Antivirus Engines: From Methods to Innovations, Design, and Applications offers an in-depth exploration of the core techniques employed in modern antivirus software. It provides a thorough technical analysis of detection methods, algorithms, and integration strategies essential for the development and enhancement of antivirus solutions. The examples provided are written in Python, showcasing foundational, native implementations of key concepts, allowing readers to gain practical experience with the underlying mechanisms of antivirus technology.
The text covers a wide array of scanning techniques, including heuristic and smart scanners, hexadecimal inspection, and cryptographic hash functions such as MD5 and SHA for file integrity verification. These implementations highlight the crucial role of various scanning engines, from signature-based detection to more advanced models like behavioral analysis and heuristic algorithms. Each chapter provides clear technical examples, demonstrating the integration of modules and methods required for a comprehensive antivirus system, addressing both common and evolving threats.
Beyond simple virus detection, the content illustrates how polymorphic malware, ransomware, and state-sponsored attacks are tackled using multi-layered approaches. Through these examples, students, researchers, and security professionals gain practical insight into the operation of antivirus engines, enhancing their ability to design or improve security solutions in a rapidly changing threat environment.
1. 13.1 Introduction - p. 279 2. 13.2 Heuristic detection on a multiparameter strategy - p. 279 3. 13.3 Heuristic detection on discrete file regions - p. 287 4. 13.4 Heuristic in-depth use of Information Content - p. 297 5. 13.5 Heuristic detection on strings - p. 309 6. 13.6 Heuristic detection using PWMs - p. 322 7. 13.7 Heuristic detection using Markov chains - p. 339 8. 13.8 Heuristic detection using transition matrices - p. 349 9. 13.8.1 Transition matrix generator for binary data - p. 349 10. 13.8.2 Transition matrix signature generator (I) - p. 353 11. 13.8.3 Single file malware scanner (II) - p. 356 12. 13.8.4 Malware scanner with transition matrix signature-based detection - p. 360 13. 13.9 Heuristics by using perceptrons - p. 365 14. 13.9.1 Perceptron data generator for a malware scanner (I) - p. 365 15. 13.9.2 Malware scanner—Perceptron level-up binary signature training - p. 368 16. 13.10 Heuristics by random sampling convergence - p. 373 17. 13.10.1 Random sampling—Signature generator - p. 373 18. 13.10.2 Signature generator based on random sampling (I) - p. 376 19. 13.10.3 Malware signature scanner with signature file and linear search (II) - p. 379 20. 13.10.4 Malware scanner with fault tolerant matching and binary search (III) - p. 383 21. 13.10.5 Signature generator vs malware scanner - p. 387 22. 13.10.6 Signature convergence via genetic algorithms - p. 388 23. 13.11 Heuristics by searching for opcodes - p. 388 24. 13.11.1 The opcodes - p. 389 25. 13.11.2 The conditional vs unconditional jumps - p. 389 26. 13.11.3 The Entry Point and unconditional jumps - p. 390 27. 13.11.4 Types of jumps - p. 390 28. 13.11.5 Heuristic PE file analyzer - p. 391 29. 13.11.6 Single PE file scanner with weighted jumps - p. 392 30. 13.11.7 A heuristic scanner using weighted jumps - p. 394 31. 13.12 Heuristics of Entry Point obfuscation - p. 397 32. 13.12.1 Entry Point: High entropy - p. 397 33. 13.12.2 Entry Point: Low entropy - p. 397 34. 13.12.3 Entry Point: Moderate entropy - p. 398 35. 13.12.4 Entry Point obfuscation - p. 398 36. 13.12.5 The baseline scanner (I) - p. 398 37. 13.12.6 The EPO scanner (II) - p. 401 38. 13.12.7 EPO: Concentration vs dilution - p. 404 39. 13.12.8 The NOP scanner - p. 404 40. 13.13 Conclusions - p. 407 41. References - p. 407 42. Chapter 14 Smart scanners and rabbit holes - p. 409 43. Chapter 15 Process and user behavior - p. 443 44. Chapter 16 The network - p. 501 45. Glossary - p. 543 46. Appendices - p. 553 47. References - p. 627 48. Index - p. 629
Antivirus Engines: From Methods to Innovations, Design, and Applications offers an in-depth exploration of the core techniques employed in modern antivirus software. It provides a thorough technical analysis of detection methods, algorithms, and integration strategies essential for the development and enhancement of antivirus solutions. The examples provided are written in Python, showcasing foundational, native implementations of key concepts, allowing readers to gain practical experience with the underlying mechanisms of antivirus technology.
