Open Access Open Access  Restricted Access Subscription Access

Machine Learning based Advanced Persistent Threat Attack Detection System

Aswathi Radhakrishnan K M

Abstract


An advanced persistent threat [APT] is a multistage selective attack that obtains unauthorised access to data and correspondence frameworks to channel classified information or cause harm to an organization, industry, or government association. In most of the situations where APT is successfully organized and ready to attack, defending against APT is too late, especially in the last phase. In the context of such long-term and undetected attacks, detection of these attacks based on the attack life cycle is important. Several approaches including machine learning techniques have been proposed to improve the problem of detection.

 


Full Text:

PDF

References


A. Alshamrani, S. Myneni, A. Chowdhary, and D. Huang. (2019) A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities.

I. Rosenberg, G. Sicard, and E. O. David, “Deepapt: nation-state apt attribution using end-to-end deep neural networks,” in International Conference on Artificial Neural Networks. Springer, 2017, pp. 91–99.

L. Shang, D. Guo, Y. Ji, and Q. Li, “Discovering unknown advanced persistent threat using shared features mined by neural networks,” Computer Networks, vol. 189, p. 107937, 2021.

I. Ghafir, M. Hammoudeh, V. Prenosil, L. Han, R. Hegarty, K. Rabie, and F. J. Aparicio-Navarro, “Detection of advanced persistent threat using machine-learning correlation analysis,” Future Generation Computer Systems, vol. 89, pp. 349–359, 2018.

W.-L. Chu, C.-J. Lin, and K.-N. Chang, “Detection and classification of advanced persistent threats and attacks using the support vector machine,” Applied Sciences, vol. 9, no. 21, p. 4579, 2019.

F. J. Abdullayeva, “Advanced persistent threat attack detection method in cloud computing based on autoencoder and softmax regression algorithm,” Array, vol. 10, p. 100067, 2021.

M. Ramilli, “Malware training sets: a machine learning dataset for everyone,” 2016, [Online; December 2016]. [Online].

Available: https://marcoramilli.com/2016/12/16/malware-training-sets-a-machine-learning-dataset-for-everyone/

S. Myneni, A. Chowdhary, A. Sabur, S. Sengupta, G. Agrawal, D. Huang, and M. Kang, “Dapt 2020-constructing a benchmark dataset for advanced persistent threats,” in International Workshop on Deploy- able Machine Learning for Security Defense. Springer, 2020, pp. 138– 163.

J. Lu, K. Chen, Z. Zhuo, and X. Zhang, “A temporal correlation and traffic analysis approach for apt attacks detection,” Cluster computing, vol. 22, no. 3, pp. 7347–7358, 2019.


Refbacks

  • There are currently no refbacks.