IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO
Source abstract
Abstract
Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a complete IoT botnet risk assessment system. The methodology helps security teams allocate resources by categorizing threats as high, medium, or low severity. This study shows how CICIoT2023 can assess cyber risks. Our research has implications beyond detection, as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
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