Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection

Ghazy, Rania A.; El-Rabaie, El-Sayed M.; Dessouky, Moawad I.; El-Fishawy, Nawal; El‐Samie, Fathi E. Abd

doi:10.1007/s11277-019-06864-3

Cited by 20 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NSL-KDD dataset was used in the above-mentioned study, but the authors suggest a hybrid method for more accurate results. In [13], the authors suggest a feature selection technique using filter and wrapper methods, but these are computationally expensive. Meanwhile, in [14], the authors propose three IDS on K-means clustering, a decision tree, and a hybrid of these methods to achieve a maximum detection rate of 70-93%.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Sarwar

Alnajim

Marwat

et al. 2022

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks. To date, numerous intrusion detection systems (IDS) have been designed for IoT networks, using various optimization techniques. However, with the increase in data dimensionality, the search space has expanded dramatically, thereby posing significant challenges to optimization methods, including particle swarm optimization (PSO). In light of these challenges, this paper proposes a method called improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection, introducing a dynamic search space reduction strategy and a number of dynamic parameters to enhance the searchability of sticky binary particle swarm optimization (SBPSO). Through this approach, an IDS was designed to detect malicious data traffic in IoT networks. The proposed model was evaluated using two IoT network datasets: IoTID20 and UNSW-NB15. It was observed that in most cases, IDSBPSO obtained either higher or similar accuracy even with less number of features. Moreover, IDSBPSO substantially reduced computational cost and prediction time, compared with conventional PSO-based feature selection methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Sarwar

Alnajim

Marwat

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This feature selection is a rule-based algorithm that chooses the lowest error rate attributes as its one rule and then ranked them accordingly [59], [60]. It constructs rules and tests a single attribute at a time and branch for every value of that attribute [61].…”

Section: One-r (Or)mentioning

confidence: 99%

An Approach for Optimizing Ensemble Intrusion Detection Systems

et al. 2021

View full text Add to dashboard Cite

A hybrid intelligently initialized particle swarm optimizer with weight factored binary gray wolf optimizer for mitigation of security issues in Internet of Things and sensor nodes

Agrawal

Singh

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary With expanding nature of Internet of Things (IoT) based solutions in various application areas, the threat of cyber attacks is looming large. Often for resource constrained IoT nodes, implementation of machine learning based intrusion detection system is not practical, as the feature set to be processed is large in size. In this article, random forest based intelligently initialized hybrid binary particle swarm optimizer (PSO)‐gray wolf optimizer (GWO) is proposed to reduce the dimensions of dataset. The proposed algorithm exploits the concept of relative weights of the leader wolves of GWO to update the positions of particles in PSO. A novel fitness function is also introduced, that includes the key performance metrics for measuring the classification efficiency. Also, a new performance metric is proposed here for commensurate comparison with other related works, which renders the task of comparison effective. For experimental evaluation, the historical dataset NSL‐KDD and the more contemporary DS2OS dataset are both taken into consideration. The proposed work attains accuracy up to 99.61% for NSL‐KDD dataset and 99.79% for DS2OS dataset. The outcomes are better than majority of algorithms and recent related works. Most notably, for increasing the accuracy, the number of features are not compromised and are even reduced to 8 and 5 features, respectively, for NSL‐KDD and DS2OS.

show abstract

Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection

Cited by 20 publications

References 8 publications

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

An Approach for Optimizing Ensemble Intrusion Detection Systems

A hybrid intelligently initialized particle swarm optimizer with weight factored binary gray wolf optimizer for mitigation of security issues in Internet of Things and sensor nodes

Contact Info

Product

Resources

About