DDoS Attacks Detection Using GA Based Optimized Traffic Matrix

Lee, Je Hak; Kim, Dong-Seong; Lee, Sang Min; Park, Jong Sou

doi:10.1109/imis.2011.116

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Hence, it is crucial to identify DDoS assaults at an early stage. This study presents an advanced method for detecting DDoS attacks [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Talpur,

Korejo,

Chandio

et al. 2024

Sensors

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The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

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“…Hence, it is crucial to identify DDoS assaults at an early stage. This study presents an advanced method for detecting DDoS attacks [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Talpur,

Korejo,

Chandio

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Lee et al [41] proposed an approach which provides a defensive mechanism against DDoS attack by using a traffic matrix. In this work, they proposed an improved attack detection model that enhances the traffic matrix construction process and some particular parameters were optimized using the genetic algorithm (GA).…”

Section: Genetic Algorithm Genetic Algorithm (Ga) Is Another Metaheuristic Technique That Work On the Concept Of Theory Of Evolutionmentioning

confidence: 99%

“…Lee et al [41] Genetic algorithms DARPA 2000, LBL-PKT-4 is proposed model is designed for the detection of distributed denial of service attack using a traffic matrix and optimizes some features of the traffic matrix by using GA.…”

Section: Genetic Algorithms Kddcup99mentioning

confidence: 99%

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

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Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

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“…The SICLN is a supervised version of the ICLN. In [15], the writers propose an enhanced DDoS attacks detection approach by optimizing the parameters of the traffic matrix using a Genetic Algorithm (GA) to maximize the detection rates. As we can see, intelligent detection is widely used in network area and is one of the most important methods of traffic anomaly detection with good development prospects.…”

Section: Intelligent Detection Based On Machine Learning and Datamentioning

confidence: 99%

An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks

Jibin

Jiang

2016

International Journal of Distributed Sensor Networks

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Traffic anomaly detection is emerging as a necessary component as wireless networks gain popularity. In this paper, based on the improved Autoregressive Integrated Moving Average (ARIMA) model, we propose a traffic anomaly detection algorithm for wireless sensor networks (WSNs) which considers the particular imbalanced, nonstationary properties of the WSN traffic and the limited energy and computing capacity of the wireless sensors at the same time. We systematically analyze the characteristics of WSN traffic, the causes of WSN abnormal traffic, and the latest related research and development. Specifically, we improve the traditional time series ARIMA model to make traffic prediction and judge the traffic anomaly in a WSN. Simulated and real WSN traffic data gathered from University of North Carolina are used to carry out simulations on Matlab. Simulation results and comparative analyses demonstrate that our proposed WSN traffic anomaly detection scheme has better anomaly detection accuracy than traditional traffic anomaly detection algorithms.

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DDoS Attacks Detection Using GA Based Optimized Traffic Matrix

Cited by 7 publications

References 13 publications

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks

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