"Harmony Search Hawks Optimization for IoT Intrusion Detection with Deep RL & NMF"

What is it about?

With the advent of the Internet of Things (IoT), the security of the network layer in IoT is getting more attention in recent decades. Various intrusion detection methods were developed in the existing research works, but the capability to detect malicious and intrusion activities in the complex Internet environment poses a challenging task in IoT. Hence, an effective and optimal intrusion detection mechanism, named Harmony Search Hawks Optimization-based Deep Reinforcement Learning (HSHO-based Deep RL), is proposed in this research to detect malicious network activities. The proposed Harmony Search Hawks Optimization (HSHO) algorithm is designed by integrating Harmony Search (HS) with the Harris Hawks Optimization (HHO) algorithm. However, the optimal detection result that is effectively achieved through the fitness measures such that the minimum fitness value is only declared as the optimal solution. The Deep Reinforcement Learning (Deep RL) classifier effectively detects the malicious or intruder behaviors and generates a satisfactory result. By reducing the dimensionality of data using nonnegative matrix factorization, the data is optimally fit to perform intrusion detection process in the IoT environment. The proposed HSHO-based Deep RL obtained better performance in terms of the metrics like accuracy (96.925%), True Positive Rate (TPR; 96.90%), and True Negative Rate (TNR; 97.920%) with respect to K -fold.

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Why is it important?

With the proliferation of IoT devices, ensuring their security has become increasingly important. The interconnected nature of IoT devices makes them vulnerable to various security threats. Intrusion detection mechanisms are crucial for identifying and mitigating these threats IoT networks are inherently complex due to the large number of interconnected devices and the diverse nature of the data they generate. Traditional intrusion detection methods may not be effective in such environments, necessitating the development of more advanced techniques.

Perspectives

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The statement begins by highlighting the increased focus on network layer security within the Internet of Things (IoT) landscape. This sets the context by acknowledging the growing concern regarding security vulnerabilities in IoT networks, which arises due to the proliferation of interconnected devices. It mentions that various intrusion detection methods have been developed, indicating a significant body of prior research in this area. This suggests a foundation of knowledge and techniques upon which new solutions can be built.

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