FedNIDS: A Federated Learning Framework for Packet-based Network Intrusion Detection System DOI Open Access
Quoc H. Nguyen, Soumyadeep Hore, Ankit Shah

et al.

Digital Threats Research and Practice, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Network intrusion detection systems (NIDS) play a critical role in discerning between benign and malicious network traffic. Deep neural networks (DNNs), anchored on large diverse datasets, exhibit promise enhancing the accuracy of NIDS by capturing intricate traffic patterns. However, safeguarding distributed computer against emerging cyber threats is increasingly challenging. Despite abundance data, decentralization persists due to data privacy security concerns. This confers an asymmetric advantage adversaries, as face formidable task securely efficiently sharing non-independently identically counter cyber-attacks. To address this, we propose Federated ( FedNIDS ), novel two-stage framework that combines power federated learning DNNs. It aims enhance known attacks, robustness resilience attack patterns, preservation, using packet-level granular data. In first stage, global DNN model collaboratively trained second stage adapts it Our experiments real-world datasets demonstrate effectiveness achieving average F1 score 0.97 across quickly disseminating information within four rounds communication.

Language: Английский

Advancing cybersecurity: a comprehensive review of AI-driven detection techniques DOI Creative Commons

A Salem,

Safaa M. Azzam,

O. E. Emam

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 4, 2024

Abstract As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways detect prevent them. Recognizing cyber threats quickly accurately is crucial because they can cause severe damage individuals businesses. This paper takes a close look at how we use artificial intelligence (AI), including machine learning (ML) deep (DL), alongside metaheuristic algorithms better. We've thoroughly examined over sixty recent studies measure effective these AI tools are identifying fighting wide range threats. Our research includes diverse array cyberattacks such as malware attacks, network intrusions, spam, others, showing that ML DL methods, together with algorithms, significantly improve well find respond We compare methods out what they're where could improve, especially face new changing cyber-attacks. presents straightforward framework for assessing Methods in threat detection. Given complexity threats, enhancing regularly ensuring strong protection critical. evaluate effectiveness limitations current proposed models, addition algorithms. vital guiding future enhancements. We're pushing smart flexible solutions adapt challenges. The findings from our suggest protecting against will rely on continuously updating stay ahead hackers' latest tricks.

Language: Английский

Citations

32
An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things DOI

Karima Hassini,

Safae Khalis,

Omar Habibi

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 294, P. 111785 - 111785

Published: April 10, 2024

Language: Английский

Citations

17
DDoS attack detection and mitigation using deep neural network in SDN environment DOI
Vanlalruata Hnamte, Ashfaq Ahmad Najar, Hong-Nhung Nguyen

et al.

Computers & Security, Journal Year: 2023, Volume and Issue: 138, P. 103661 - 103661

Published: Dec. 19, 2023

Language: Английский

Citations

35
Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity DOI Creative Commons
Asaad Balla, Mohamed Hadi Habaebi, Elfatih A. A. Elsheikh

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 6100 - 6116

Published: Jan. 1, 2024

Supervisory Control and Data Acquisition (SCADA) systems are crucial for modern industrial processes securing them against increasing cyber threats is a significant challenge. This study presents an advanced method bolstering SCADA security by employing modified hybrid deep learning model. A key innovation in this work integrating the Self-similarity Hurst parameter into dataset alongside CNN-LSTM model, significantly boosting Intrusion Detection System's (IDS) capabilities. The parameter, which quantifies self-similarity dataset, instrumental detecting anomalies. Our in-depth analysis of CICIDS2017 sheds light on contemporary attack patterns network traffic behaviors. architecture was substantially altered adding multiple convolutional layers with progressively filters, batch normalization stable training, dropout regularization. Principal Component Analysis (PCA) applied dimensionality reduction, thereby optimizing dataset. Test results demonstrate superior performance model incorporating achieving 95.21% accuracy 82.59% recall, surpassing standard inclusion marks substantial advancement identifying emerging threats, while architectural improvements to led more robust accurate intrusion detection control settings.

Language: Английский

Citations

5
Explainable AI-based innovative hybrid ensemble model for intrusion detection DOI Creative Commons
Usman Ahmed, Jiangbin Zheng, Ahmad Almogren

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Oct. 21, 2024

Abstract Cybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial identifying illegitimate access or anomalous behaviour within computer systems, consequently opposing sensitive information. Traditional IDS approaches often struggle high false positive rates ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for (HAEnID), an innovative powerful method enhance intrusion detection, different from conventional techniques. HAEnID is composed of string multi-layered ensemble, which consists Stacking (SEM), Bayesian Model Averaging (BMA), Conditional (CEM). combines best these three ensemble techniques ultimate success considerable cut alarms. A key feature adaptive mechanism that allows components change over time as traffic patterns vary new appear. way, would provide adequate protection vectors change. Furthermore, model interpretable explainable using Shapley Additive Explanations (SHAP) Local Interpretable Model-agnostic (LIME). The proposed on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness consistency across various configurations. Feature selection further enhances performance, BMA-M (20) reaching 98.79% accuracy. These results highlight potential accurate reliable and, hence, state-of-the-art choice explainability.

Language: Английский

Citations

5
5G-SIID: an intelligent hybrid DDoS intrusion detector for 5G IoT networks DOI

Sapna Sadhwani,

Aakar Mathur,

Raja Muthalagu

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 27, 2024

Language: Английский

Citations

4
Detecting attacks on the internet of things network in the computing fog layer with an embedded learning approach based on clustering and blockchain DOI

Abdolmanan Babaei Goushlavandani,

Peyman Bayat, Gholamhossein Ekbatanifard

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0
A comprehensive framework for cyber threat detection: leveraging AI, NLP, and malware analysis DOI
Nachaat Mohamed

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

Citations

0
Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification DOI

Benitha Christinal J,

Ameelia Roseline A

Optical Fiber Technology, Journal Year: 2025, Volume and Issue: 93, P. 104206 - 104206

Published: March 20, 2025

Language: Английский

Citations

0
Artificial Intelligence-Driven Network Intrusion Detection and Response System DOI

Haokun Chen,

Yiqun Wang, Song Zhai

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 508 - 518

Published: Jan. 1, 2025

Language: Английский

Citations

0