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

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

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Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 82 - 82

Published: Feb. 25, 2025

Recent advancements across various sectors have resulted in a significant increase the utilization of smart gadgets. This augmentation has an expansion network and devices linked to it. Nevertheless, development concurrently rise policy infractions impacting information security. Finding intruders immediately is critical component maintaining The intrusion detection system useful for security because it can quickly identify threats give alarms. In this paper, new approach was proposed. Combining results machine learning models like random forest, decision tree, k-nearest neighbors, XGBoost with logistic regression as meta-model what method based on. For feature selection technique, proposed creates advanced that combines correlation-based embedded technique on XGBoost. handling challenge imbalanced dataset, SMOTE-TOMEK used. suggested algorithm tested NSL-KDD CIC-IDS datasets. It shows high performance accuracy 99.99% both These prove effectiveness approach.

Language: Английский

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An integrated intrusion detection framework based on subspace clustering and ensemble learning

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 115, P. 109113 - 109113

Published: Feb. 10, 2024

In the rapidly evolving landscape of Internet Things (IoT), ensuring robust intrusion detection is paramount for device and data security. This paper proposes a novel method in IoT networks that leverages unique blend subspace clustering ensemble learning. Our framework integrates three innovative strategies: Clustering Results as Features (CRF), Two-Level Decision Making (TDM), Iterative Feedback Loop (IFL). These strategies synergize to enhance performance model robustness. We employ mutual information feature selection utilize four algorithms – CLIQUE, PROCLUS, SUBCLU, LOF create additional sets. Three base learners NB, LGBM, XGB are used conjunction with Logistic Regression (LR) meta-learner. To fine-tune our model, we apply Particle Swarm Optimization (PSO) hyperparameter optimization. evaluate on UNSW-NB15 dataset, which contains realistic diverse network traffic data. The results show outperforms state-of-the-art methods terms accuracy (97.05%), precision (96.33%), recall (96.55%), F1-score (96.45%), false positive rate (0.029). can effectively detect both known unknown attacks achieve high low rate. contributes practical implications security theoretical advancements research.

Language: Английский

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A Survey on Intrusion Detection System in IoT Networks

Cyber Security and Applications, Journal Year: 2024, Volume and Issue: 3, P. 100082 - 100082

Published: Dec. 20, 2024

Language: Английский

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Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and ML

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109603 - 109603

Published: Jan. 1, 2025

Language: Английский

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QoS-Aware cloud security using lightweight EfficientNet with Adaptive Sparse Bayesian Optimization

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 20, 2025

Language: Английский

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An advanced attack categorization in NIDS using enhanced deep learning based EOptResfEANet methodology with hybrid feature extraction

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(5)

Published: April 28, 2025

Language: Английский

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Memory feedback transformer based intrusion detection system for IoMT healthcare networks

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101597 - 101597

Published: May 1, 2025

Language: Английский

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Edge-featured multi-hop attention graph neural network for intrusion detection system

Computers & Security, Journal Year: 2024, Volume and Issue: 148, P. 104132 - 104132

Published: Sept. 26, 2024

Language: Английский

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A Robust Framework for Detecting Brute-Force Attacks through Deep Learning Techniques

International Journal of Recent Technology and Engineering (IJRTE), Journal Year: 2025, Volume and Issue: 13(5), P. 27 - 42

Published: Jan. 25, 2025

A considerable concern arises with the precise identification of brute-force threats within a networked environment. It emphasizes need for new methods, as existing ones often lead to many false alarms, well delays in real-time threat detection. To tackle these issues, this study proposes novel intrusion detection framework that utilizes deep learning models more accurate and efficient attacks. The framework’s structure includes data collection preprocessing components performed at outset using CSE-CICIDS2018 dataset. design architecture steps. Feature extraction selection techniques are employed optimize model training. Further, after building model, various attributes extracted from feature be used Then, construction multiple architectures algorithms, which include Artificial Neural Networks (ANN), Convolutional (CNN), Recurrent (RNN), Long Short-Term Memory (LSTM) models. Evaluation results show CNN LSTM achieved highest accuracy 99.995 Parsant 99.99 respectively. showcases its ability detect complex attack patterns network traffic. indicates got best optimum test time 9.94 seconds. This establishes an effective method, achieving high quickly. In comparison, we have surpassed current methods while addressing their weaknesses. findings consistent effectiveness frameworks faster alternative, increasing capability detecting intrusions on real-time.

Language: Английский

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