Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

Mishra, Anupama; Joshi, Bineet Kumar; Arya, Varsha; Gupta, Avadhesh Kumar; Chui, Kwok Tai

doi:10.4018/ijssci.309707

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…Based on this, a hierarchical joint learning architecture was utilized to ensure the privacy of the learning process and organizational data, achieving a secure and privacy collaborative IoT NID. While these research methods all have superior performance in certain aspects, further improvement is still needed to meet the urgent needs of network security (Mishra et al, 2022). A new set of classifiers has been proposed to address the issue of significant network bandwidth and server losses caused by DDoS attacks.…”

Section: Fedl-based Nidmentioning

confidence: 99%

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

Zhou,

Shi

2024

International Journal on Semantic Web and Information Systems

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Under the premise of ensuring data privacy, traditional network intrusion detection (NID) methods cannot achieve high accuracy for different types of intrusions. A NID method combining transformer and federated learning (FedL) is proposed for this purpose. First, a multi-party collaborative learning framework was built based on FedL, which achieved data exchange and sharing. Then, by introducing the self-attention mechanism (AttM) to improve the traditional transformer, it could quickly converge. Finally, an NID model integrating transformer and FedL was constructed by combining DNN, GRU, and an encoder module composed of improved transformer, achieving accurate detection of network intrusion. The proposed NID method was compared with the other three methods. The results show that the proposed method has the highest NID accuracy and F1 score on the NSL-KDD and UNSW-NB15 dataset, with the highest accuracy reaching 99.65% and 89.25%, while the F1 score has the highest accuracy, reaching 99.45% and 88.13%, outperforming the other three comparative algorithms in terms of performance.

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Section: Fedl-based Nidmentioning

confidence: 99%

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

Zhou,

Shi

2024

International Journal on Semantic Web and Information Systems

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“…13 A Distributed Denial of Service (DDoS) assault is a type of malicious attack against cloud systems that can create significant disruptions. 14 Several DDoS attacks are now conducted utilizing structured, remotely managed botnets. 12 The brute-force and semantic are the two types of DDoS assaults.…”

Section: Introductionmentioning

confidence: 99%

“…However, numerous DDoS assaults remain in the cloud platform variant in implementation strategy, scale, and aim 13 . A Distributed Denial of Service (DDoS) assault is a type of malicious attack against cloud systems that can create significant disruptions 14 . Several DDoS attacks are now conducted utilizing structured, remotely managed botnets 12 .…”

Section: Introductionmentioning

confidence: 99%

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

Reddy,

Meera

2024

Adaptive Control & Signal

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SummaryDDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre‐processing, feature extraction, and attack detection are the three main components of the suggested intrusion detection system. Improved data imbalance processing is processed during pre‐processing. Features including HOS‐based features enhanced entropy‐based features, correlation features, and raw features are extracted from the pre‐processed data. The generated features are trained using an ensemble model, which integrates classifiers like SVM, RF, NN, LSTM, and DRN, during the attack detection phase. A new hybrid approach known as TUDMA optimally trains the model by setting the ideal weight because DRN provides the final detected outcome. By reducing errors, this ideal training will guarantee an improvement in detection results. The suggested hybrid optimization combines the two techniques, TDO and DMO. The accuracy of the methods FS‐WOA‐DNN, RBF‐PSO, SMA, BRO, SLO, and NMRA was 86.73%, 85.69%, 84.58%, 87.43%, 88.91%, and 89.78%, respectively, while the accuracy of the TUDMA was 95.34% in the 80th learning percentage. Finally, for many measurements, the suggested efficiency is superior to the traditional methods.

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“…However, the inherent characteristics of WSNs, like limited resources, dynamic network topology, and lack of a centralized management infrastructure, make them vulnerable to a myriad of attacks. WSN attacks manifests in various forms, including Denial‐of‐Service (DoS), Distributed Denial of Service (DDoS), Root to Local (R2L), User to Root (U2R) attacks, 1–3 among others. These attacks compromise data integrity, confidentiality, and availability, posing significant challenges to the reliability and performance of WSNs 4,5 .…”

Section: Introductionmentioning

confidence: 99%

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Seth,

Sharma,

Ratmele

2024

Concurrency and Computation

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SummaryDetecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs), as they are susceptible to various attacks that compromise nodes and yield inaccurate outcomes. Conventional attack detection approaches face challenges like high false positives, vulnerability to complex attacks, and limited adaptability to changing attacks due to predefined patterns. Additionally, the computational strain on resource‐constrained nodes hampers network efficiency, demanding innovative and resilient security solutions. Therefore, this research work introduces Novel Hybrid Approaches for Privacy‐preserved Multiple Attacks Detection (NHAPMAD) framework in both network attack detection system and host attack detection system. This topology bolsters the network's defense against various types of attacks, thereby ensuring the preservation of sensitive data privacy. Federated learning has been incorporated into the NHAPMAD framework to protect user privacy in WSNs. The introduced framework provides better anomaly detection and reduces false alarms. Additionally, the introduced model protects sensitive data, ensuring the security and integrity of the entire WSN, promoting a stable and dependable operating environment. The introduced method exhibits superior performance across various metrics like overall accuracy (99.6%), F1‐score (99.59%), recall (99.47%), precision (99.63%), detection rate (99.5%), processing time (0.0009 s), etc. compared to traditional approaches, marking a significant advancement in the realm of WSN security.

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Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

Cited by 12 publications

References 30 publications

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

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