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Bhaggiaraj, S.
- Fuzzy Based Optimization for Improving the Trust Score in Manets
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Authors
Affiliations
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2854-2860Abstract
In this paper, research develop a method for identifying abnormal behavior based on two inputs: the trustworthiness of the user, as well as the reliability of the recommendations that they make. Specifically, research look at the reliability of the user recommendations. The next thing that needs to be done is to calculate the node general trust value in order to determine if there has been any kind of malicious attack. This will show whether or not the node has been compromised in any way. It is conceivable that this could lessen the amount of power that is needed for the communication that takes place between different networks. Additionally, it demonstrates that the model is better able to utilize the evaluation results of the common neighbor nodes to synthesize the confidence value when fewer nodes are deployed in the network. This is demonstrated by the fact that fewer nodes are deployed in the network. The reliability of the trust assessment improves while the number of trusts for which recommendations are made decreases.Keywords
Fuzzy Optimization, Trust, Score, MANETs, Direct TrustReferences
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- Next-generation Intrusion Detection and Prevention Systems for It and Network Security
Abstract Views :40 |
PDF Views:2
Authors
Affiliations
1 Department of Information Technology, Sri Ramakrishna Engineering College, IN
2 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, IN
1 Department of Information Technology, Sri Ramakrishna Engineering College, IN
2 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 3 (2023), Pagination: 2992-2997Abstract
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
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