Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey

Sinha, Preeti; Jha, Vijay Kumar; Kumar, Amit; Bhushan, Bharat

doi:10.1109/cspc.2017.8305855

Cited by 89 publications

(37 citation statements)

References 21 publications

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“…Random forest is a predictor that collects the information from each tree { 9 ( , 3 , 9 , ≥ 1)}, where + , Z … . 3 are the output of each random trees. These trees are combined to form the aggregated estimation to train the forest using: 9 d ( , 9 ) = g [ 9 ( , , 9 )] (7) where is the expectation with respect to the random parameter, conditionally, on and the data set 9 .…”

Section: ) Random Forestmentioning

confidence: 99%

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A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

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Section: ) Random Forestmentioning

confidence: 99%

“…In the classification situation where the dataset is divided into several classes based on the input parameters threshold values, the random forest classifier is obtained via a majority vote among the classification trees, that is v,9 ‡ ; + … . 3…”

Section: ) Random Forestmentioning

confidence: 99%

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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“…In order for a node to retransmit, it spends its energy over and over again for the very same packet, and this consumption eventually results in the reduction of energy. Normally, one byte is enough for making a CRC error in addition to disabling the data packet [16].…”

Section: Attacks On S-macmentioning

confidence: 99%

“…The first node tends to attain the medium, but the illicit nodes also get the benefit of this method and transmit the data packet with a low waiting time duration. It repeatedly transmits such packets to attain the medium and, hence, causes a hindrance for the legitimate nodes to have maximum access to the medium [16].…”

Section: B Unfairness Attackmentioning

confidence: 99%

Intruder Attacks on Wireless Sensor Networks: A Soft Decision and Prevention Mechanism

Hussain¹,

Zahra²,

Hussain³

et al. 2019

IJACSA

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Because of the wide-ranging of applications in a variety of fields, such as medicine, environmental studies, robotics, warfare and security, and so forth, the research on wireless sensor networks (WSNs) has attracted much attention recently. WSNs offer economical, flexible, scalable and pragmatic solutions in many situations. Sensor nodes are tiny and have a limited, non-rechargeable battery source, small memory/computational abilities and low transmitter power. Energy resources are vital as once the battery is depleted, the node is no longer usable. Multiple medium access control (MAC) protocols are designed to increase the life cycle of a node by minimizing its unnecessary energy consumption. In some critical applications like the surveillance of enemy movements on a battlefield, opponents deploy adversary nodes to disturb the performance of WSNs by mainly depleting the battery sources of legitimate nodes. In this work, an intrusion detection mechanism has been adapted to detect different kinds of intruders' attacks in MAC protocols of WSN's. A soft decision mechanism has been implemented to detect collision and exhaustion attacks. A preventative mechanism has also been introduced, which helps a node to avoid these intrusive attacks. Results show how the lifetime of a node increases and network performance also increases with better throughput and reduced delay.

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“…The classification of attacks on wireless sensor networks in the direction of impact is given in [24,25,26].…”

Section: Honeywell Experionmentioning

confidence: 99%

Intrusion detection system on the basis of data mining algorithms in the industrial network

Gurin¹,

Vulfin²,

Vasilyev³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The purpose of the work is to increase the security of the industrial network of an automated process control system based on intelligent network traffic analysis algorithms. The analysis of the problem of detecting and recording actions of violators on the implementation of a network attack on an automated process control system in the industrial network of an enterprise has been performed. A structural and functional model of the monitoring system of the industrial network of industrial control systems is proposed. An algorithm is developed for the intellectual analysis of network traffic of industrial protocols and a software package that implements the proposed algorithms as part of a monitoring system to evaluate the effectiveness of the proposed solution on field data. Data Science M A Gurin, A M Vulfin, V I Vasilyev and A V Nikonov V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 554 IPS/IDS WEB services DMZ ERP/MES level DB and data collection DMZ Department network Department network AD File Server HMI SCADA/ DCS

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Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey

Cited by 89 publications

References 21 publications

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Intruder Attacks on Wireless Sensor Networks: A Soft Decision and Prevention Mechanism

Intrusion detection system on the basis of data mining algorithms in the industrial network

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