Big Data & DDoS ATTACKS: A Discussion of Ensemble Algorithms to Detect Cyber Attacks  

作　　者：Anja Housden-Brooks Anja Housden-Brooks(Anglia Ruskin University, Cambridge, UK)

机构地区：[1]Anglia Ruskin University, Cambridge, UK

出　　处：《Journal of Computer and Communications》2024年第12期246-265,共20页电脑和通信(英文)

摘　　要：The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that�The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that�

关 键 词：K-Means Clustering The KNN Algorithm PySpark Ensemble Learning Methods DDoS Attacks Veracity Malicious Traffic Alert Systems 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]