Enhanced Simulating Annealing and SVM for Intrusion Detection System in Wireless Sensor Networks

2021 ◽  
Author(s):  
D Prabakar ◽  
s Gomathi ◽  
S Sasikala ◽  
TR Saravanan ◽  
s Ramesh
Keyword(s):  
Simulated Annealing ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Secure Routing ◽  
Wireless Sensor ◽  
Support Vector ◽  
Features Selection ◽  
Detection Scheme ◽  
Simulating Annealing ◽  
Performance Results

Abstract Wireless Sensor Networking (WSN) is among the most recent technologies with uses ranging from medicine to the military. Nevertheless, WSNs are impervious to numerous types of cyber-attacks that could compromise the performance of the entire network, which could lead to fatal problems such as a routing attacks, denial-of-service attack, probe, etc. Key management protocols, secure routing, and authentication protocols cannot offer WSN protections for such kinds of attacks. The intrusion detection scheme is the way to solve the issue. This paper proposes an Enhanced simulated annealing based support vector machine algorithm for intrusion detection. Traditional features selection algorithm simulating annealing takes much time to run. So, to avoid this problem, we have introduced Enhanced simulated annealing. From the performance results, it can be seen that our proposed feature selection method provides better performance results than the existing method.

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 Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

scholarly journals Anomaly Detection in Wireless Sensor Networks: A Proposed Framework

2020 ◽  
Vol 14 (10) ◽  
pp. 150
Author(s):  
Dina M. Ibrahim ◽  
Nada M. Alruhaily
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Research Problem ◽  
Sensor Nodes ◽  
Base Stations ◽  
Sensor Data ◽  
Wireless Sensor

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>

Weighted K Nearest Neighbour-Based Cooperation Intrusion Detection System for Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 2972-2978
Author(s):  
Ju Long Pan ◽  
Ling Long Hu ◽  
Wen Jin Li ◽  
Hui Cui ◽  
Zi Yin Li
Keyword(s):  
Wireless Sensor Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nearest Neighbour ◽  
Malicious Nodes ◽  
Detection Scheme

To identify the malicious nodes timely in wireless sensor networks(WSNs), a cooperation intrusion detection scheme based on weighted k Nearest Neighbour(kNN) is proposed. Given a few types of sensor nodes, the test model extracts the properties of sensor nodes related with the known types of malicious nodes, and establishes sample spaces of all sensor nodes which participate in network activities. According to the known node’s attributes sampled, the unknown type sensor nodes are classified based on weighted kNN. Considering of energy consumption, an intrusion detection system selection algorithm is joined in the sink node. Simulation results show that the scheme has a lower false detection rate and a higher detection rate at the same time, and it can preserve energy of detection nodes compared with an existing intrusion detection scheme.

scholarly journals PDTB-IDS: A Parameter and Distributed Trust Based Intrusion Detection System for Wireless Sensor Network

2019 ◽  
Vol 8 (4) ◽  
pp. 11730-11737
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Trust Model ◽  
Secure Routing ◽  
Wireless Sensor ◽  
General Trust

Wireless sensor network (WSN) is a noteworthy division in present day correspondence frameworks and faith detecting steering convention is utilized to improve security in WSN. Already, Trust Sensing based Secure Routing Mechanism (TSSRM) was projected which will diminish the overhead steering and improve the unwavering quality of information transmission over the system. In any case, the security tool of this technique might be invalid, if the system steering convention is modified. Hence, in this work, a Parameter and Distributed Trust Based Intrusion Detection System (PDTB-IDS) with a safe correspondence structure with a trust the board framework for remote sensor systems are proposed. The significant commitment is to distinguish different parameters and trust factors that impact trust in WSN is conveyed among different factors, for example, vitality, unwavering quality, information, and so on. Subsequently coordinate believe, proposal believe and circuit trust from those components are determined and the general trust estimation of the sensor hub is evaluated by joining the individual trust esteems. The trust model can decide whether or not the specific hub is pernicious or not by looking at trust got from the proposed method. The numerical assessment of the research work is completed with the help of NS2 simulation environment from which it is proved that the projected strategy provides enhanced outcome than the present TSSRM method.

scholarly journals Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6106
Author(s):  
Abdelouahid Derhab ◽  
Abdelghani Bouras ◽  
Mohamed Belaoued ◽  
Leandros Maglaras ◽  
Farrukh Aslam Khan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Wireless Sensor ◽  
Reporting Scheme ◽  
Detection Scheme ◽  
One Class Classifier ◽  
And Performance ◽  
Flow Conservation

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

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