NDLI: Security Enhancement in Wireless Sensor Networks Using Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Raj, A.B. Ramesh, M.V. Kulkarni, R.V. Hemalatha, T.
Copyright Year	2012
Abstract	Ensuring the security of wireless sensor networks (WSNs) is vital for monitoring real-time systems. One of the major security flaws experienced by WSNs is denial of service (DoS) which can even lead to the breakdown of the complete system or to wrong decisions being made by the system that can cause adverse results. This research work focuses on two techniques for detecting a DoS attack at a medium access control (MAC) layer. Our research compares and evaluates the performance of two major machine learning techniques: neural network (NN) and support vector machine (SVM). Vanderbilt Prowler is used for simulating the scenarios. In the simulations, normalized critical parameters and their corresponding probabilities of DoS attack are computed in 50 trial runs. These normalized critical parameters and their corresponding probabilities of DoS attack are used as training inputs in NN and SVM approaches. The simulation results clearly show that SVM provides better accuracy compared to NN, 97% accuracy by SVM and 91% accuracy by NN. The simulation also shows that SVM takes much less time to detect and determine the probability of a DoS attack, 0.25 seconds by SVM and 0.75 seconds by NN. All these results clearly show that SVM performs better than NN when used for detecting the probability of DoS attack in WSNs.
Starting Page	1264
Ending Page	1269
File Size	379991
Page Count	6
File Format	PDF
ISBN	9781467321648
DOI	10.1109/HPCC.2012.186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-25
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Wireless sensor networks Neural Network Denial of Service Artificial neural networks Vectors Support Vector Machine Wireless Sensor Networks Security Computer crime
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes

Denial of service detection by support vector machines and radial-basis function neural network

Wireless Sensor Network Security: Real-Time Detection and Prevention of Attacks

Comparison of different fusion approaches for network intrusion detection using ensemble of RBFNN

Integrating Machine Learning Techniques to Constitute a Hybrid Security System

Detecting denial of service attacks using support vector machines

Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines

Reducing neural network training data using support vectors

Denial-of-service (DoS) detection through practical entropy estimation on hierarchical sensor networks

Security Enhancement in Wireless Sensor Networks Using Machine Learning

Similar Documents

Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes

Denial of service detection by support vector machines and radial-basis function neural network

Wireless Sensor Network Security: Real-Time Detection and Prevention of Attacks

Comparison of different fusion approaches for network intrusion detection using ensemble of RBFNN

Integrating Machine Learning Techniques to Constitute a Hybrid Security System

Detecting denial of service attacks using support vector machines

Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines

Reducing neural network training data using support vectors

Denial-of-service (DoS) detection through practical entropy estimation on hierarchical sensor networks

Security Enhancement in Wireless Sensor Networks Using Machine Learning