor learn Teach AV monitor to recognize programs causing false positives. (requires advanced user)
38 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy I Some applications, especially non-commercial ones, can raise false positive, because of their suspicious routines. e.g. UnHash v1.0 through its encryption functionalities (used for finding hash collisions) almost every time is qualified as virus. What we can do to prevent it, is to: 1 Let Monitor learn Teach AV monitor to recognize programs causing false positives. (requires advanced user)
39 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy II 2 Set proper scanning depth Configure Monitor with suitable heuristic scanning depth by manipulating threshold computed from flag weights. 3 Assume that machine is not infected Some AV software can scan through computer knowing it s clean and learn which programs are false positives. 4 Combine scanning techniques Combine multiple scanning techniques to exclude potential false positives. 5 Perform scan as often as possible Knowing what s going on is essential...
40 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy II 2 Set proper scanning depth Configure Monitor with suitable heuristic scanning depth by manipulating threshold computed from flag weights. 3 Assume that machine is not infected Some AV software can scan through computer knowing it s clean and learn which programs are false positives. 4 Combine scanning techniques Combine multiple scanning techniques to exclude potential false positives. 5 Perform scan as often as possible Knowing what s going on is essential...
41 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy II 2 Set proper scanning depth Configure Monitor with suitable heuristic scanning depth by manipulating threshold computed from flag weights. 3 Assume that machine is not infected Some AV software can scan through computer knowing it s clean and learn which programs are false positives. 4 Combine scanning techniques Combine multiple scanning techniques to exclude potential false positives. 5 Perform scan as often as possible Knowing what s going on is essential...
42 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy II 2 Set proper scanning depth Configure Monitor with suitable heuristic scanning depth by manipulating threshold computed from flag weights. 3 Assume that machine is not infected Some AV software can scan through computer knowing it s clean and learn which programs are false positives. 4 Combine scanning techniques Combine multiple scanning techniques to exclude potential false positives. 5 Perform scan as often as possible Knowing what s going on is essential...
43 Lacks in specific detection Heuristic scanning conception Recognizing potential threat Coping with anti-heuristic mechanisms Towards accuracy improvement Avoiding false positives & improving accuracy II 2 Set proper scanning depth Configure Monitor with suitable heuristic scanning depth by manipulating threshold computed from flag weights. 3 Assume that machine is not infected Some AV software can scan through computer knowing it s clean and learn which programs are false positives. 4 Combine scanning techniques Combine multiple scanning techniques to exclude potential false positives. 5 Perform scan as often as possible Knowing what s going on is essential...
44 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Presenting Panda Security solutions
45 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Modern Panda Security solutions belong to the third generation AV software. First Generation: Antivirus From 1990 s, signature scanning including polymorphic virus recognition. Primitive heuristics. Second Generation: Antimalware From 2000, integrated firewall, anti-malware engine. Third Generation: Proactive Technologies From 2004, TruPrevent R, genetic and rootkit heuristics, behavioral analysis and blocking, uncloaking techniques, generic unpacking
46 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Modern Panda Security solutions belong to the third generation AV software. First Generation: Antivirus From 1990 s, signature scanning including polymorphic virus recognition. Primitive heuristics. Second Generation: Antimalware From 2000, integrated firewall, anti-malware engine. Third Generation: Proactive Technologies From 2004, TruPrevent R, genetic and rootkit heuristics, behavioral analysis and blocking, uncloaking techniques, generic unpacking
47 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Modern Panda Security solutions belong to the third generation AV software. First Generation: Antivirus From 1990 s, signature scanning including polymorphic virus recognition. Primitive heuristics. Second Generation: Antimalware From 2000, integrated firewall, anti-malware engine. Third Generation: Proactive Technologies From 2004, TruPrevent R, genetic and rootkit heuristics, behavioral analysis and blocking, uncloaking techniques, generic unpacking
48 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Modern Panda Security solutions belong to the third generation AV software. First Generation: Antivirus From 1990 s, signature scanning including polymorphic virus recognition. Primitive heuristics. Second Generation: Antimalware From 2000, integrated firewall, anti-malware engine. Third Generation: Proactive Technologies From 2004, TruPrevent R, genetic and rootkit heuristics, behavioral analysis and blocking, uncloaking techniques, generic unpacking
49 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Modern Panda Security solutions belong to the third generation AV software. First Generation: Antivirus From 1990 s, signature scanning including polymorphic virus recognition. Primitive heuristics. Second Generation: Antimalware From 2000, integrated firewall, anti-malware engine. Third Generation: Proactive Technologies From 2004, TruPrevent R, genetic and rootkit heuristics, behavioral analysis and blocking, uncloaking techniques, generic unpacking
50 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Genetic Heuristic Engine, codename: Nereus was initially released in The innovation connected with Nereus rely on idea inspired by the field of genetics and its usefulness to understand how organisms are individually identified and associated to other organisms. Features: More than few hundred characteristics of each file that is scanned Complex malware recognition (type determination) Rootkit heuristics (time based analysis) Generic packer detectors and generic unpacking algorithms New threat automatic notification Automatic creation of detection and disinfection signatures for samples previously analyzed by processing and classification module
51 Panda s Technology Evolution Genetic Heuristic Engine - Nereus Figure: Panda s integrated endpoint security From [6]
52 Heuristic scanning is the most popular heuristic method for virus detection and recognition. Basically it is inherited from combination of pattern matching, automatic learning and environment emulation metaheuristics. As a heuristic method it s not 100% effective. So why do we apply HS? Pros Cons Can detect future threats User is less dependent on product update Improves conventional scanning results False positives Making decision after alarm requires knowledge
53 Peter Szor The Art of Computer Virus Research and Defense Addison-Wesley Professional, 1st edition, February 2005 Tomasz Andel, Krzysztof Zawadzki Techniki pisania wirusów oraz antywirusów Inynieria bezpieczeństwa systemów sieciowych i internetowych, PWr Wroclaw 2008 Frans Veldman Heuristic Anti-Virus Technology
54 Richard Zwienenberg Heuristic Scanners: Artificial Intelligence? edited by Éric Filiol Journal in Computer Virology Springer Paris, Volume Panda Research From Traditional Antivirus to Collective Intelligence August
55 Various online knowledge repositories For starters it s good to search wikipedia... VX Heavens Breaking Business and Technology News IEEE Zines: Phalcon/Skism: 40HEX, VLAD, VBB: Viruses Bits & Bytes, Immortal Riot: Insane Reality, NuKe: NuKe IntoJournal, Dark Angel VirGuide AND OTHER...
56 Why?... Questions? What if?...
57 THANK YOU
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