Comprehensive Analysis of Implementation and Evaluation IoT based Techniques in Networked Security Systems

 

Raenu Kolandaisamy1, Suhas Gupta2, Shashikant Patil3, Jaymeel Shah4, Abhinav Mishra5, N. Gobi6

 

1 Full time Student Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Malaysia

2 Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

3 Professor, Department of uGDX, ATLAS SkillTech University, Mumbai, Maharashtra, India

4 Associate Professor, Department of Computer science and Engineering, Faculty of Engineering and Technology, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India

5 Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India

6Assistant Professor, Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, Karnataka, India

Emails:  raenu@ucsiuniversity.edu.my;  suhas.gupta.orp@chitkara.edu.in;    shashikant.patil@atlasuniversity.edu.in; jaimeel.shah@paruluniversity.ac.in; abhinav.mishra.orp@chitkara.edu.in; gobi.n@jainuniversity.ac.in

 

Corresponding Author: Raenu Kolandaisamy, Email: raenu@ucsiuniversity.edu.my

 


Text Box: Abstract This research introduces an advanced network security methodology based on IoT, combining five innovative algorithms: Dynamic Threat Detection (DTD), Adaptive Intrusion Prevention System (AIPS), Anomaly-Based Security Metrics (ABSM), Context-Aware Firewall (CAF), and Cognitive Security Assessment (CSA). Each algorithm contributes specific functionalities, ranging from real-time threat detection and adaptive policy adjustments to anomaly quantification, contextual rule modifications, and holistic security risk assessments. The ablation study conducted on each algorithm reveals critical components driving their performance, ensuring a deep understanding of their inner workings. The proposed method demonstrates superior performance in accuracy, scalability, usability, and adaptability compared to existing network security methods. Visual representations and a comprehensive evaluation further validate the proposed method's effectiveness, positioning it as an advanced and efficient solution for addressing evolving network security challenges.


 


Received: September 24, 2023 Revised: February 07, 2024 Accepted: June 19, 2024

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Keywords: security algorithms; threat detection; intrusion prevention, IoT; anomaly detection; firewall; cognitive assessment; machine learning; adaptive monitoring; continuous improvement; network context.