Advanced Persistent Threat Detection Performance Analysis Based on Machine Learning Models


  • Anil Kumar Reseach Scholar, Department of CSE, GJU S&T, Hisar and Assistant Professor of Computer Science, Indira Gandhi Govt. College, Tohana, Harayana, India
  • Amandeep Noliya Assistant Professor, Department of Artificial Intelligence and Data Science, Guru Jambheshwar University of Science & Technology, Hisar, India
  • Ritu Makani Associate Professor, Department of CSE, GJU S&T, Hisar, India
  • Pardeep Kumar Assistant Professor of Computer Science, Indira Gandhi Govt. College, Tohana, Harayana, India
  • Jagsir Singh Assistant Professor of Computer Science, Indira Gandhi Govt. College, Tohana, Harayana, India


Enter APT, APT Machine Learning, SVM, KNN, CNN


Advanced Persistent Threats (APTs) present a serious threat to modern cyber security, prompting research and evaluation of effective detection techniques. The on-going development of Advanced Persistent Threats (APTs) has motivated the investigation of novel strategies for preventing their malicious activities. The research presented here provides an in-depth investigation of machine learning-based APT detection techniques. APTs are explained in the beginning along with their features and the specifics of their attack models. By outlining their attack techniques and tactics, further analyse APTs. An extensive examination of APT attack detection strategies is covered in this study, with a focus on machine learning techniques. In the context of APT detection, Support Vector Machines (SVM), k-Nearest Neighbours (KNN), Deep Belief Networks (DBN), Decision Trees, and Convolutional Neural Networks (CNN) are considered. The underlying assumptions and applicability of each method for APT detection are evaluated. The performance study of the aforementioned machine learning approaches is the main goal of this research. To facilitate this, GiuseppeLaurenza/I_F_Identifier datasetis employed, which comprises a diverse range of network traffic scenarios. Different performance metrics, including precision, recall, F1-score, accuracy, true positive rate, and true negative rate, are employed to gauge the effectiveness of the detection techniques. The results unveiled in this study underline the superiority of Convolutional Neural Networks (CNN) over the other examined methods. The precision, recall, F1-score, accuracy, true positive rate, and true negative rate metrics collectively endorse CNN's prowess in accurately and comprehensively detecting APT attacks within network traffic. These findings not only contribute to the ongoing discourse on APT detection but also underscore the efficacy of CNNs in fortifying cyber security systems against sophisticated threats.


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How to Cite

Kumar, A. ., Noliya, A. ., Makani, R. ., Kumar, P. ., & Singh, J. . (2023). Advanced Persistent Threat Detection Performance Analysis Based on Machine Learning Models. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 741–757. Retrieved from



Research Article