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The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain. |
