Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Shanthini, S.
- 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
- G.I.P. Duppa and N. Surantha, “Evaluation of Network Security based on Next Generation Intrusion Prevention System”, Telecommunication Computing Electronics and Control, Vol. 17, No. 1, pp. 39-48, 2019.
- C.D.N. Kumar and V. Saravanan, “A Survival Study on Energy Efficient and Secured Routing in Mobile Adhoc Network”, International Organization of Scientific Research Journal of Computer Engineering, Vol. 2, No. 1, pp. 1-9, 2018.
- B. Gobinathan, P. Niranjan and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-9, 2021.
- J. Liang and Y. Kim, “Evolution of Firewalls: Toward Securer Network using Next Generation Firewall”, Proceedings of IEEE Annual Workshop on Computing and Communication, pp. 752-759, 2022.
- F.J. Siddiqui, H. Ashraf and A. Ullah, “Dual Server Based Security System for Multimedia Services in Next Generation Networks”, Multimedia Tools and Applications, Vol. 79, pp. 7299-7318, 2020.
- V. Saravanan and R. Rajkumar, “Secure Source-Based Loose RSA Encryption for Synchronization (SSOBRSAS) and Evolutionary Clustering Based Energy Estimation for Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science, Vol. 5, No. 5, pp. 1-12, 2014.
- J. Singh, J. Deepika, J. Sathyendra Bhat and S. Sakthivel, “Energy-Efficient Clustering and Routing Algorithm using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-13, 2022.
- S. Thirukumaran and S. Shanthana, “Enabling Self Auditing for Mobile Clients in Cloud Computing”, International Journal of Advanced Computer Technology, Vol. 2, No. 3, pp. 53-60, 2013.
- G. Uçtu, “A Suggested Testbed to Evaluate Multicast Network and Threat Prevention Performance of Next Generation Firewalls”, Future Generation Computer Systems, Vol. 124, pp. 56-67, 2021.
- M.T. Arefin and M.R. Alam, “Enterprise Network: Security Enhancement and Policy Management using Next-Generation Firewall (NGFW)”, Proceedings of International Conference on Computer Networks, Big Data and IoT, pp. 753-769, 2021.
- J.H. Park, “Symmetry-Adapted Machine Learning for Information Security”, Symmetry, Vol. 12, No. 6, pp. 1044-1049, 2020.
- J. Hussain and V. Hnamte, “Deep Learning based Intrusion Detection System: Software Defined Network”, Proceedings of Asian Conference on Innovation in Technology, pp. 1-6, 2021.
- T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.