Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

Abstract

With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) technique. In the BAOML-CADC technique, the major focus lies in identifying cyberattacks. To do so, the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection (TEA-FS) method for the optimal choice of features. The BAOML-CADC technique uses an extreme learning machine (ELM) model for cyberattack recognition. In addition, a BC-based integrity verification technique is developed to defend against the misrouting attack, showing the innovation of the work. The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset. The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

“…In Equation (7), |R| refers to the cardinality of the selected subset, γ R (D) indicates the classification error rate, |C| symbolizes the overall feature count, and parameters α and β are respective to the importance of classification quality and subset length. ∈ [1,0] and β = 1 − α.…”

“…Nevertheless, cyber threats are regularly harmful and interrupt internet services day by day. This encourages numerous researchers to provide their broad offers to pattern security systems [7].…”

“…It is helpful to identify the well-known attacks with the fewest false alerts or alarms. The technique needs a few conversions of the rules of attacks and signatures on the datasets [7]. Researchers have been encouraged to use decentralized IDSs that incorporate various machine learning (ML) methods comprising optimization algorithms, deep learning (DL), and artificial neural networks (ANNs), on account of current developments in intelligent machines.…”