Proposing a Rank and Wormhole Attack Detection Framework using Machine Learning

Fatima-tuz-Zahra,; Jhanjhi, N. Z.; Brohi, Sarfraz Nawaz; Malik, Nazir Ahmed

doi:10.1109/macs48846.2019.9024821

Cited by 43 publications

(16 citation statements)

References 15 publications

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“…However, in sinkhole attack, a malicious user adds a sinkhole node that is an enticing and optimal route so that the network traffic goes forward. Shukla [111], Zahra et al [112] and Bostani et al [113] have formulated a wormhole threat model in the WSN or RPL network that can generate a terrible result, including network alteration, falsification or node manipulation. However, Napiah et al…”

Section: A Perception Layer Threatsmentioning

confidence: 99%

“…[111], [112] , [113], [114] Wormhole/Sinkhole/ Rank Modify packet routes, flow speed, rank of the nodes.…”

Section: Processing Layer Threatsmentioning

confidence: 99%

“…This paper also introduced distributed two-stage hybrid lightweight ML-IDS by combining K-means clustering and Decision tree that provide 71-75% detection rate but with more accuracy than others. Zahra et al [112] have proposed a wormhole detection framework using a ML approach. The paper only considered Routing protocol attacks by analyzing the security features of RPL and differentiating the valid and invalid nodes in case of wormhole attacks.…”

Section: Aementioning

confidence: 99%

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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resourceconstrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques can not be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms-branches of Artificial Intelligence (AI)-can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AIbased security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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Section: A Perception Layer Threatsmentioning

confidence: 99%

“…[111], [112] , [113], [114] Wormhole/Sinkhole/ Rank Modify packet routes, flow speed, rank of the nodes.…”

Section: Processing Layer Threatsmentioning

confidence: 99%

Section: Aementioning

confidence: 99%

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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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“…Another solution which is currently an active area of research as well is machine learning. Developing security solutions using machine learning techniques [62] is a promising field for development of security techniques to prevent active and passive security attacks.…”

Section: Unique Solution To Prevent Active and Passive Attacksmentioning

confidence: 99%

Active and Passive Security Attacks in Wireless Networks and Prevention Techniques

Ee¹,

Ming²,

Yap³

et al. 2020

Preprint

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In this modern day and age, wireless networks are everywhere. It is not a far stretch to say that it is downright crucial due to its ability to keep people connected and updated everywhere from universities and workplaces down to our personal homes. Though the benefits of wireless networks are undeniable, it also brings a new host of security concerns due to the amount of sensitive data being transmitted through these networks. A simple attack such as packet sniffing is enough to “sniff” out just enough data to cripple even giant corporate bodies if left unchecked. The aim of this research paper is to dissect and discuss the security and privacy issues of wireless networks that surround us. An interesting finding would be that wireless networks are heavily susceptible to both malicious attacks such as denial of service attacks and passive attacks like eavesdropping. It was also noted that information is more easily obtainable through wireless networks as compared to wired networks due to the ability to gleam information from long distances without having to be physically present on site. A literature review and a survey were conducted to learn about the opinions of casual users of wireless networks. The information gathered suggests that while there are many who care about privacy, they still lack the necessary knowledge to protect said privacy. These users carelessly connect to public wireless networks wherever it is available without proper precaution and care. Appropriate relevant solutions are presented based on literature study to overcome insecure wireless network usage practices in order to avoid active and passive attacks.

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“…However, no matter what the issues are, there have been solutions proposed by researchers and they continue to develop them according to the requirements. For example, in [58][59][60] authors have proposed detection of attacks in communication protocols used in internet of things networks, while in [61], RFID technology has been proposed to be used for secure attendance monitoring. In [62] authors have performed comprehensive analysis of privacy protection issues in cloud computing applications which can be used as a basis for development of secure solutions in the context of mobile cloud computing.…”

Section: Solution For Issues and Challengesmentioning

confidence: 99%

Security and Privacy Issues in Wireless Networks and Mitigation Methods

Kamal¹,

Yen²,

Ling³

et al. 2020

Preprint

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The rapid growth of network services, Internet of Things devices and online users on the Internet have led to an increase in the amount of data transmitted daily. As more and more information is stored and transmitted on the Internet, cybercriminals are trying to gain access to the information to achieve their goals, whether it is to sell it on the dark web or for other malicious intent. Through thorough literature study relating to the causes and issues that are brought from the security and privacy segment of wireless networks, it is observed that there are various factors that can cause the networks to be an insecure; especially factors that revolve around cybercriminals with their growing expertise and the lack of preparation and efforts to combat them by relevant bodies. The aim of this paper is to showcase major and frequent security as well as privacy issues in wireless networks along with specialized solutions that can assist the related organizations or the public to fathom how great of an impact these challenges can bring if every related stakeholder took a step in reducing them. Through this paper it is discovered that there are many ways these challenges can be mitigated, however, the lack of implementation of privacy and security solutions is still largely present due to the absence of practical application of these solutions by responsible parties in real world scenarios.

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Proposing a Rank and Wormhole Attack Detection Framework using Machine Learning

Cited by 43 publications

References 15 publications

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Active and Passive Security Attacks in Wireless Networks and Prevention Techniques

Security and Privacy Issues in Wireless Networks and Mitigation Methods

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