Improved Cybersecurity Framework based on Truckingc Heuristics Algorithm for Detecting Malicious Devices in Fog Computing and Internet of Things (IoT) Environments

• Main Authors: Lamir, Isah Muhammad, Ya'u Gital, Abdulsalam, Ibrahim, Kabiru Musa, Lawal, Mustapha Abdulrahman, Danlami, Mohammed, Yakubu, Ismail Zahraddeen
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Cyber Attacks and Denial of Service; Denial-of-service attack; Hidden Markov Model; Hidden Markov models; Honeypots; Image edge detection; Intrusion Detection; Prediction algorithms; Vertibi; Viterbi algorithm; Web services
• ISSN: 2642-6595
• DOI: 10.1109/ICAECC59324.2023.10560227

The ever-increasing demands for real time data generation and processing, the novel cloud computing concept alone cannot, in any way effectively and efficiently handle the high demand of resources and response time, thus, the need for the introduction of the novel fog computing concept that focuses on decentralization and as well, serves as an intermediary between the cloud and IoT. However, security challenges evolving from these trends exponentially grow at a rate exceeding the progression in security solutions remains a major concern. Security of IoT devices remains one of the notable challenges that greatly hinders successful implementation of this novel concept as justified in literatures. In this research work, an improved Cybersecurity Framework based on truckingc heuristics algorithm is proposed using three techniques: Intrusion Detection System (IDS), Hidden Markov Models (HMM) and Improved Virtual Honeypot (IVHD) to categories devices into four different levels based on their intended mission, creates logs for messages exchanged via all interfaces and as well monitors communication traffic to detect malicious edge devices, detects attack source by tracking back the channels used by malicious devices, physically localize position of malicious nodes for instant isolation to cure attacked devices and revert them back to their initial states and as well, averts and mitigates Distributed Denial of Service (DDoS) attacks respectively over the Fog layer. Experimental results indicates that the detection accuracy increases with increase in the number of deployed Honeypots. Consequently, the proposed cyber security framework work therefore improves on existing frameworks via effectively detecting, tracking & isolating malicious edge devices, recovers hacked or compromised devices back to their normal states and dynamically averts and mitigate DDoS attacks over fog computing environment and Internet of Things (IoT) environment.