Fast and accurate classifying model for denial-of-service attacks by using machine learning

Kareem, Mohammed Ibrahim; Jasim, Mahdi Nsaif

doi:10.11591/eei.v11i3.3688

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Kareem et al [28] conducted an assessment of the efficiency of rapid ML techniques for model testing and generation within communication networks, with a focus on identifying denial-of-service attacks. The CICIDS2017 dataset in the WEKA tool served as the training and testing ground for multiple ML algorithms.…”

Section: A Machine Learning Approachesmentioning

confidence: 99%

“…The evaluated methods included REP tree (REPT), random tree (RT), RF, decision stump (DS), and J48. Performance metrics such as accuracy, F −score, precision, and recall were employed by Kareem et al [28]. Their experiments revealed that J48 exhibited superior performance and quicker testing times, especially when utilizing 4-8 features.…”

Section: A Machine Learning Approachesmentioning

confidence: 99%

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A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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Internet security is under serious threat due to Distributed Denial of Service (DDoS) attacks. These attacks inflict considerable damage by disrupting network services, resulting in the impairment and complete disablement of system functions. The accurate classification and detection of DDoS attacks is extremely important. We provide a review of different models of Machine Learning (ML)/Deep Learning (DL)-based DDoS attack detection used by researchers that consider different classifiers. Our analysis indicates a heightened emphasis on ML-based classifiers where 22% of studies opted for the widely recognized SVM classifier. For DL-based, 27% of the studies opted for the widely recognized CNN. While the majority of researchers have formulated their datasets, NSL-KDD was employed in 55% of the studies. In addition, we discussed the future directions and challenges of DDoS detection.

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Section: A Machine Learning Approachesmentioning

confidence: 99%

Section: A Machine Learning Approachesmentioning

confidence: 99%

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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“…IRC-based bots have a huge knowledge and code base, allowing their developers to reuse it to build new botnets, like the Agobot versions. Agobot's code is neatly organized and readily available online, making it simple for botnet authors to build their networks [9].…”

Section: Related Workmentioning

confidence: 99%

Smart Approach for Botnet Detection Based on Network Traffic Analysis

Obeidat

Yaqbeh

2022

Journal of Electrical and Computer Engineering

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Today, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. By categorizing behavior based on time slots, we investigate the viability of detecting botnet behavior without seeing a whole network data flow. We also evaluate the efficacy of two well-known classification methods with reference to this data. We demonstrate experimentally, using existing datasets, that it is possible to detect botnet activities with high precision.

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“…Therefore, IoT users consequently have paid great attention to the vulnerabilities. Numerous devices or systems work together to attack a single target, making it challenging to locate and disable the attacking devices [11][12][13][14][15]. Cyberattackers frequently use a botnet to interfere with internet infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Safarov,

Basak,

Nasimov

et al. 2023

Future Internet

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In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among the numerous cyber threats, denial of service (DoS) and distributed denial of service (DDoS) attacks pose significant risks, as they can render websites and servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying and mitigating these attacks due to their widespread nature, intricate patterns, and computational complexities. However, by harnessing the power of deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy in detecting and classifying DoS and DDoS attacks. The successful implementation of our proposed framework addresses the challenges posed by imbalanced data and exhibits its potential for real-world applications. By leveraging the dense channel-spatial attention mechanism, our model can precisely identify and classify DoS and DDoS attacks, bolstering the cybersecurity defenses of websites and servers. The high accuracy rates achieved across different datasets reinforce the robustness of our approach, underscoring its efficacy in enhancing intrusion detection capabilities. As a result, our framework holds promise in bolstering cybersecurity measures in real-world scenarios, contributing to the ongoing efforts to safeguard against cyber threats in an increasingly interconnected digital landscape. Comparative analysis with current intrusion detection methods reveals the superior performance of our model. We achieved accuracy rates of 99.38%, 99.26%, and 99.43% for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. These remarkable results demonstrate the capability of our approach to accurately detect and classify various types of DoS and DDoS assaults. By leveraging the inherent strengths of deep learning, such as pattern recognition and feature extraction, our model effectively overcomes the limitations of traditional methods, enhancing the accuracy and efficiency of intrusion detection systems.

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Fast and accurate classifying model for denial-of-service attacks by using machine learning

Cited by 11 publications

References 22 publications

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Smart Approach for Botnet Detection Based on Network Traffic Analysis

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

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