Reliable Behavioral Dataset for IPv6 Neighbor Discovery Protocol Investigation

Najjar, Firas; Kadhum, Mohammed M.

doi:10.1109/icitcs.2015.7293014

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…Flooding attack : In this attack, the attacker floods the network with fake NS messages, which can cause drones to become unresponsive or even crash. This can disrupt communication on the network and prevent legitimate drones from connecting 44 …”

Section: Related Workmentioning

confidence: 99%

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IPv6 addressing with hidden duplicate address detection to mitigate denial of service attacks in the internet of drone

Kumar,

Pragya

2024

Concurrency and Computation

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SummaryThe fusion of drones with the Internet, termed the Internet of Drones (IoD), resembles the Internet of Things (IoT) paradigm. In IoD, drones necessitate unique identifiers similar to IPv6 addresses. This paper presents a novel approach for securing the duplicate address detection (DAD) process in the IoD environment. This paper focuses on addressing the security challenges posed by malicious drones in the DAD process. The proposed solution introduces a hidden DAD (H‐DAD) process, to mitigate attacks on the DAD process. In this scheme, drones transmit a transformed portion of their interface identifier (IID) while concealing the full IID, thus thwarting potential disruptions by malicious entities. We evaluated the effectiveness of the H‐DAD scheme through experimental analysis using Python with SimPy, comparing it with the Improved DAD, Secure DAD, and Standard DAD schemes. Our results demonstrate a notable improvement, with the Hidden DAD scheme achieving a 100% address success rate under attack. Moreover, it exhibits approximately 5% and 12% lower overhead and energy consumption, respectively, compared to the other schemes.

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Section: Related Workmentioning

confidence: 99%

“…Table 1 summarizes key schemes that secure the DAD process from various DoS attacks. Cryptographic methods 25,42–45,47,50,51,53,56,59 aim to hide the details of the DAD process to make it harder for adversary nodes to attack it. Still, they involve complex cryptographic techniques unsuitable for resource‐constrained environments.…”

Section: Related Workmentioning

confidence: 99%

IPv6 addressing with hidden duplicate address detection to mitigate denial of service attacks in the internet of drone

Kumar,

Pragya

2024

Concurrency and Computation

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“…Dataset presented in [30] is selected in testing and evaluations as it successfully captures the normal behavioral of NDP, and capture RA and NS flooding attacks which make it good selection choice. Table 1 summarizes the summation of captured packets inside the dataset.…”

Section: Datasetmentioning

confidence: 99%

Detecting Neighbor Discovery Protocol-Based Flooding Attack Using Machine Learning Techniques

Najjar

Kadhum

El-Taj

2016

Lecture Notes in Electrical Engineering

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Neighbor Discovery Protocol (NDP) is stateless and lacks of authentication which exposes it to flooding attacks. Securing NDP is critical due to the large deployment of open network. Commonly existing solutions for securing NDP violate its design principle in terms of overhead and complexity. Other solutions suffer from high false positive alerts which affects solution trustiness. This paper aims to investigate the use of machine learning mechanism for detecting NDP flooding attacks. It was found that the advantage of using machine learning is that the detection can be done without relying on attack signatures they can learn broader definitions of attack attributes.

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“…Hence, the aggregated traffic's correctness cannot be determined. As indicated in [24,25], "datasets in the intrusion detection domain have been widely criticized for their accuracy and capacity to reflect real-world settings. "…”

Section: Introductionmentioning

confidence: 99%

Labelled Dataset on Distributed Denial-of-Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6)

Manickam

AIghuraibawi

Abdullah

et al. 2022

Wireless Communications and Mobile Computing

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The most dangerous attack against IPv6 networks today is a distributed denial-of-service (DDoS) attack using Internet Control Message Protocol version 6 (ICMPv6) messages. Many ICMPv6-DDoS attack detection mechanisms rely on self-created datasets because very few suitable ICMPv6-DDoS attack datasets are publicly available due to privacy and security concerns. When implemented in a real network, however, a detection system that relies on a dataset with incorrect packet or flow representation and contains unqualified features generates a large number of false alerts. The goal of this work is to create a comprehensive ICMPv6-DDoS attack dataset that can be used for tuning, benchmarking, and evaluating any detection systems designed to detect ICMPv6-DDoS attacks. The proposed datasets met the criteria for a good dataset, ensuring their usefulness to other researchers. A GNS3 network simulation tool is used to simulate an IPv6 network and generate ICMPv6 traffic for the dataset. The generated traffic contains both normal and abnormal ICMPv6 traffic, with the abnormal traffic containing ten different ICMPv6-DDoS attacks based on RA and NS message flooding. Five classifiers were chosen, varying in terms of type, classification performance, and the number of features used, and the results were as follows: decision tree 80%, support vector machine 78%, naïve Bayes 80%, k -nearest neighbours 81%, and neural networks 81%. The proposed dataset has been shown to accurately represent attack traffic in tests, with a high detection accuracy and a low false-positive rate.

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Reliable Behavioral Dataset for IPv6 Neighbor Discovery Protocol Investigation

Cited by 10 publications

References 18 publications

IPv6 addressing with hidden duplicate address detection to mitigate denial of service attacks in the internet of drone

IPv6 addressing with hidden duplicate address detection to mitigate denial of service attacks in the internet of drone

Detecting Neighbor Discovery Protocol-Based Flooding Attack Using Machine Learning Techniques

Labelled Dataset on Distributed Denial-of-Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6)

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