Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

Shah, Reehan Ali; Qian, Yuntao; Kumar, Dileep; Ali, Murtaza; Alvi, Muhammad Bux

doi:10.3390/fi9040081

Cited by 29 publications

(15 citation statements)

References 28 publications

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“…Shah et al 59 devise sparse logistic regression (SPLR) model for feature selection problems in intrusion detection systems over the KDD99 dataset. Adaptive boosting (AB) or in simple AdaBoost 60 is an ensemble learning boosting technique algorithm, which is used to reduce the weighted errors during training samples of the boosting or to minimize the over‐fitting problems inherent to machine learning.…”

Section: Proposed Methodologymentioning

confidence: 99%

Botnet attack detection in Internet of Things devices over cloud environment via machine learning

Waqas

Kumar

Laghari

et al. 2021

Concurrency and Computation

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Summary With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can communicate with each other and share data easily. However, the IoT paradigm is prone to security concerns as many attackers try to hit the network and make it vulnerable. In this scenario, security concerns are the most important and to address them various models have been designed to overcome these security issues, but still there exist many emerging variants of botnet attacks such as Mirai, Persirai, and Bashlite that exploits the security breaches. This research article aims to investigate cyber security in the advent of B‐IDS, DDOS, and malware attacks. For this purpose, different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection. For performance measures, these algorithms have been tested on nine sensor devices over N‐BaIoT datasets to measure the security and accuracy of the intrusion detection system. The results show that the tree‐based algorithm achieved more than 99% accuracy which is quite higher as compared to other tested methods on the same sensor devices.

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Section: Proposed Methodologymentioning

confidence: 99%

Botnet attack detection in Internet of Things devices over cloud environment via machine learning

Waqas

Kumar

Laghari

et al. 2021

Concurrency and Computation

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show abstract

“…It can be combined with the other IDS feature selection and classification models in detecting and classifying the advanced and new threats, thereby reducing the false alarms. The researchers [10] present Network Intrusion Detection Using Sparse Regression Techniques and Discriminative Feature Selection. SPLR may integrate feature extraction and categorization into a cohesive framework, unlike features extraction methods such as "filter" and "wrapper" techniques, which divide the attribute choice and categorization concerns.…”

Section: Related Workmentioning

confidence: 99%

A Network Intrusion Detection Framework on Sparse Deep Denoising Autoencoder for Dimensionality Reduction

et al. 2023

Preprint

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"Network security" is currently among the crucial areas of computer science. Due to the proliferation of IoT tools and "peer-to-peer" systems, the necessity to mitigate safety concerns is prominent. Intrusion detection in network systems is employed to analyse network packets for malicious activity. Different kinds of attacks, such as "Denial of Service", "Probe", "Remote-to-Local", and "User-to-Root" are instances of unexpected behaviour. Once attacks are spotted, customized alerts can be sent to the personnel responsible. In numerous application fields of information security, intrusion detection is a necessity in the modern era. In this study, we examined the usefulness of several autoencoder types in identifying network intrusions. In this research, a system for network intrusion detection based on Sparse Deep Denoising Autoencoder for dimension reduction was established. If the autoencoder is trained on regular network information, reconstruction error (the dissimilarity between the original and recreated data) is used to identify anomalies. Intrusion Detection System Development utilized standard datasets such as KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. The effectiveness of four distinct autoencoders was analysed to identify network attacks. We were able to achieve over 96% accuracy with only reconstruction error by using a sparse deep denoising autoencoder. The primary research aim was to enhance the performance of the network by achieving high intrusion detection precision.

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“…Syarif et al [10] have used a combination of Binary Particle Swarm Optimization (PSO) and K-Nearest Neighbour (KNN) for network intrusion detection. Shah et al [11] propose a network attack detection technique based on Sparse Logistic Regression. Sparse Logistic Regression in their case is used for feature selection and attack classification.…”

Section: ) Machine Learningmentioning

confidence: 99%

“…The used atypical attacks are synthesized in a virtual environment to make better generalizations for the AI models. Our approach is unique, compared to other AI cybersecurity [10], [11], [14], [16], [17], [19], in that it improves model generalization using hyperparameter optimization and retraining to produce unbiased classifiers.…”

Section: E Novelty and Advantagesmentioning

confidence: 99%

Building an Intrusion Detection System to Detect Atypical Cyberattack Flows

et al. 2021

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Artificial Intelligence (AI) techniques provide effective solutions for the detection of many aberrant network traffic patterns and attack flows. However, the validation of these techniques often relies on one training dataset. Recent results show that such training may fail in the face of dynamically-changing cyberattacks. Given the increased sophistication of cyberattacks nowadays, it is imperative to examine and improve the performance of such AI models. This paper proposes a defensive AI engine combined with a twofold feature selection technique and hyperparameter optimization of the AI model. In this work, we utilize the proposed system for binary attack flow identification and the AI models are trained and validated on the CICIDS2017 dataset. The system is then evaluated using synthesized atypical attack flows to mimic real-world scenarios. We demonstrate the effectiveness of the proposed atypical attack flow detection approach using several Deep Learning and Machine Learning models including DNN, Linear-SVC, and Stacked Decision Tree Classifier (S-DTC). Simulation results demonstrate that the proposed defensive AI engine significantly improves the True Positive Rate (TPR) of AI models on multiple atypical attacks.

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Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

Cited by 29 publications

References 28 publications

Botnet attack detection in Internet of Things devices over cloud environment via machine learning

Botnet attack detection in Internet of Things devices over cloud environment via machine learning

A Network Intrusion Detection Framework on Sparse Deep Denoising Autoencoder for Dimensionality Reduction

Building an Intrusion Detection System to Detect Atypical Cyberattack Flows

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