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APT-Dt-KC: advanced persistent threat detection based on kill-chain model


Advanced persistent threat attacks are considered as a serious risk to almost any infrastructure since attackers are constantly changing and evolving their advanced techniques and methods. It is difficult to use traditional defense for detecting the advanced persistent threat attacks and protect network information. The detection of advanced persistent threat attack is usually mixed with many other attacks. Therefore, it is necessary to have a solution that is safe from error and failure in detecting them. In this paper, an intelligent approach is proposed called “APT-Dt-KC” to analyze, identify, and prevent cyber-attacks using the cyber-kill chain model and matching its fuzzy characteristics with the advanced persistent threat attack. In APT-Dt-KC, Pearson correlation test is used to reduce the amount of processing data, and then, a hybrid intrusion detection method is proposed using Bayesian classification algorithm and fuzzy analytical hierarchy process. The experimental results show that APT-Dt-KC has a false positive rate and false negative rate 1.9% and 3.6% less than the existing approach, respectively. The accuracy and detection rate of APT-Dt-KC has reached 98% with an average improvement of 5% over the existing approach.

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Correspondence to Meghdad Mirabi.

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Panahnejad, M., Mirabi, M. APT-Dt-KC: advanced persistent threat detection based on kill-chain model. J Supercomput 78, 8644–8677 (2022).

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  • Advanced persistent threat
  • Cyber-kill chain
  • Bayesian classification
  • Fuzzy clustering
  • Support vector machine