MITD-Net: Markov image-based threat detection network

The increasing sophistication of malicious activities within applications emphasizes the need for advanced predictive technologies. Malicious user behavior (MUB) is a concern in organizations, as it is a significant source of security breaches caused by employees within the organization. Although previous studies in user activity detection have demonstrated some success, these technologies have been insufficient in identifying new or unfamiliar security threats. To improve the detection of insider threats, this study introduces MITD-Net, a novel method based on a MobileNet convolutional neural network (CNN) architecture to predict the MUB effectively and efficiently. MITD-Net is faster and accurate than its counterparts, leveraging the computational efficiency and adaptability of deep neural networks in low-resource environments. Our model addresses the challenge of predicting harmful behavior. MITD-Net contributes to the proactive identification and mitigation of potential threats, thereby enhancing overall system security. The proposed method aims to extract features from the CERT r4.2 dataset, converting them into a Markov image to detect the MUB from authorized parties. Experimental evaluations conducted on CERT r4.2 datasets demonstrate the effectiveness of the proposed model. Moreover, this paper compares the results of previous studies. The experimental findings show that the proposed approach outperforms or achieves state-of-the-art techniques. Ablation studies were also performed to evaluate the significance of each individual component of the model.