An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform

Ponmalar, A.; Dhanakoti, V.

doi:10.1016/j.asoc.2021.108295

Cited by 46 publications

(15 citation statements)

References 22 publications

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“…The comparative analysis of the overall performance of the intrusion detection model is described in Table 3. The measures such as MCC, specificity, sensitivity, precision, and F‐score with the Spark‐Chi‐SVM, 14 DNN, 3 ML, 15 LSTM, 17 ensemble support vector machine based Chaos Game Optimization (ESVM‐CGO) 36 and proposed method validate the performance of intrusion detection. Based on this study, the proposed method shows 99.27% MCC, 99.32% specificity, 99.02% sensitivity, 99.34% precision, and 99.27% F‐score results.…”

Section: Resultsmentioning

confidence: 90%

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

Ponmalar

Dhanakoti²

2022

Concurrency and Computation

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The rate of internet traffic in the digital world has expanded fast due to technological improvement. Because of the large number of internet users, there is a large volume of network traffic, and it is the most common and challenging task to evaluate, process, and store the data. The intrusion detection systems enhance the performance of early attacks prediction and malicious attack detection. The exiting techniques met a few challenges in terms of feature selection, computational cost, accuracy, higher dimensionality, computational time and so forth. To solve these issues, we proposed a novel deep learning model for intrusion detection in big data. The data was collected from NSL‐KDD, KDD‐Cup99, and UNSW‐NB15 datasets. Initially, the big data framework with a testbed is set up for big data analytics and processing in which the required information from a large volume is extracted. The datasets are stored in the Hadoop Distributed File System, which reduces the number of latencies in the distributed process. Finally, the intrusion detection from big data is performed using convolutional neural network‐based Hybrid Whale Tabu Optimization algorithm and it effectively classifies whether the data is intrusion is non‐intrusion. The proposed method provided appropriate and superior experimental results using the state‐of‐art method with various performance measures such as accuracy, Matthews correlation coefficient, balanced accuracy, specificity, sensitivity, precision, and F‐measures.

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Section: Resultsmentioning

confidence: 90%

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

Ponmalar

Dhanakoti²

2022

Concurrency and Computation

Self Cite

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show abstract

“…The experimental results on the NSL-KDD benchmark dataset show that this method outperforms the other related approaches in terms of overall accuracy. Ponmalar and Dhanakoti [32] proposed an intrusion detection approach using an ensemble SVM based on the chaos game optimization algorithm in a big data platform. Rashid et al [33] proposed a tree-based stacking ensemble intrusion detection model that integrates decision tree, random forest, and extreme gradient boosting.…”

Section: Related Workmentioning

confidence: 99%

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

et al. 2023

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Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years. The development of effective and efficient intelligent network intrusion detection methods using advanced machine learning algorithms is of great importance for defending against various network intrusions in complex network environments. In this study, a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, named HC-DTTWSVM, is proposed, which can effectively detect different categories of network intrusion. First, the hierarchical clustering algorithm is applied to construct the decision tree for network traffic data, where the bottom-up merging approach is used to maximize the separation of the upper nodes of the decision tree, which reduces the error accumulation in the construction of the decision tree. Then, twin support vector machines are embedded in the constructed decision tree to implement the network intrusion detection model, which can effectively detect the network intrusion category in a top-down manner. The detection performance of the proposed HC-DTTWSVM method is evaluated on NSL-KDD and UNSW-NB15 intrusion detection benchmark datasets. Experimental results show that HC-DTTWSVM can effectively detect different categories of network intrusion and achieves comparable detection performance compared to some of the recently proposed network intrusion detection methods.INDEX TERMS Network intrusion detection, twin support vector machine, hierarchical clustering, decision tree.

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“…There are 41 characteristics in total, 38 of which are numeric, and 3 of which are not (protocol type, service type, and fag). There are also fundamental traffic features (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41), content features (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), features (1-10), and a class label for each item.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Several researchers have suggested using AI in the smart healthcare system as a reliable and practical security solution [14]. Many of the relevant studies in the literature have developed computerized simulations for network intrusion recognition using machine learning techniques, such as K-nearest neighbor (KNN) [15], Naïve Bayes [16], Support Vector Machine (SVM) [17], Random Forest (RF) [18], etc.…”

Section: Introductionmentioning

confidence: 99%

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

Ali,

Alqurashi,

Alsolami

et al. 2023

Mathematics

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The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

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An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform

Cited by 46 publications

References 22 publications

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

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