Latent Semantic Analysis and Graph Theory for Alert Correlation: A Proposed Approach for IoT Botnet Detection

• Published in: IEEE open journal of the Communications Society 2024, Vol.5, p.3904-3919
• Main Authors: Lefoane, Moemedi, Ghafir, Ibrahim, Kabir, Sohag, Awan, Irfan-Ullah, El Hindi, Khalil, Mahendran, Anand
• Format: Article
• Language: English
• Subjects: Accuracy; Botnet; Botnet attack; Correlation; Feature extraction; Graph theory; graph-based analysis; Internet of Things; intrusion detection system; latent semantic analysis; machine learning; Malware; Semantic analysis; Semantics; Unsupervised learning
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2024.3419570

In recent times, the proliferation of Internet of Things (IoT) technology has brought a significant shift in the digital transformation of various industries. The enabling technologies have accelerated this adoption. The possibilities unlocked by IoT have been unprecedented, leading to the emergence of smart applications that have been integrated into national infrastructure. However, the popularity of IoT technology has also attracted the attention of adversaries, who have leveraged the inherent limitations of IoT devices to launch sophisticated attacks, including Multi-Stage attacks (MSAs) such as IoT botnet attacks. These attacks have caused significant losses in revenue across industries, amounting to billions of dollars. To address this challenge, this paper proposes a system for IoT botnet detection that comprises two phases. The first phase aims to identify IoT botnet traffic, the input to this phase is the IoT traffic, which is subjected to feature selection and classification model training to distinguish malicious traffic from normal traffic. The second phase analyses the malicious traffic from stage one to identify different botnet attack campaigns. The second stage employs an alert correlation approach that combines the Latent Semantic Analysis (LSA) unsupervised learning and graph theory based techniques. The proposed system was evaluated using a publicly available real IoT traffic dataset and yielded promising results, with a True Positive Rate (TPR) of over 99% and a False Positive Rate (FPR) of 0%.