Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

• Published in: SN computer science 2024-11, Vol.5 (8), p.1056, Article 1056
• Main Authors: Chakraborty, Sanjay, Pandey, Saroj Kumar, Maity, Saikat, Dey, Lopamudra
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Assaults; Barriers; Behavior; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Deep learning; Feature selection; Information Systems and Communication Service; Internet of Things; Machine learning; Malware; Neural networks; Original Research; Pattern Recognition and Graphics; Polymorphism; Ransomware; Software Engineering/Programming and Operating Systems; Vision
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03429-5

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper.