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Network Anomaly Detection Based on Artificial Intelligence

  • Chia-Mei Chen
  • Wen-Ling Lo
  • Gu-Hsin Lai
  • Yu-Chen Hu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 110)

Abstract

The cyber kill chain consists of the following stages: reconnaissance, weaponization, delivery, exploitation, installation, command and control (C2), actions on objectives. Based on the kill chain framework, identifying botnets is critical for defensing cyber attacks. Bot masters control the botnet through command and control servers; they often adopt the most commonly used communication channel such as through web connection in order to blend in malicious communication messages into massive normal traffic for detection evasion purpose.

By analyzing malicious and normal traffic, this study discovered the network anomalous patterns. Botnet connections exhibit some similarity behaviors which are not possessed by normal traffic. This study develops an anomaly score function to represent the anomalies and proposes a network anomaly detection method based on ant colony optimization algorithm and clustering algorithm. The experimental results show that the proposed anomaly detection method identifies botnets efficiently.

Keywords

Botnet Anomaly detection Artificial intelligence 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chia-Mei Chen
    • 1
  • Wen-Ling Lo
    • 1
  • Gu-Hsin Lai
    • 2
  • Yu-Chen Hu
    • 3
  1. 1.Department of Information ManagementNational Sun Yat-sen UniversityKaohsiungTaiwan
  2. 2.Department of Technology Crime InvestigationTaiwan Police CollegeTaipeiTaiwan
  3. 3.Department of Computer Science and Information ManagementProvidence UniversityTaichungTaiwan

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