Efficient Intrusion Detection System in the Cloud Using Fusion Feature Selection Approaches and an Ensemble Classifier

Bakro, Mhamad; Kumar, Rakesh Ranjan; Alabrah, Amerah; Ashraf, Zubair; Bisoy, Sukant Kishoro; Parveen, Nikhat; Khawatmi, Souheil; Abdelsalam, Ahmed K.

doi:10.3390/electronics12112427

Cited by 25 publications

(5 citation statements)

References 91 publications

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“…This highlights the significant contribution of deep learning in addressing cybersecurity challenges in Industry 5.0 environments. This paper [8] has proposed a cloud intrusion detection system (IDS) that is focused on boosting the classification accuracy by improving feature selection and weighing the ensemble model with the crow search algorithm (CSA). The feature selection is handled by combining both filter and automated models to obtain improved feature sets.…”

Section: Comparative Analysismentioning

confidence: 99%

Using Machine Learning to Detect Cyber Attacks

Prakash,

2024

gengpi

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With the relentless growth in cyber threats, the imperative to fortify digital systems against malicious activities has become paramount. Leveraging the capabilities of machine learning (ML) has emerged as a pivotal strategy for bolstering cybersecurity defenses. This paper provides an in-depth exploration of the application of ML techniques in the realm of cyber attack detection. The study delves into the intricacies of feature selection, data preprocessing, and model evaluation techniques, pivotal components in refining the accuracy and efficiency of ML-based cybersecurity systems. The paper illustrates the practical implementation of these ML models across diverse cyber attack scenarios, showcasing their effectiveness in identifying and mitigating threats.

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Section: Comparative Analysismentioning

confidence: 99%

Using Machine Learning to Detect Cyber Attacks

Prakash,

2024

gengpi

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“…In [49] the ensemble combines well-known grouping methods such as Naïve Bayes, Multilayer Perceptron (MLP), and SVM, Decision trees.…”

Section: Ensemble Algorithmsmentioning

confidence: 99%

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

Maksimović,

Nikolić,

Vidojević

et al. 2024

IEEE Access

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This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

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“…An efficient technique to propose IDS was conducted by M. Bakro et al [37]. It uses a feature selection approach using four filters, chi-square, symmetric uncertainty, and an automated method of the stacked encoder.…”

Section: Related Workmentioning

confidence: 99%

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Alabrah

2023

Applied Sciences

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Network security problems arise these days due to many challenges in cyberspace. The malicious attacks on installed wide networks are rapidly spreading due to their vulnerability. Therefore, the user and system information are at high risk due to network attacks. To protect networks against these attacks, Network Intrusion Detection and Prevention Systems (NIDPS) are installed on them. These NIDPS can detect malicious attacks by monitoring abnormal behavior and patterns in network traffic. These systems were mainly developed using Artificial Intelligence (AI) algorithms. These intelligent NIDPS are also able to detect the attack type while detecting network attacks. Previous studies have proposed many NIDPS for network security. However, many challenges exist so far such as limited available data for training AI algorithms, class imbalance problems, and automated selection of the most important features. These problems need to be solved first, which will lead to the precise detection of network attacks. Therefore, the proposed framework used the highly imbalanced UNSW-NB15 dataset for binary and multiclass classification of network attacks. In this framework, firstly dataset normalization is applied using standard deviation and the mean of feature columns; secondly, an Improved Salp Swarm Algorithm (ISSA) is applied for automated feature selection separately on binary and multiclass subsets. Thirdly, after applying feature selection, the SMOTE–Tomek class balancing method is applied where at least four different ML classifiers are used for binary and multiclass classification. The achieved results outperformed as compared to previous studies and improved the overall performance of NIDPS.

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Efficient Intrusion Detection System in the Cloud Using Fusion Feature Selection Approaches and an Ensemble Classifier

Cited by 25 publications

References 91 publications

Using Machine Learning to Detect Cyber Attacks

Using Machine Learning to Detect Cyber Attacks

Using Triple Modular Redundancy for Threshold Determination in DDOS Intrusion Detection Systems

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

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