The number of cyber-attacks and data breaches has immensely increased across different enterprises, companies, and industries as a result of the exploitation of the weaknesses in securing Internet of Things (IoT) devices. The increasing number of various devices connected to IoT and their different protocols has led to growing volume of zero-day attacks. Deep learning (DL) has demonstrated its superiority in big data fields and cyber-security. Recently, DL has been used in cyber-attacks detection because of its capability of extracting and learning deep features of known attacks and detecting unknown attacks without the need for manual feature engineering. However, DL cannot be implemented on IoT devices with limited resources because it requires extensive computation, strong power and storage capabilities. This paper presents a comprehensive attack detection framework of a distributed, robust, and high detection rate to detect several IoT cyber-attacks using DL. The proposed framework implements an attack detector on fog nodes because of its distributed nature, high computational capacity and proximity to edge devices. Six DL models are compared to identify the DL model with the best performance. All DL models are evaluated using five different datasets, each of which involves various attacks. Experiments show that the long short-term memory model outperforms the five other DL models. The proposed framework is effective in terms of response time and detection accuracy and can detect several types of cyber-attacks with 99.97% detection rate and 99.96% detection accuracy in binary classification and 99.65% detection accuracy in multi-class classification.

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Recently, the development of distributed renewable energy resources, smart devices, and smart grids empowers the emergence of peer-to-peer energy trading via local energy markets. However, due to security and privacy concerns in energy trading, sensitive information of energy traders could be leaked to an adversary. In addition, malicious users could perform attacks against the energy market, such as collusion, double spending, and repudiation attacks. Moreover, network attacks could be executed by external attackers against energy networks, such as eavesdropping, data spoofing, and tampering attacks. To tackle the abovementioned attacks, we propose a secure and privacy-preserving energy trading system (SPETS). First, a permissioned energy blockchain is presented to perform secure energy transactions between energy sellers and buyers. Second, a discrete-time double auction is proposed for energy allocation and pricing. Third, the concept of reputation scores is adopted to guarantee market reliability and trust. The proposed energy system is implemented using Hyperledger Fabric (HF) where the chaincode is utilized to control the energy market. Theoretical analysis proves that SPETS is resilient to several security attacks. Simulation results demonstrate the increase in sellers’ and buyers’ welfare by approximately 76.5% and 26%, respectively. The proposed system ensures trustfulness and guarantees efficient energy allocation. The benchmark analysis proves that SPETS consumes few resources in terms of memory and disk usage, CPU, and network utilization.