Feature Selection for Deep Neural Networks in Cyber Security Applications

Davis, Alexander; Gill, Sumanjit; Wong, Robert Y.; Tayeb, Shahab

doi:10.1109/iemtronics51293.2020.9216403

Cited by 14 publications

(7 citation statements)

References 35 publications

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“…IDS plays a vital role in network security as its objective is to prevent disruption in the communication network. Ideally, static probability, neural networks, and deep learning algorithms will be used to evaluate if data are malicious or not [9]. To overcome signaturebased attack classification drawbacks, the research focuses on dynamic approaches based on machine learning.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

“…Here, actions of the network are learned by using the data recorded to identify new types of attacks. Work on machine learning has expanded the security of the systems overall [9], and the major research topic in network security is IDS due to the significant impact of attacks and violations in real-time user applications [9]. Denning et al [10] specify an efficient model of real-time IDS systems that can detect penetration and break-in of attacks in computer attacks.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

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Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

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With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.

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Section: Intrusion Detection Systemmentioning

confidence: 99%

Section: Intrusion Detection Systemmentioning

confidence: 99%

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

Self Cite

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“…This operation removes unwanted features based on the feature importance top score and uses the feature ranking, leading to increased learning algorithm performance [39], [40]. Also, this process provides the model with the removal of the redundant information and improvement in the generalization [41]. Many techniques are used for feature selection, such as Gain Ratio (GR), Symmetrical uncertainty, Chi-Square analysis, Information Gain (IG), and Practical Swarm Optimization (PSO) [42], [43].…”

Section: Effectiveness Of Dimensionality Reduction For Feature Selectionmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…The Min-Max normalization [28] is among the most commonly used normalization [29] [30] [31]. Min-Max implements linear transformation on the data.…”

Section: Related Workmentioning

confidence: 99%

An Agile Approach to Identify Single and Hybrid Normalization for Enhancing Machine Learning-Based Network Intrusion Detection

Siddiqi¹,

Pak

2021

IEEE Access

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Detecting intrusion in network traffic has remained a problematic task for years. Progress in the field of machine learning is paving the way for enhancing intrusion detection systems. Due to this progress intrusion detection has become an integral part of network security. Intrusion detection has achieved high detection accuracy with the help of supervised machine learning methods. A key factor in enhancing the performance of supervised classifiers is how data is augmented for training the classification model. Data in real-world networks or publicly available datasets are not always normally (Gaussian) distributed. Instead, the distributions of variables are more likely to be skewed. To achieve a high detection rate, data normalization or transformation plays an important role for machine learning-based intrusion detection systems. Several methods are available to normalize the attributes of the data before training a classification model. However, opting for the most suitable normalization technique is still a questionable task. In this paper, a statistical method is proposed that can identify the most suitable normalization method for the dataset. The normalization method identified by the proposed approach gives the highest accuracy for an intrusion detection system. To highlight the efficiency of the proposed method, five different datasets were used with two different feature selection methods. The datasets belong to both Internet of things and traditional network environments. The proposed method is also able to identify hybrid normalizations to achieve even improved intrusion detection results.

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Feature Selection for Deep Neural Networks in Cyber Security Applications

Cited by 14 publications

References 35 publications

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

An Agile Approach to Identify Single and Hybrid Normalization for Enhancing Machine Learning-Based Network Intrusion Detection

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