Optimal feature selection with CNN-feature learning for DDoS attack detection using meta-heuristic-based LSTM

Dora, V. Raghava Swamy; Lakshmi, V. Naga

doi:10.1007/s41315-022-00224-4

Cited by 23 publications

(17 citation statements)

References 36 publications

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“…The first attack takes advantage of an excellent regular connection, whereas the second uses a sample that matches a legitimate traffic signature. The results of this study using the 2017 dataset are compared with a model presented in [ 35 ] having identical performance and are reported in Table 7 . Thus, we conclude that all our results are comparable with the existing literature.…”

Section: Results and Analysismentioning

confidence: 99%

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar

Huang

Javed

et al. 2023

Sensors

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Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

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Section: Results and Analysismentioning

confidence: 99%

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar

Huang

Javed

et al. 2023

Sensors

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“…However, selecting appropriate features for training the ML model is challenging. Feature selection techniques for automatically selecting high-level features can be a promising solution to this issue [131][132][133]. • It can be noticed that ML techniques achieved exemplary performance and flexibility by learning and representing real-world problem features as nested hierarchy of concepts in a simple way [134,135].…”

Section: Techniques For Intrusion Detection In Sdnmentioning

confidence: 99%

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

Kumar¹,

Alqahtani²

2023

Computer Modeling in Engineering &Amp; Sciences

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Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in the field. It is followed by introducing intrusion detection, ML techniques and their types. Specifically, we present a systematic study of recent works, discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN, and promising future works in this field.

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“…To evaluate the performance of model DARPA1998, DARPA LLS DDoS-1.0, CICIDS2017, NSL-KDD and KDD99 datasets were used with 96.52%, 95.94%, 96.52%, 96.37% and 96.37% respectively. The research work focused on binary classification of DDoS vs benign network traffic where performance on NSL-KDD is comparatively lower ( Dora & Lakshmi, 2022 ).…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Abbas,

Hina,

Sajjad

et al. 2023

PeerJ Computer Science

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Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.

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Optimal feature selection with CNN-feature learning for DDoS attack detection using meta-heuristic-based LSTM

Cited by 23 publications

References 36 publications

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

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