Secure Intelligent Fuzzy Blockchain Framework: Effective Threat Detection in IoT Networks
Department
Software Engineering and Game Development
Document Type
Article
Publication Date
1-1-2023
Abstract
Integrating blockchain into the Internet of Things (IoT) for security is a new development in computational communication systems. While security threats are changing their strategies and constructing new threats on blockchain-based IoT systems. Also, in combining blockchain with IoT networks, malicious transactions and active attacks deliver more vulnerabilities, privacy issues, and security threats. The concept of blockchain-based IoT attacks is a hot topic in both IoT and blockchain disciplines. Network attacks are a type of security and privacy threat and cover the exact scope of threats related to the combination of IoT and blockchain. Even though blockchain has potential security benefits, new cyberattacks have emerged that make blockchain alone insufficient to deal with threats and attacks in IoT networks since vagueness and ambiguity issues are unavoidable in IoT data. The heterogeneous nature of IoT sources has made uncertainty a critical issue in IoT networks. Deep Learning (DL) models have difficulty dealing with uncertainty issues and cannot manage them efficiently as an essential tool in security techniques. Thus, we need better security, privacy, and practical approaches, such as efficient threat detection against network attacks in blockchain-based IoT environments. Also helpful to consider fuzzy logic to tackle deterministic issues when DL models face uncertainty. This paper designs and implements a secure, intelligent fuzzy blockchain framework. This framework utilizes a novel fuzzy DL model, optimized adaptive neuro-fuzzy inference system (ANFIS)-based attack detection, fuzzy matching (FM), and fuzzy control system (FCS) for detection of network attacks. The proposed fuzzy DL applies the fuzzy Choquet integral to have a powerful nonlinear aggregation function in the detection. We use metaheuristic algorithms to optimize the attack detection error function in ANFIS. We also validate transactions via FM to tackle fraud detection and efficiency in the blockchain layer. This framework is the first secure, intelligent fuzzy blockchain framework that identifies and detects security threats while considering uncertainty issues in IoT networks and having more flexibility in decision-making and accepting transactions in the blockchain layer. Evaluation results verify the efficiency of the blockchain layer in throughput and latency metrics and the intelligent fuzzy layer in performance metrics (Accuracy, Precision, Recall, and F1-Score) in threat detection on both blockchain and IoT network sides. Additionally, FCS demonstrates that we obtain an effective system (stable model) for threat detection in blockchain-based IoT networks.
Journal Title
Computers in Industry
Journal ISSN
01663615
Volume
144
Digital Object Identifier (DOI)
10.1016/j.compind.2022.103801