Summary Among the recent network security issues, Distributed Denial of Service (DDoS) attack is one of the most dangerous threats in today's cyberspace that can disrupt essential services. These attacks compromise network security by flooding the target with malicious traffic. Several researchers have designed effective DDoS detection mechanisms using machine learning (ML) and deep learning (DL)‐based techniques. However, existing detection approaches paid less attention to issues such as class imbalance, multi‐classification, or computational cost of the models, especially time. In this study, we propose a novel framework for detecting and classifying DDoS attacks with high accuracy and low computational cost. To address the class imbalance, we employ random sampling, while feature selection techniques such as low information gain, quasi‐constant elimination, and principal component analysis are utilized for optimal feature selection and reduction. Our proposed CNN‐based model achieves outstanding performance, boasting an accuracy of 99.99% for binary classification and 98.44% for multi‐classification. The proposed model is compared to existing works and baseline line models and found to be effective in binary and multi‐classification.

Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.