Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani, Ahamed

doi:10.1109/access.2021.3062909

Cited by 90 publications

(17 citation statements)

References 138 publications

(135 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 4 below shows the comparative analysis of the current study with previous state of art methods (ML/DL) of studies. [42] Trees 85.55% DDoS Datasets Gao, Aljuhani, et al [43,44] ML (KNN, SVM, ANN) 83%, 84%, 81% Banking Datasets Rehman et al [45] GRU 81.7% DDoS Datasets Guo et al [46] ANN, SVM 88.5%, 91% Real Time Dataset Additionally, the proposed models have some disadvantages. The models required high computational power and specialized hardware, e.g., they needed a good GPU to accomplish the training process.…”

Section: Discussionmentioning

confidence: 99%

“…The models required high computational power and specialized hardware, e.g., they needed a good GPU to accomplish the training process. [39] ANN Model 83.5% IoT Banking Devices Datasets deh et al [40] CNN-LSTM 78%, 79% Banking Fraud Time Series Data et al [41] SVM 86.7% DDoS Datasets a et al [42] Trees 85.55% DDoS Datasets ani, et al [43,44] ML(KNN,SVM,ANN) 83%,84%,81% Banking Datasets n et al [45] GRU 81.7% DDoS Datasets et al [46] ANN, SVM 88.5%, 91% Real Time Dataset…”

Section: Time Complexity (Sec)mentioning

confidence: 99%

See 1 more Smart Citation

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

View full text Add to dashboard Cite

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Time Complexity (Sec)mentioning

confidence: 99%

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Several studies proposed to use Support Vector Machine (SVM) classifiers, such as [6][7][8][9][10], and [11]. artificial neural networks, such as [12,13] and [14], and other ML algorithms, as in [15][16][17][18][19], and [20]. Interestingly, reinforcement learning has been also adopted to perform DDoS mitigation in paper [21].…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

et al. 2021

View full text Add to dashboard Cite

Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of $$\upmu \text {s}$$ μ s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language. Graphic Abstract

show abstract

“…Critical infrastructures are vulnerable to a wide range of cyberattacks, which have a significant economic impact on organizations and service providers. Cyberattacks such as distributed denial of service cause the service to be unavailable for its intended clients [7]. The dictionary is another common attack against remote access services; it is used to crack a password in a dictionary or word list, which allows attackers to hijack the system remotely [8].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Industrial Internet of Things Cyberattacks

Alanazi¹,

Aljuhani²

2023

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates' feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

show abstract

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Cited by 90 publications

References 138 publications

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

Anomaly Detection for Industrial Internet of Things Cyberattacks

Contact Info

Product

Resources

About