scholarly journals Improved Key Distribution and Management in Wireless Sensor Network

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Vishal Choudhary ◽  
Sunil Taruna
Keyword(s):  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Key Distribution ◽  
Base Station ◽  
Sensor Nodes ◽  
Communication Overhead ◽  
Main Concern ◽  
Set Up

Key set up and distribution in sensor network is a complex task due to inherent limitations in sensor networks. For reducing the “communication and computation overhead” we proposed that the sensor network is partitioned into different zones. Each zone has a separate intrusion detection system (IDS) and key distribution center (KDC).IDS can detect the activity in its area and communicate with its KDC. KDC is controlled by the base station. Our main concern is that how to make a secure link in between KDC to sensor nodes and KDC to the Base station. Here we will try to reduce the computation and communication overhead of the already overloaded base station by separating the intrusion detection work of the base station with a separate entity in each zone.

scholarly journals Data mining approach to analyzing intrusion detection of wireless sensor network

2021 ◽  
Vol 21 (1) ◽  
pp. 516
Author(s):  
Md Alauddin Rezvi ◽  
Sidratul Moontaha ◽  
Khadija Akter Trisha ◽  
Shamse Tasnim Cynthia ◽  
Shamim Ripon
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Detection System ◽  
Denial Of Service ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector

<span>Wireless sensor network (WSN) is a collection of wireless sensor nodes which are distributed in nature and a base station where the dispersed nodes are used to monitor and the physical conditions of the environment is recorded and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect denial of service (DoS) attacks which is of four types. They are Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support vector machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts. </span>

scholarly journals Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1411 ◽  
Author(s):  
Fuad A. Ghaleb ◽  
Faisal Saeed ◽  
Mohammad Al-Sarem ◽  
Bander Ali Saleh Al-rimy ◽  
Wadii Boulila ◽  
...  
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Lower Boundary ◽  
Normal Operation ◽  
Communication Overhead ◽  
On Demand ◽  
Collaborative Intrusion Detection

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

Wireless sensor network intrusion detection system based on MK-ELM

2020 ◽  
Vol 24 (16) ◽  
pp. 12361-12374 ◽  
Author(s):  
Wenjie Zhang ◽  
Dezhi Han ◽  
Kuan-Ching Li ◽  
Francisco Isidro Massetto
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System
Sign in / Sign up

Export Citation Format

Share Document