Intrusion detection in network flows based on an optimized clustering criterion

Karimpour, Jaber; Lotfi, Shahriar; Siahmarzkooh, Aliakbar Tajari

doi:10.3906/elk-1601-105

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…In [36], the authors proposed a method based on graph-clustering. The criterion for detection is based on the internal and external weights of clusters.…”

Section: Statistical Basedmentioning

confidence: 99%

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Kushwah¹,

Ranga²

2021

Turk J Elec Eng & Comp Sci

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One of the major security challenges in cloud computing is Distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending a large number of requests. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed which is a combination of extreme learning machine (ELM) model and blackhole optimization algorithm. The proposed technique is used to implement a DDoS attack detection system for cloud computing. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the performance of our proposed technique. It achieves an accuracy of 99.23%, 92.19%, 99.50%, 99.80% with NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, respectively. The performance comparison with other techniques based on ELM, ANN trained with blackhole optimization, backpropagation ANN, and other state-of-the-art techniques is also performed.

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“…In [36], the authors proposed a method based on graph-clustering. The criterion for detection is based on the internal and external weights of clusters.…”

Section: Statistical Basedmentioning

confidence: 99%

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Kushwah¹,

Ranga²

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Various unsupervised learning algorithms have been applied, however, most of them have huge memory requirements [2,11]. Clustering has been used [12][13][14], but incorrect grouping would lead to higher risk of false negatives. As a result, dimensionality reduction becomes a crucial part of the process and thus the majority of approaches have focused on the use of PCA for this task [3,15].…”

Section: Imentioning

confidence: 99%

Enhanced-PCA based Dimensionality Reduction and Feature Selection for Real-Time Network Threat Detection

Mishra

2020

Eng. Technol. Appl. Sci. Res.

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With the rise of the data amount being collected and exchanged over networks, the threat of cyber-attacks has also increased significantly. Timely and accurate detection of any intrusion activity in networks has become a crucial task in order to safeguard data and other valuable assets. While manual moderation and programmed logic have been used for this purpose, the use of machine learning algorithms for superior pattern mapping is desired. The system logs in a network tend to include many parameters, and not all of them provide indications of an impending network threat. The selection of the right features is thus important for achieving better results. There is a need for accurate mapping of high dimension features to low dimension intermediate representations while retaining crucial information. In this paper, an approach for feature reduction and selection when working on the task of network threat detection is proposed. This approach modifies the traditional Principal Component Analysis (PCA) algorithm by working on its shortcomings and by minimizing the false detection rates. Specifically, work has been done upon the calculation of symmetric uncertainty and subsequent sorting of features. The performance of the proposed approach is evaluated on four standard-sized datasets that are collected using the Microsoft SYSMON real-time log collection tool. The proposed method is found to be better than the standard PCA and FAST methods for data reduction. The proposed approach makes a strong case as a dimensionality reduction and feature selection technique for minimizing false detection rates when operating on real-time data.

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“…[31] [21] The piecewise linear neural branch predictor It provides much greater precision but dramatically increases the overhead of control pointing and recovery and the number of adders.…”

Section: Related Workmentioning

confidence: 99%

An astute LVQ approach using neural network for the prediction of conditional branches in pipeline processor

Nain

Chaudhary

2018

ICST Transactions on Scalable Information Systems

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Nowadays, microprocessors use the deep pipeline to execute multiple instructions per cycle. The frequency and behavior of conditional instructions mainly affect the performance of instruction-level parallelism. However, recent processors still have problems with the correct prediction of conditional branches. Firstly, the perceptron neural network and global-based perceptron prediction has been exploited and implemented. Further, a new approach, linear vector quantization (LVQ) neural network, is explored and implemented to see its possibility and potentiality as a branch predictor in terms of accuracy rate. Simulation is performed by varying the parameter of hardware budget and the length of history register using different trace files for identification of the best branch predictor technique. The proposed LVQ perceptron branch predictor achieves an 85.56% accuracy rate using a hardware budget and an 86.36% accuracy rate in terms of history length by comparing the simulation results.

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Intrusion detection in network flows based on an optimized clustering criterion

Cited by 12 publications

References 35 publications

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Enhanced-PCA based Dimensionality Reduction and Feature Selection for Real-Time Network Threat Detection

An astute LVQ approach using neural network for the prediction of conditional branches in pipeline processor

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