The growing utilization of Internet administration in the recent years has facilitated an increment in the various types of attacks. Despite the best protection measures, many attacks have been effectively done against many organization and enterprises. Anomaly detection, misuse detection are used to detect various type of attacks. Anomaly detection produce high rate of false alarms and the later one produce less number of false alarms but it is not much efficient than the previous one. In this paper we proposed a new method by combining both detection method in real time and tried to overcome the drawbacks of existing techniques. © 2015 IEEE.

Usha Devi G

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Balan E.V

Priyan M.K

Gokulnath C

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Hybrid architecture with misuse and anomaly detection techniques for wireless networks

2015 International Conference on Communications and Signal Processing (ICCSP)

Features selection is the process of choosing the relevant subset of features from the high-dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high-dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble-based algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR-FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%. © 2020 Wiley Periodicals, Inc.

Computational Intelligence

Forest optimization algorithm‐based feature selection using classifier ensemble

Increase in body mass index value is a serious health problem across the world. Humans with high body mass index value have the higher probability of getting the cardiovascular disease and high blood pressure. The proposed work is concerned in predicting the probability of CVD and high blood pressure in India. The disease has been predicted with body mass index value; from the health reports of India, the pervasiveness of CVD and HBP is identified. The demographic health survey 2016 of India is used in this work. Bayesian model is used to find the pervasiveness of CVD and HBP based on the gender and the place of living. Out of 432 articles studied, 34 articles suggested the pervasiveness of CVD and HBP. Pervasiveness of CVD increased (95% of interval) rapidly between 1992 and 2016 from 3.0% (0.3–5.7) to 16.4% (9.4–16.2) for men, and from 5.2% (2.3–8.6) to 15.4% (3.3–18.6) for women. Pervasiveness of high blood pressure increased rapidly between 1992 and 2016 from 11.0% (8.6–17.4) to 21.4% (19.4–23.6) for men and from 14.0% (4.3–16.7) to 20.4% (5.6–25.6) for women. The pervasiveness of CVD in 2030 is predicted as 24.6% (13.6–37.8), and pervasiveness of high blood pressure is predicted to be 21.7% (19.6–27.8). The annual average of pervasiveness of CVD is high for women in village areas, and pervasiveness of high blood pressure is found high for men in city regions. The cutoff point for pervasiveness of CVD for overall population is 23.02&nbsp;kg/m2. © 2018, The Natural Computing Applications Forum.

Neural Computing and Applications

Optimal body mass index cutoff point for cardiovascular disease and high blood pressure

Journal of Medical Systems

Vehicular Cloud for Smart Driving Using Internet of Things

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

Detecting Streaming of Twitter Spam Using Hybrid Method

Personal and Ubiquitous Computing

Speech recognition with improved support vector machine using dual classifiers and cross fitness validation

International Journal of Network Security & Its Applications

A Hierarchical Intrusion Detection Architecture for Wireless Sensor Networks

Hierarchical Design Based Intrusion Detection System For Wireless Ad Hoc Sensor Network

IEEE Transactions on Parallel and Distributed Systems

Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

Proceedings of the 6th international conference on Information processing in sensor networks - IPSN '07

Probabilistic detection of mobile targets in heterogeneous sensor networks

Proceedings of the seventh ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '06

Delay of intrusion detection in wireless sensor networks

Journal	Data powered by Typeset2015 International Conference on Communications and Signal Processing (ICCSP)
Publisher	Data powered by TypesetIEEE
Open Access	0