The text covers a wide array of scanning techniques, including heuristic and smart scanners, hexadecimal inspection, and cryptographic hash functions such as MD5 and SHA for file integrity verification. These implementations highlight the crucial role of various scanning engines, from signature-based detection to more advanced models like behavioral analysis and heuristic algorithms. Each chapter provides clear technical examples, demonstrating the integration of modules and methods required for a comprehensive antivirus system, addressing both common and evolving threats.
Beyond simple virus detection, the content illustrates how polymorphic malware, ransomware, and state-sponsored attacks are tackled using multi-layered approaches. Through these examples, students, researchers, and security professionals gain practical insight into the operation of antivirus engines, enhancing their ability to design or improve security solutions in a rapidly changing threat environment.
1. 13.1 Introduction - p. 279 2. 13.2 Heuristic detection on a multiparameter strategy - p. 279 3. 13.3 Heuristic detection on discrete file regions - p. 287 4. 13.4 Heuristic in-depth use of Information Content - p. 297 5. 13.5 Heuristic detection on strings - p. 309 6. 13.6 Heuristic detection using PWMs - p. 322 7. 13.7 Heuristic detection using Markov chains - p. 339 8. 13.8 Heuristic detection using transition matrices - p. 349 9. 13.8.1 Transition matrix generator for binary data - p. 349 10. 13.8.2 Transition matrix signature generator (I) - p. 353 11. 13.8.3 Single file malware scanner (II) - p. 356 12. 13.8.4 Malware scanner with transition matrix signature-based detection - p. 360 13. 13.9 Heuristics by using perceptrons - p. 365 14. 13.9.1 Perceptron data generator for a malware scanner (I) - p. 365 15. 13.9.2 Malware scanner—Perceptron level-up binary signature training - p. 368 16. 13.10 Heuristics by random sampling convergence - p. 373 17. 13.10.1 Random sampling—Signature generator - p. 373 18. 13.10.2 Signature generator based on random sampling (I) - p. 376 19. 13.10.3 Malware signature scanner with signature file and linear search (II) - p. 379 20. 13.10.4 Malware scanner with fault tolerant matching and binary search (III) - p. 383 21. 13.10.5 Signature generator vs malware scanner - p. 387 22. 13.10.6 Signature convergence via genetic algorithms - p. 388 23. 13.11 Heuristics by searching for opcodes - p. 388 24. 13.11.1 The opcodes - p. 389 25. 13.11.2 The conditional vs unconditional jumps - p. 389 26. 13.11.3 The Entry Point and unconditional jumps - p. 390 27. 13.11.4 Types of jumps - p. 390 28. 13.11.5 Heuristic PE file analyzer - p. 391 29. 13.11.6 Single PE file scanner with weighted jumps - p. 392 30. 13.11.7 A heuristic scanner using weighted jumps - p. 394 31. 13.12 Heuristics of Entry Point obfuscation - p. 397 32. 13.12.1 Entry Point: High entropy - p. 397 33. 13.12.2 Entry Point: Low entropy - p. 397 34. 13.12.3 Entry Point: Moderate entropy - p. 398 35. 13.12.4 Entry Point obfuscation - p. 398 36. 13.12.5 The baseline scanner (I) - p. 398 37. 13.12.6 The EPO scanner (II) - p. 401 38. 13.12.7 EPO: Concentration vs dilution - p. 404 39. 13.12.8 The NOP scanner - p. 404 40. 13.13 Conclusions - p. 407 41. References - p. 407 42. Chapter 14 Smart scanners and rabbit holes - p. 409 43. Chapter 15 Process and user behavior - p. 443 44. Chapter 16 The network - p. 501 45. Glossary - p. 543 46. Appendices - p. 553 47. References - p. 627 48. Index - p. 629
