Detecting the penetration of malicious behavior in big data using hybrid algorithms - Wang, Yue; Shi, Yan (Signal, image and video processing, 2024)
Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusions into networks and information systems is one of the major issues in this subject. It is imperative and important to enhance intrusion detection skills in order to address malevolent behavior in large data. This paper presents a scalable approach to harmful data detection. Three variables have been considered in this strategy and model: scalability, user review, and temporal progress. High volumes of data can be processed using this technology. Time is split into time periods for data training in this system, and each time interval uses users’ review information to train the data. Large volumes of data require sophisticated strategies to handle, and scalability in storage allows for faster processing and fewer computations. This approach is a kind of hardware–software hybrid solution for malware detection. A fresh approach to feature extraction has also been applied. In the suggested method, the bacteria algorithm in conjunction with the immune system algorithm has been utilized for the prediction operation, and the modified support vector machine algorithm and optical density have been utilized for classification. Based on the findings, the suggested combination algorithm outperforms other comparable techniques with a 21% detection rate, a 62% false alarm rate, a 15% accuracy rate, and a 73% training duration.