Identification and Analysis of Malicious Applications

The growing sophistication in cyber threats, particularly malice in URLs, and Android malware necessitates more progressive features in the detection mechanisms for protecting user and data security. This review paper is an integrated analysis of two key domains: malicious URL detection and Android malware detection, keeping feature extraction techniques and machine learning models used in both the domains under study. Compared to other works on malicious URL detection based on URL-based features, focusing on lexical, host-based, and content-based analysis, our work mainly draws upon APIbased feature extraction through API sequences and clustering related to Android malware detection. In addition, more comprehensive datasets result in improvement with Support Vector Machine algorithms combined with heuristic methods used to handle problem identification and analysis, which increase the efficiency by 85%+. This paper proposes a unified framework that highlights shared challenges and techniques for improving the detection across platforms. We identify gaps in literature and some future directions for advancing cross-platform systems. Keywords: Malicious URL Detection, Android Malware, Feature Extraction, SVM, Heuristic Methods, Cybersecurity.