Human perspective to anomaly detection for cybersecurity

Traditionally signature-based network Intrusion Detection Systems (IDS) rely on inputs from domain experts and can only identify the attacks if they occur as individual event. IDS generate large number of alerts and it becomes very difficult for human users to go through each message. Previous researches have proposed analytics based approaches to analyze IDS alert patterns based on anomaly detection models, multi-steps models or probabilistic approaches. However, due to the complexities of network intrusions, it is impossible to develop all possible attack patterns or to avoid false positives. With the advance in technologies and popularity of networks in our daily life, it is becoming more and more difficult to detect network intrusions. However, no matter how rapid the technologies change, the human behaviors behind the cyber attacks stay relatively constant. This provides us an opportunity to develop an improved system to detect the unusual cyber attacks. In this paper, we developed four network intrusion models based on consideration of human factors. We then tested these models on ITOC Cyber Defense Competition (CDX) 2009 data. Our results are encouraging. These Models are not only able to recognize most network attacks identified by SNORT log alerts, they are also able to distinguish the non-attack network traffic that was potentially missed by SNORT as indicated by ground truth validation of the data.