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

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Авг. 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.

Язык: Английский

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Cyber-Secure SDN: A CNN-Based Approach for Efficient Detection and Mitigation of DDoS attacks

Computers & Security, Год журнала: 2024, Номер 139, С. 103716 - 103716

Опубликована: Янв. 21, 2024

Язык: Английский

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Machine Learning and Deep Learning Techniques for Distributed Denial of Service Anomaly Detection in Software Defined Networks—Current Research Solutions

IEEE Access, Год журнала: 2024, Номер 12, С. 17982 - 18011

Опубликована: Янв. 1, 2024

This state-of-the-art review comprehensively examines the landscape of Distributed Denial Service (DDoS) anomaly detection in Software Defined Networks (SDNs) through lens advanced Machine Learning (ML) and Deep (DL) techniques. The application domain this work is focused on addressing inherent security vulnerabilities SDN environments developing an automated system for detecting mitigating network attacks. problem need effective defensive mechanisms methodologies to address these vulnerabilities. Conventional measurement are limited context SDNs, proposed ML DL techniques aim overcome limitations by providing more accurate efficient mitigation DDoS objective provide a comprehensive related works field recent advances, categorized into two groups via systems utilize variety techniques, including Supervised (SL), Unsupervised (UL) Ensemble (EL) solutions, process IP flows, profile traffic, identify output comprises policies learned ML/DL act as sophisticated gatekeepers, applying curtail extent damage resulting from results obtained evaluation metrics, accuracy, precision, recall, confirm marked effectiveness various types attacks, systems' foundational contributions manifest their efficacy both attack defense within environment. However, acknowledges certain pressing further validation real-world scenarios assess methods' practicality effectiveness. In summary, systematic offers valuable perspectives present status Denial-of-Service Software-Defined employing methodologies, highlighting strengths identifying areas future research development.

Язык: Английский

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Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing

Mathematics, Год журнала: 2024, Номер 12(9), С. 1294 - 1294

Опубликована: Апрель 25, 2024

The early and accurate detection of Distributed Denial Service (DDoS) attacks is a fundamental area research to safeguard the integrity functionality organizations’ digital ecosystems. Despite growing importance neural networks in recent years, use classical techniques remains relevant due their interpretability, speed, resource efficiency, satisfactory performance. This article presents results comparative analysis six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), Dense Neural Network (DNN), for classifying DDoS attacks. CICDDoS2019 dataset was used, which underwent data preprocessing remove outliers, 22 features were selected using Pearson correlation coefficient. RF classifier achieved best accuracy rate (99.97%), outperforming other classifiers even previously published network-based techniques. These findings underscore feasibility effectiveness algorithms field attack detection, reaffirming relevance as valuable tool advanced cyber defense.

Язык: Английский

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MP-GUARD: A novel multi-pronged intrusion detection and mitigation framework for scalable SD-IoT networks using cooperative monitoring, ensemble learning, and new P4-extracted feature set

Computers & Electrical Engineering, Год журнала: 2024, Номер 118, С. 109484 - 109484

Опубликована: Июль 23, 2024

Язык: Английский

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Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices

Sensors, Год журнала: 2024, Номер 24(15), С. 5022 - 5022

Опубликована: Авг. 3, 2024

The number of connected devices or Internet Things (IoT) has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion IoT devices; this is expected reach 25.4 by 2030 and grow year over for foreseeable future. share, collect, exchange data via internet, wireless networks, other networks with one another. interconnection technology improves facilitates people's lives but, at same time, poses a real threat their security. Denial-of-Service (DoS) Distributed (DDoS) attacks are considered most common threatening that strike devices' These be an increasing trend, it will major challenge reduce risk, especially In context, paper presents improved framework (SDN-ML-IoT) works as Intrusion Prevention Detection System (IDPS) could help detect DDoS more efficiency mitigate them time. This SDN-ML-IoT uses Machine Learning (ML) method Software-Defined Networking (SDN) environment order protect smart home from attacks. We employed ML based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), Naive Bayes (NB) One-versus-Rest (OvR) strategy then compared our work related works. Based performance metrics, such confusion matrix, training prediction accuracy, Area Under Receiver Operating Characteristic curve (AUC-ROC), was established SDN-ML-IoT, when applied RF, outperforms algorithms, well similar approaches work. It had impressive accuracy 99.99%, less than 3 s. conducted comparative analysis various models algorithms used results indicated proposed approach others, showcasing its effectiveness both detecting mitigating within SDNs. these promising results, we have opted deploy SDN. implementation ensures safeguarding homes against network traffic.

Язык: Английский

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Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection

IEEE Access, Год журнала: 2024, Номер 12, С. 93235 - 93260

Опубликована: Янв. 1, 2024

Cyber Threat Detection (CTD) is subject to complicated and rapidly accelerating developments. Poor accuracy, high learning complexity, limited scalability, a false positive rate are problems that CTD encounters. Deep Learning defense mechanisms aim build effective models for threat detection protection allowing them adapt the complex ever-accelerating changes in field of CTD. Furthermore, swarm intelligence algorithms have been developed tackle optimization challenges. In this paper, Chaotic Zebra Optimization Long-Short Term Memory (CZOLSTM) algorithm proposed. The proposed hybrid between Algorithm (CZOA) feature selection LSTM cyber classification CSE-CIC-IDS2018 dataset. Invoking chaotic map CZOLSTM can improve diversity search avoid trapping local minimum. evaluating effectiveness newly CZOLSTM, binary multi-class classifications considered. acquired outcomes demonstrate efficiency implemented improvements across many other algorithms. When comparing performance detection, it outperforms six innovative deep five classification. Other evaluation criteria such as recall, F1 score, precision also used comparison. results showed best accuracy was achieved using 99.83%, with F1-score 99.82%, recall 99.82%. among compared

Язык: Английский

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MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

Internet of Things, Год журнала: 2024, Номер 26, С. 101231 - 101231

Опубликована: Май 19, 2024

Язык: Английский

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Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Telecom, Год журнала: 2024, Номер 5(2), С. 333 - 346

Опубликована: Апрель 17, 2024

SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives administration employees more authority, allowing for seamless supervision. However, centralization makes it vulnerable a variety of attack vectors, with distributed denial service (DDoS) attacks posing serious concern. Feature selection-based Machine Learning (ML) techniques are than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in context defending against attacks. In this study, NGBoost is compared four additional machine learning algorithms: convolutional neural (CNN), Stochastic Gradient Descent (SGD), Decision Tree, Random Forest, order assess effectiveness DDoS detection on CICDDoS2019 dataset. It focuses important measures such as F1 score, recall, accuracy, precision. We have examined NeTBIOS, layer-7 attack, SYN, layer-4 our paper. Our investigation shows that Natural Boosting Convolutional Neural Networks, particular, show promise tabular data categorization. conclusion, we go through specific study results protecting using DDoS. These experimental findings offer framework making decisions.

Язык: Английский

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Next-gen distributed denial-of-service detection and mitigation in software-defined networking using hybrid machine learning approach

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 97 - 133

Опубликована: Янв. 1, 2025

Язык: Английский

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