Distributed Intrusion Detection System for Wireless Sensor Networks

Medhat, Karen; Ramadan, Rabie A.; Talkhan, Ihab

doi:10.1109/ngmast.2015.29

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…The output of the anomaly and misuse modules are integrated with a decision-making module to identify the presence of an attack before subsequently classifying into different attack type. In [14], a distributed two-layer and threelayer IDS scheme was proposed for WSN to detect intrusion using 10% of the data to learn during the training phase. A complexity reduction process was introduced to select the features to minimize the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

et al. 2019

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Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment. Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period.

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Section: Related Workmentioning

confidence: 99%

Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

et al. 2019

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“…A Wireless Sensor Network consists of a large number of sensor nodes that communicate wirelessly. Healthcare, sports training, workplace safety, consumer electronics, secure authentication, and protection of uniformed personnel are just a few of the applications of WSN, meaning it is ubiquitous and has broad market potential 1 . In a wireless sensor network, data aggregation reduces packet transmissions and enhances network duration, as sensor data is collected and delivered to the base station by aggregation nodes.…”

Section: Introductionmentioning

confidence: 99%

Improving Wireless Sensor Network Security Using Quantum Key Distribution

Alhasnawy

AL-Mashanji

2023

Baghdad Sci.J

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Wireless Sensor Networks (WSNs) are promoting the spread of the Internet for devices in all areas of life, which makes it is a promising technology in the future. In the coming days, as attack technologies become more improved, security will have an important role in WSN. Currently, quantum computers pose a significant risk to current encryption technologies that work in tandem with intrusion detection systems because it is difficult to implement quantum properties on sensors due to the resource limitations. In this paper, quantum computing is used to develop a future-proof, robust, lightweight and resource-conscious approach to sensor networks. Great emphasis is placed on the concepts of using the BB84 protocol with the AES algorithm in WSN security. The results of analysis indicated a high level of security between the data by depending on the generation of secure keys, and reached an accuracy rate of about (80-95) % based on using NIST statistical. The efficiency of the work increased to 0.704 after using the Quantum Bit Error Rate equation, eventually increasing the network performance. This results in the reduction of the overall amount of energy, and the time required for performing the key exchange in the encryption and decryption processes decreased.

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Efficient monitoring for intrusion detection in wireless sensor networks

Abdellatif

Mosbah

2018

Concurrency and Computation

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SummaryIn wireless sensor networks (WSN), Intrusion Detection through sensor monitoring enables the detection of intricate security attacks such as denial of service and routing attacks. However, the monitoring process, which observes sensor behaviour, can induce a large amount of energy and bandwidth overhead, thus reducing the system lifetime. Furthermore, monitors can be attacked and their observations corrupted, which breaks the system security. Therefore, our goal is the design of a reliable and energy‐efficient monitoring system that resists internal security attacks. We present DAMS (Distributed and Adaptable Monitoring System) that extends LEACH protocol with a trust‐based and energy‐efficient clustering protocols for both cluster heads and monitors election. Compared to other LEACH extensions, the obtained topology reduces monitoring energy overhead, improves the system lifetime, and increases the packet delivery ratio.

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Distributed Intrusion Detection System for Wireless Sensor Networks

Cited by 9 publications

References 13 publications

Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

Improving Wireless Sensor Network Security Using Quantum Key Distribution

Efficient monitoring for intrusion detection in wireless sensor networks

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