A Parallel Rank Based Multi-Class Ensemble Classification Framework on ISOT Cyber Threat Detection
Keywords:
Multi-class classification, data filtering, outlier detection, cyber-attack detectionAbstract
A group of internet-connected compromised devices forming a network is called a Botnet. This network can consist of personal computers, servers, IoT devices, and mobile devices. Botnets are one of the most common network security threats, used for malicious activities such as data theft, spamming, collecting personal information from users, and launching Distributed Denial of Service (DDoS) attacks. The growing popularity of IoT and mobile devices has made them an attractive target for attackers, as they often have unpatched security vulnerabilities. In today's world, computer networks play a crucial role in the information and communication technology era, connecting heterogeneous devices for data communication and sharing. However, the large number of Internet-connected devices makes them vulnerable to massive security attacks. Most widely-used IoT devices lack security design, making them vulnerable to recent attacks that exploit these weaknesses and recruit the devices to cause severe harm. Parallel multi-class classification refers to the process of performing classification tasks simultaneously on multiple classes or categories of data using parallel computing techniques. In traditional multi-class classification, a model is trained to classify data into one of several mutually exclusive classes. However, in some scenarios, it may be advantageous to perform these classifications in parallel, especially when dealing with a large number of classes or when speed and efficiency are crucial. Parallel multi-class classification can be implemented using parallel processing or distributed computing frameworks to train and evaluate multiple classifiers concurrently.
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