A New Method for Intrusion Detection on RPL Routing Protocol Using Fuzzy Logic

Farzaneh, Behnam; Koosha, Mohammad; Boochanpour, Elahe; Alizadeh, Emad

doi:10.1109/icwr49608.2020.9122278

Cited by 23 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such instances, a local repair attack may be carried out by a malicious node that intermittently activates all of its nearest neighbors. However, there is no issue with dropping valid packets, generating control overhead, raising the delay for packet [117].…”

Section: A Perception Layer Threatsmentioning

confidence: 99%

“…Spoo�ing attack [100,102] DDoS [155,156] Wormhole attack [115] Sybil attack [115] �looding attack [122] Phishing [143] Sybil attack [115] Local repair attack [117] Flooding attack [161] Fog [104] introduced an SVMbased authentication scheme to protect medical devices from resource depletion. The simulation result showed that the detection accuracy of the SVM classifier exceeds 90%.…”

Section: Classi�icationmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: A Perception Layer Threatsmentioning

confidence: 99%

Section: Classi�icationmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…FNR: The rate of the malevolent node to total normal nodes incorrectly signed as a normal node. [21][22][23][24][25] The calculation is proved by Equation 12. PDR: This criterion represents the rate of packets that were successfully delivered to the destination.…”

Section: Dr = Tpr Tpr + Fnrmentioning

confidence: 99%

“…

italicDR = ((), \frac{italicTPR}{TPR + FNR}) * 100 where All = italicTPR + italicTNR + italicFPR + italicFNR

FPR: The FPR is determined by the total number of nodes mistakenly found as the malevolent nodes divided by the total number of usual nodes 18–20 . Hence, Equation illustrates the

italicFPR = ((), \frac{italicFPR}{FPR + TNR}) * 100, where italicTNR = ((), \frac{italicTNR}{TNR + FPR}) * 100

FNR: The rate of the malevolent node to total normal nodes incorrectly signed as a normal node 21–25 . The calculation is proved by Equation .…”

Section: Performance Evaluationmentioning

confidence: 99%

A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem

Zaminkar¹,

Sarkohaki

Fotohi

2020

Int J Communication

View full text Add to dashboard Cite

Summary Internet of Things (IoT) provides the possibility for milliards of devices throughout the world to communicate with each other, and data are collected autonomously. The big data generated by the devices should be managed securely. Due to security challenges, like malicious nodes, many approaches cannot respond to these concerns. In this paper, a robust hybrid method, including encryption, is used as an efficient approach for resolving the Low‐Power and Lossy Networks (RPL) protocol concerns so that the devices are connected securely. Therefore, the proposed Detection of sinkholes in RPL (DSH‐RPL) method for securing the RPL protocol comprises the four following phases: the first phase creates a reliable RPL. The second phase detects the sinkhole attack. The third phase quarantines the detected malicious node, and the fourth phase transmits data through encryption. The simulation results show that the DSH‐RPL reduces the false‐positive rate more than 18.2% and 23.1% and reduces the false‐negative rate more than 16.1% and 22.78% and it also increases the packet delivery rate more than 19.68% and 25.32% and increases the detection rate more than 26% and 31% compared to SecTrust‐RPL and IBOOS‐RPL.

show abstract

“…In the cooperative Black-hole attack, there are many malicious nodes that work together to influence network performance [14]. Intrusion Detection and Prevention System can be used for monitoring network operations and detect intruders [15,16].…”

Section: Black-hole Attackmentioning

confidence: 99%

A distributed trust mechanism for malicious behaviors in VANETs

Ahmed

Abdulwahed

Farzaneh

2020

IJEECS

Self Cite

View full text Add to dashboard Cite

<p>Vehicular Ad-hoc Networks (VANETs) are one of the most important types of networks which are widely used in recent years. Along with all the benefits of Quality of Service (QoS) improvements, vulnerability analysis for this type of networks is an important issue. For instance, a Gray-hole attack decreases network performance. We proposed a novel solution to help to secure these networks against this vulnerability. The proposed method can detect and prevent the Gray-hole attack. Anywhere in the network, each node (vehicle) can distinguish between the Gray-hole attack and the failed link. Some topology related information helps us to detect attacks more accurately. Also, the proposed method uses the most reliable path in terms of link failure when there is no malicious node. In this paper, we used the TOPSIS method for choosing the most trusted node for routing intelligently. We validated our proposal using a simulation model in the NS-2 simulator. Simulation results show that the proposed method can prevent Gray-hole attack efficiently with low overhead.</p>

show abstract

A New Method for Intrusion Detection on RPL Routing Protocol Using Fuzzy Logic

Cited by 23 publications

References 21 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

A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem

A distributed trust mechanism for malicious behaviors in VANETs

Contact Info

Product

Resources

About