ICTACT Journal on Communication Technology

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Next-generation Intrusion Detection and Prevention Systems for It and Network Security


Affiliations
1 Department of Information Technology, Sri Ramakrishna Engineering College, India
2 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
     

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In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devices within the IT infrastructure, ensuring the availability of labeled data that signifies known intrusion events. After meticulous preprocessing and feature engineering, we adapt the Dense VGG model, originally designed for image classification, to work with tabular IoT data. Transfer learning techniques are applied, leveraging pre-trained VGG models to expedite convergence and enhance performance. Real-time data streaming mechanisms are established to seamlessly integrate IoT data, making the system proactive in identifying threats. Upon detection, the system can respond by isolating affected devices, blocking suspicious network traffic, or initiating incident response protocols. Continuous monitoring and evaluation ensure the system reliability, with key metrics serving as indicators of its efficacy. Deployment considerations, such as scalability and redundancy, guarantee the system readiness to handle the influx of IoT data. Furthermore, integration with other security tools and compliance with regulatory standards strengthen the system overall cybersecurity posture. The core of our system lies in its intrusion detection logic, a set of rules and thresholds that trigger alerts or preventive measures based on model predictions. In testing, our system demonstrated an impressive intrusion detection accuracy of over 95%, significantly reducing false positives.

Keywords

Prevention Systems, Intrusion Detection, IoT Data, Dense VGG Classifier, Intrusion Detection Accuracy, Cybersecurity.
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Abstract Views: 66

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  • Next-generation Intrusion Detection and Prevention Systems for It and Network Security

Abstract Views: 66  |  PDF Views: 2

Authors

S. Bhaggiaraj
Department of Information Technology, Sri Ramakrishna Engineering College, India
S. Shanthini
Department of Information Technology, Sri Ramakrishna Engineering College, India
S. S. Sugantha Mallika
Department of Information Technology, Sri Ramakrishna Engineering College, India
R. Muthuram
Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

Abstract


In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devices within the IT infrastructure, ensuring the availability of labeled data that signifies known intrusion events. After meticulous preprocessing and feature engineering, we adapt the Dense VGG model, originally designed for image classification, to work with tabular IoT data. Transfer learning techniques are applied, leveraging pre-trained VGG models to expedite convergence and enhance performance. Real-time data streaming mechanisms are established to seamlessly integrate IoT data, making the system proactive in identifying threats. Upon detection, the system can respond by isolating affected devices, blocking suspicious network traffic, or initiating incident response protocols. Continuous monitoring and evaluation ensure the system reliability, with key metrics serving as indicators of its efficacy. Deployment considerations, such as scalability and redundancy, guarantee the system readiness to handle the influx of IoT data. Furthermore, integration with other security tools and compliance with regulatory standards strengthen the system overall cybersecurity posture. The core of our system lies in its intrusion detection logic, a set of rules and thresholds that trigger alerts or preventive measures based on model predictions. In testing, our system demonstrated an impressive intrusion detection accuracy of over 95%, significantly reducing false positives.

Keywords


Prevention Systems, Intrusion Detection, IoT Data, Dense VGG Classifier, Intrusion Detection Accuracy, Cybersecurity.

References