Abstract
Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.
Highlights
The data stored on the internet is growing day by day for threats that target sensitive or crucial data, and this has raised many security issues, such as malicious intrusions [1]
Random Forest and Multi-Layer Perceptron were used to detect an attack in real-time and evaluate the performance with and without the big data approach
Spark Machine learning (ML) libraries were used to evaluate the performance with the big data approach and Scikit ML on Google Colab [58] libraries for the non-big data approach
Summary
The data stored on the internet is growing day by day for threats that target sensitive or crucial data, and this has raised many security issues, such as malicious intrusions [1]. Detection Systems (IDS) to mitigate the risk of malicious intrusion attacks [2]. Sustainability 2021, 13, 10743 the data can be extracted from information retrieval models as well as information extraction of any kind [3,4]. Traditional intrusion detection techniques can only work best on slow-speed data or small data. One major attack is the DDoS attack. DDoS attacks are cyber-attacks on specific servers or network with the intended purpose of disrupting that network or server’s normal operation [6]
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