Node Attribute Behavior Based Intrusion Detection in Sensor Networks (original) (raw)
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Wireless Node Behavior Based Intrusion Detection Using Genetic Algorithm
2007
Ad-hoc networks are facing increased number of security threats in recent years. Despite numerous technological advancements in wireless network security, it is still very difficult to protect the wireless ad-hoc networks because of lack of centralized traffic concentration, thus requiring monitoring the behavior of individual wireless nodes. This paper presents a behavior-based wireless network intrusion detection using genetic algorithm which assumes misbehavior identification by observing a deviation from normal or expected behavior of wireless node’s event sequence. The features set are constructed from MAC layer to profile the normal behavior of wireless node. If any deviations from the normal behavior pattern of wireless node can be used to detect the intrusions in the wireless ad-hoc network. The wireless node behavior is learnt by using genetic algorithm. Current wireless node behavior can be predicted by genetic algorithm based on the past behavior. A 3-tuple value is calcu...
A Survey on Intrusion Detection in Wireless Sensor Networks
In recent years, the applications based on the Wireless Sensor Networks are growing very fast. The application areas include agriculture, healthcare, military, hospitality management, mobiles and many others. So these networks are very important for us and the security of the network from the various attacks is also a more important issue in WSN application now days. Stopping these attacks or enhancing the security of the WSN system various intrusion detection policies are developed till date to detect the node/s that is/are not working normally. Out of various detection techniques three major categories explored in this paper are Anomaly detection, Misuse detection and Specification-based detection. Here in this review paper various attacks on Wireless Sensor Networks and existing Intrusion detection techniques are discussed to detect the compromised node/s. The paper also provides a brief discussion about the characteristics of the Wireless Sensor Networks and the classification of attacks.
Intrusion Detection System in Wireless Sensor Networks: A Review
International Journal of Advanced Computer Science and Applications, 2015
The security of wireless sensor networks is a topic that has been studied extensively in the literature. The intrusion detection system is used to detect various attacks occurring on sensor nodes of Wireless Sensor Networks that are placed in various hostile environments. As many innovative and efficient models have emerged in the last decade in this area, we mainly focus our work on Intrusion detection Systems. This paper reviews various intrusion detection systems which can be broadly classified based on certain traditional techniques, namely signature based, anomaly based and hybrid based. The models proposed by various researchers have been critically examined based on certain classification parameters, such as detection rate, false alarm, algorithms used, etc. This work contains a summarization study of various intrusion detection systems used particularly in Wireless Sensor Networks, and also highlights their distinct features.
Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks
International Journal of Distributed Sensor Networks, 2013
Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor network (WSN).
Optical Memory and Neural Networks, 2020
From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition and many more. WSN node exposes itself to various security related attacks due to low battery power supply, low bandwidth support, data transmission over multi hop node, dependency on intermediate or other nodes, distributed in nature and self-organization. The WSN attacks observe in all layers of OSI model. Wireless sensor nodes has various issues because of that, it experiences number problem related to its functionalities and some malfunction due to attacks. It is require to build defence and network monitoring system for identifying attacks and prevent them. Intrusion detection system (IDS) plays an important role to detect threads inside the system and generate the alert related to the attack. In this work, supervised classification models for intrusion detection are built using such as Random Forest classifier, Support Vector Machine, Decision Tree Classifier, LGBM Classifier, Extra Tree Classifier, Gradient Boosting Classifier, Ada Boost Classifier, K Nearest Neighbour Classifier, MLP Classifier, Gaussian Naive Bayes Classifier and Logistic Regression Classifier. The NSLKDD, i.e. Modified version of the KDD99 Data Set on which we checks these algorithms. Experimental results how the highest accuracy relative to other classification systems in the support vector machine.
Intrusion Detection System to Detect True Malicious Nodes in Wireless Sensor Network
International Journal of Advance Engineering and Research Development, 2015
A WSN is a network containing of few sensor nodes with sensing, wireless communications and computing skills. These sensor nodes are distributed and arranged in uncontrollable environment for the collection of securitysensitive information. The security of such type of networks is a big concern, especially for the applications where confidentiality has major importance. Therefore, in order to operate WSNs in a secure way, any kind of in trusions should be identified before attackers can harm the network. Intrusion detection has been one of the foremost areas of research in WSN. Hence, it is crucial to develop an efficient Intrusion Detection System which detects true malicious nodes.so we are trying to develop a new Intrusion detection system which Detect true malicious nodes from Wireless Sensor network.
Genetic Algorithm for Intrusion Detection in Wireless Sensor Networks
International Journal of Communication Technology for Social Networking Services, 2013
For protection of networks from unauthorized entries and hackers, intrusion detection system brings upgraded monitoring which enhances integrity and efficiency of the system. Proficient characteristics of sensors like open wireless medium, multi-hop data forwarding along with distributed nature makes wireless sensor networks a promising technology whose applications ranges from prudent military to health care. For coherent and secure operations of wireless sensor networks, intrusions must be detected and removed very efficiently before they cause harm. Deployment of wireless sensor networks with their resource restrictions makes its security highly demanding. Genetic algorithms animated by natural mechanisms acts as an immune system for classification and detection of intrusions. Clustering is contemplated as a method to increase the overall network lifetime of wireless sensor networks. This article provides a vision of approach assimilating genetic algorithm with clustering for intrusion detection and enhancing network lifetime of wireless sensor networks. Also, some energy efficient techniques for routing in wireless sensor networks are compared with genetic algorithm approaches.
2024
Background and Objectives: Wireless sensor networks (WSNs) are ad-hoc technologies that have various applications in different industries such as in healthcare systems, environment and military surveillance, manufacturing, and IoT context in general. Expanding the scope of sensor network applications has led researchers to develop solutions to provide sustainable communications and networks for distributed environments, as well as how to secure these methods with limited resources. Methods: The lack of infrastructure space and the vulnerable nature of these networks make it difficult to design security models and algorithms for them. So, to run the sensor network in safe mode, any type of attack must be detected before any security breach is materialized. According to the importance of the network and also the nature of the sensor networks along with the critical challenge of energy consumption, solutions and defensive lines such as intrusion prevention and intrusion detection systems will be selected. Results: This paper surveys subjectively the intrusion and anomaly detection system in WSNs to determine potentials and challenges for further processing. Therefore, designing an efficient and optimal intrusion detection solution applicable to wireless sensor networks, IoT, and other ad-hoc networks has been a major challenge that will help the researcher to design or choose the best approach for their future research. Conclusion: This research also paves the way of interested researchers to find existing challenges and shortcomings for further processing.
Anomaly detection in wireless sensor networks
2016
During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.