Flow-Based Approach to Detect Abnormal Behavior in Neighbor Discovery Protocol (NDP)

Bahashwan, Abdullah Ahmed; Anbar, Mohammed; Hasbullah, Iznan H.; Alashhab, Ziyad R.; Bin-Salem, Ali Abdulqader

doi:10.1109/access.2021.3066630

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…In this paper, the effectiveness of the proposed system was evaluated using performance metrics such as detection accuracy rate, shown in Eq. ( 6) [33][34].…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study for SDN Security Based on Machine Learning

Alheeti

Alzahrani

Alamri

et al. 2023

Int. J. Interact. Mob. Technol.

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In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.

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“…In this paper, the effectiveness of the proposed system was evaluated using performance metrics such as detection accuracy rate, shown in Eq. ( 6) [33][34].…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study for SDN Security Based on Machine Learning

Alheeti

Alzahrani

Alamri

et al. 2023

Int. J. Interact. Mob. Technol.

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show abstract

“…Compared with the traditional ordinary average distance algorithm, the above decision method adds each type of sample point and set the idea of "holistic decision-making." Researchers are proposing various protocol schemes [24][25][26][27][28] to maintain integrity, confidentiality, and security of the information shared among users and servers.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Denial‐of‐Service Attack Detection over IPv6 Network Based on KNN Algorithm

Alharbi

Alferaidi

Yadav

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid increase and complexity of IPv6 network traffic, the traditional intrusion detection system Snort detects DoS attacks based on specific rules, which reduces the detection performance of IDS. To solve the DoS intrusion detection problem in the IPv6 network environment, the lightweight KNN optimization algorithm in machine learning is adopted. First, the double dimensionality reduction of features is achieved through the information gain rate, and discrete features with more subfeatures are selected and aggregated to further dimensionality reduction and feature dimension of the actual operation. Secondly, the information gain rate is used as the weight to optimize the sample Euclidean distance measurement. Based on the proposed measure of the reverse distance influence, the classification decision algorithm of the KNN algorithm is optimized to make the detection technology better. The effect is further improved. The experimental results show that the traditional TAD-KNN algorithm based on average distance and the GR-KNN algorithm that only optimizes the distance definition, the GR-AD-KNN algorithm can not only improve the overall detection performance in the detection of IPv6 network traffic characteristics but also for small groups of samples. As a result, classification has better detection results.

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“…5, October 2023: 5617-5631 5618 inherently secure. As a result, all IPv6 nodes in any link-local network are vulnerable to ICMPv6-based distributed denial of service (DDoS) attacks and exposed to spoofed ICMPv6 packets [2].…”

Section: Introductionmentioning

confidence: 99%

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

Kabla

Anbar

Hamouda³

et al. 2023

IJECE

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<span lang="EN-US">The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS.</span>

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Flow-Based Approach to Detect Abnormal Behavior in Neighbor Discovery Protocol (NDP)

Cited by 13 publications

References 52 publications

A Comparative Study for SDN Security Based on Machine Learning

A Comparative Study for SDN Security Based on Machine Learning

Denial‐of‐Service Attack Detection over IPv6 Network Based on KNN Algorithm

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

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