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Bio/nature-inspired algorithms in A.I. for malicious activity detection

Bio/nature-inspired algorithms in A.I. for malicious activity detection

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In this chapter, we discussed the importance of bio-inspired techniques in the field of intrusion detection. A brief explanation is given on the most popular cyberattacks, such as DDoS attacks, as well as the most popular concepts of bio-inspired algorithm detection.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 Towards technology through nature
  • 9.2.1 Terminologies of bio/nature-inspired algorithms
  • 9.2.1.1 Populations
  • 9.2.1.2 Selection
  • 9.2.1.3 Crossover
  • 9.2.1.4 Mutation
  • 9.2.2 Review of bio/nature-inspired algorithms
  • 9.2.2.1 Artificial neural networks
  • 9.2.2.2 Evolutionary algorithms
  • 9.2.2.3 Swarm intelligence algorithms
  • 9.2.2.4 Artificial immune systems
  • 9.2.2.5 Fuzzy logic
  • 9.2.2.6 Chaos theory
  • 9.2.2.7 Game theory
  • 9.3 Cyberattacks and malware detection
  • 9.3.1 Distributed/Denial of service attacks
  • 9.3.2 Botnets
  • 9.3.3 Malware
  • 9.3.3.1 Viruses
  • 9.3.3.2 (Remote access) Trojan horses
  • 9.3.3.3 Rootkits
  • 9.3.3.4 Backdoors
  • 9.3.3.5 Spyware
  • 9.3.3.6 Worms
  • 9.3.3.7 Ransomware
  • 9.3.4 Probe attacks
  • 9.3.5 Buffer overflow
  • 9.3.6 Brute force attack
  • 9.3.7 Masquerading attacks
  • 9.3.8 Datasets used in intrusion detection
  • 9.3.8.1 DARPA dataset
  • 9.3.8.2 KDD99 dataset
  • 9.3.8.3 ISCX IDS 2012 dataset
  • 9.3.8.4 ISCX IDS 2017 dataset
  • 9.3.8.5 Botnet dataset
  • 9.3.8.6 CIC DoS dataset
  • 9.3.8.7 TheAWID dataset
  • 9.3.8.8 The UNSW-NB15 dataset
  • 9.4 Bio/Nature-inspired algorithm studies in intrusion detection
  • 9.4.1 Game theoretic studies
  • 9.4.2 Evolution strategies studies
  • 9.4.3 Genetic algorithms studies
  • 9.4.4 Fuzzy logic studies
  • 9.4.5 Swarm intelligence studies
  • 9.4.6 Artificial neural network studies
  • 9.4.7 Artificial immune systems studies
  • 9.4.8 Chaos theory studies
  • 9.5 Case study: application layer (D)DoS detection
  • 9.5.1 Evaluation environment
  • 9.5.1.1 Network simulator 3
  • 9.5.1.2 Simulation of (D)DoS attacks
  • 9.5.2 Feature selection
  • 9.5.2.1 Requests number
  • 9.5.2.2 Packets number
  • 9.5.2.3 Data rate
  • 9.5.2.4 Average packet size
  • 9.5.2.5 Average time between requests
  • 9.5.2.6 Average time between response and request
  • 9.5.2.7 Average time between responses
  • 9.5.2.8 Parallel requests
  • 9.5.3 Intrusion detection evaluation metrics
  • 9.5.3.1 True positive rate
  • 9.5.3.2 True negative rate
  • 9.5.3.3 False positive rate
  • 9.5.3.4 False negative rate
  • 9.5.3.5 Precision
  • 9.5.4 Experiments results analysis
  • 9.5.5 Results analysis and discussion
  • 9.6 Discussion
  • 9.7 Conclusion
  • References

Inspec keywords: computer network security; Internet of Things; biomimetics; computer crime; invasive software

Other keywords: learning algorithms; malicious software; Internet of Things; bio-inspired algorithm detection; IoT; intrusion detection; bio-inspired techniques; DDoS

Subjects: Ubiquitous and pervasive computing; Computer networks and techniques; Data security

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