A Survey on Impersonation Attack in Wireless Networks

One of the key features of a Wireless Sensor Network (WSN) device is its constrained resource availability and its security weakness. As a result, the research of the energy and security issues is crucial because of how much they influence WSN behavior. Traditional security procedures are not the best given the restricted resources of nodes, thus it is necessary to analyze new strategies in order to take this particular node attribute into consideration. In this study, a MAC layer impersonation attack is examined, and the performance of a network of four sensor nodes, a gateway, and a server is assessed using the LMAC and BMAC protocols in terms of the average energy used by each node in the WSN under consideration.

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“…In this section we provide a brief introduction to impersonation attack and a possible mitigation. 18…”

Section: Impersonation Attack and Relative Mitigationmentioning

confidence: 99%

A MAC layer analysis of an impersonation attack in a wireless sensor network

Palmieri

Campoverde

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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“…• Impersonation Attack: The attackers want to impersonate themselves either as a physical node or a unit of implicit node [16]. Such attacks are particularly easy to launch in IoT environments and can root significant harm to system performance [17]. [18].…”

Section: Threat Modelmentioning

confidence: 99%

Malicious Nodes Detection based on Artificial Neural Network in IoT Environments

Khatun

Chowdhury

Uddin

2019

2019 22nd International Conference on Computer and Information Technology (ICCIT)

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The central promise of the Internet of Things (IoT) is to accelerate the interaction with surroundings. Appliances such as smartwatches, smart bulbs, thermostats, fitness trackers, next-generation vehicles, and so on have gained the ability to communicate and accept instructions from outside with the help of various embedded devices. While doing so, they almost always operate with little to no human interaction. These embedded devices, alongside a lot of benefits, also bring forth several security challenges. From the manufacturers' point of view, performance and production remain the top priority leaving security a less addressed problem. This practice has manifested itself in the form of various attacks including the Distributed Denial of Service (DDoS) where malware prepared with the aid of malicious nodes in IoT devices are used to carry out the attack. It is, therefore, essential to comprehend the nature of these attacks and swiftly identify infected devices to combat this situation. Machine Learning, or more particularly a branch of this technique commonly known as Deep Learning, has already demonstrated its outstanding potentials while administering with the heterogeneous data of diverse sizes. Using the Artificial Neural Network (ANN), this work suggests a method of detecting malicious nodes in IoT environments. The contribution of the paper is in twofold. First, it classifies the usual and malicious patterns of IoT devices in the network, and second, describes a scheme for successfully detecting malicious nodes with 77.51% accuracy.

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“…Detecting impersonation attacks within a wireless network is complex; therefore, the advantage is with the attacker due to the simplicity of launching these attacks [ 18 ]. The difficulty in detecting and preventing impersonation attacks originates from the fact that many are based on the abuse of authorized commands within the IEEE 802.11 standard.…”

Section: Introductionmentioning

confidence: 99%

Preventing Attacks on Wireless Networks Using SDN Controlled OODA Loops and Cyber Kill Chains

Zanna

Radcliffe

Kumar

2022

Sensors

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Impersonation-based attacks on wireless networks are easy to perform and can significantly impact network security. Their detection is problematic due to the attacks utilizing legitimate functions. This paper proposes a novel algorithm based on Observe-Orientate-Decide-Act (OODA) loop and Cyber Kill Chain (CKC) strategies to detect and neutralize these attacks. To evaluate this approach, we conducted experiments using four attack methods on a wireless router equivalent device, five wireless client devices, and two attack devices. The system employs a Radio Frequency (RF) device identification system and attack state machine implemented using a Software Defined Networking (SDN) architecture and the P4 programming language. The technique remains compliant with the IEEE 802.11 standard and requires no client-side modifications. The results show that the RF section detected 97.5% (average) of impersonated frames, and the overall method neutralized all attacks in the four attack scenarios. This outcome demonstrates that this technique, built on the OODA loops and CKC methodology, using SDN architecture and P4, is suitable for real-time detection and prevention of wireless impersonation attacks.

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A Survey on Impersonation Attack in Wireless Networks

Abstract: Abstract

Cited by 6 publications

References 8 publications

A MAC layer analysis of an impersonation attack in a wireless sensor network

A MAC layer analysis of an impersonation attack in a wireless sensor network

Malicious Nodes Detection based on Artificial Neural Network in IoT Environments

Preventing Attacks on Wireless Networks Using SDN Controlled OODA Loops and Cyber Kill Chains

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