Distributed denial of service attack detection and mitigation strategy in 5G-enabled internet of things networks with adaptive cascaded gated recurrent unit

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

Published: Jan. 28, 2025

Language: Английский

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Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1905 - 1905

Published: March 19, 2025

The Internet has been vulnerable to several attacks as it expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). three main types most frequently reported in the current period are DoS DDoS attacks. Advanced too complex for traditional security solutions, such intrusion detection systems firewalls, detect. combination machine learning methods with AI-based led introduction novel attack systems. Due their remarkable performance, models, particular, have essential identifying However, there is a considerable gap work on real-time This study uses Mininet POX Controller simulate an environment detect settings. CICDDoS2019 dataset identifies classifies simulated environment. In addition, virtual software-defined network (SDN) used collect information from surrounding area. When occurs, pre-trained models analyze traffic predict real-time. performance proposed methodology evaluated based two metrics: accuracy time. results reveal that model achieves 99% within 1 s

Language: Английский

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Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise

Technologies, Journal Year: 2025, Volume and Issue: 13(4), P. 141 - 141

Published: April 4, 2025

Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent interpolation-based techniques. Five classifiers, including XGBoost convolutional neural network (CNN), were evaluated on augmented datasets. achieved superior performance noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming These results underscore noise’s efficacy enhancing accuracy, offering robust alternative conventional oversampling methods. Our findings emphasize pivotal role of strategies optimizing classifier for financial data.

Language: Английский

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LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments

Published: July 24, 2024

In detecting DDoS, deep learning faces challenges and difficulties such as high computational demands, long training times, complex model interpretation. This research focuses on overcoming these by proposing an effective strategy for DDoS attacks in unbalanced network environments. uses SMOTE to increase the class distribution of data set allowing models using LSTM learn time anomalies effectively when occur. The experiments carried out have shown significant improvement performance integrated with SMOTE. These include validation loss results 0.048 0.1943 without SMOTE, accuracy 99.50 97.50. Apart from that, there was f1 score 93.4% 98.3%. this research, it is proven that can be used improve heterogeneous networks, well increasing robustness reliability.

Language: Английский

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DBSCAN SMOTE LSTM: Effective Strategies for Distributed Denial of Service Detection in Imbalanced Network Environments

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(9), P. 118 - 118

Published: Sept. 10, 2024

In detecting Distributed Denial of Service (DDoS), deep learning faces challenges and difficulties such as high computational demands, long training times, complex model interpretation. This research focuses on overcoming these by proposing an effective strategy for DDoS attacks in imbalanced network environments. employed DBSCAN SMOTE to increase the class distribution dataset allowing models using LSTM learn time anomalies effectively when occur. The experiments carried out revealed significant improvement performance integrated with SMOTE. These include validation loss results 0.048 0.1943 without SMOTE, accuracy 99.50 97.50. Apart from that, there was F1 score 93.4% 98.3%. proved that can be used improve heterogeneous networks, well increasing robustness reliability.

Language: Английский

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Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

Security and Privacy, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

ABSTRACT The Internet of Things (IoT) represents a vast network devices connected to the Internet, making it easier for users connect modern technology. However, complexity these networks and large volume data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks spoofing. It has become necessary use intrusion detection systems protect networks. Existing IoT face many problems limitations, including high false alarm rates delayed detection. Also, datasets used training may be outdated or sparse, which reduces model's accuracy, mechanisms not defend when any is detected. To address new hybrid deep learning machine methodology proposed that contributes detecting DDoS spoofing attacks, reducing alarms, then implementing defensive measures. In consists three stages: first stage propose method feature selection consisting techniques (correlation coefficient sequential selector); second model by integrating neural with classifier (cascaded long short‐term memory [LSTM] Naive Bayes classifier); third stage, improving defense blocking ports after threats maintaining integrity. evaluating performance methodology, (CIC‐DDoS2019, CIC‐IoT2023, CIC‐IoV2024) were used, also balanced obtain effective results. accuracy 99.91%, 99.88%, 99.77% was obtained. cross‐validation technique test ensure no overfitting. proven its provides powerful solution enhance security can applied fields other attacks.

Language: Английский

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Collaborative Defense Method Against DDoS Attacks on SDN-Architected Cloud Servers

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 362 - 370

Published: Jan. 1, 2024

Language: Английский

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Detection and Mitigation of DDoS Attacks : A Review of Robust and Scalable Solutions

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Journal Year: 2024, Volume and Issue: 10(5), P. 12 - 23

Published: Sept. 5, 2024

Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust scalable detection mitigation techniques. While numerous methods been proposed, limitations such high false positive rates, resource constraints, evolving nature continue challenge existing solutions. This aims analyze evaluate various mechanisms, including machine learning, anomaly detection, hybrid models, focus on scalability adaptability in real-world applications. objective identify key strengths weaknesses current approaches, highlighting future research directions for building resilient defense capable operating efficiently under high-traffic conditions.

Language: Английский

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Hybridization of synergistic swarm and differential evolution with graph convolutional network for distributed denial of service detection and mitigation in IoT environment

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Enhanced technologies of the future are gradually improving digital landscape. Internet Things (IoT) technology is an advanced technique that quickly increasing owing to development a network organized online devices. In today's era, IoT considered one most robust technologies. However, attackers can effortlessly hack devices employed generate botnets, and it applied present distributed denial service (DDoS) attacks beside networks. The DDoS attack foremost on system causes complete go down. Thus, average consumers may need help get services they from server. compromised or want be perceived well in system. So, presently, Deep Learning (DL) plays prominent part forecasting end-users' behaviour by extracting features identifying adversary network. This paper proposes Synergistic Swarm Optimization Differential Evolution with Graph Convolutional Network Cyberattack Detection Mitigation (SSODE-GCNDM) environment. main intention SSODE-GCNDM method recognize presence platforms. Primarily, utilizes Z-score normalization scale input data into uniform format. presented approach synergistic swarm optimization differential evolution (SSO-DE) for feature selection. Moreover, graph convolutional (GCN) recognizes mitigates attacks. Finally, implements northern goshawk (NGO) fine-tune hyperparameters involved GCN method. An extensive range experimentation analyses occur, outcomes observed using numerous features. experimental validation portrayed superior accuracy value 99.62% compared existing approaches.

Language: Английский

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