We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
SILENCING EAVESDROPPERS: SVM UNVEILING PHYSICAL LAYER THREATS.
- Authors
G., POOVARASAN; G., VIDHYA SRI; G., SINGARAVEL
- Abstract
Frothy Disturbance Intrusion Detection Systems (FIDS) can help detect and prevent security attacks using the Support Vector Machine (SVM) algorithm. Recognizing the importance of FIDS in protecting various domains linked to the internet, focus lies on adapting traditional intrusion detection methods for the landscape, which faces challenges such as resource constraints and limited memory and battery capacity. This study entails the creation of a lightweight attack detection technique that utilizes a supervised machine learning-based FIDS using the SVM algorithm. Simulations are used to demonstrate the usefulness of the proposed SVM-based FIDS classifier, which employs a combination of two or three complex features and achieves satisfactory classification accuracy and detection time. This strategy has the ability to enhance application security by effectively addressing the particular.
- Subjects
SUPPORT vector machines
- Publication
I-Manager's Journal on Information Technology, 2024, Vol 13, Issue 1, p42
- ISSN
2277-5110
- Publication type
Article
- DOI
10.26634/jit.13.1.20506