Currently, we saw the increment trend of mobile application(app) exploitation that leads to loss of confidential information and money. Many malware camouflages itself as a genuine mobile app or exploits vulnerabilities inside mobile apps. Hence, this paper presents a mobile app called CallDetect that detects Android Application Interface (API) exploitation for call logs inspired by apoptosis. Apoptosis is known as cell-programmed death, and it is part of the human immunology system. Once it suspects any danger that might cause any harm to the human body, it will kill the suspected danger and itself. In the case of CallDetect, it will scan and uninstall the potentially malicious mobile application on a mobile phone. CallDetect consists of 13 new classifications of API call log, which are used as the database for CallDetect. These classifications were built by using static analysis and open source tools in a controlled lab environment. There were 5560 training datasets from Drebin and 550 anonymous testing dataset from Google Playstore. Our finding showed that 39 mobile apps, or 7%, were identified with possible call log exploitation. This paper can be used as a reference for call log API exploitation and can be further enhanced by integrating it with permission and system call exploitation.

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Cyber-attacks such as ransomware, data breaches, and phishing triggered by malware, especially for iOS (iPhone operating system) platforms, are increasing. Yet not much works on malware detection for the iOS platform have been done compared to the Android platform. Hence, this paper presents an iOS malware classification inspired by phylogenetics. It consists of mobile behaviour, exploits, and surveillance features. The new iOS classification helps to identify, detect, and predict any new malware variants. The experiment was conducted by using hybrid analysis, with twelve (12) malwares datasets from the Contagio Mobile website. As a result, twenty-nine (29) new classifications have been developed. One hundred (100) anonymous mobile applications (50 from the Apple Store and 50 from iOS Ninja) have been used for evaluation. Based on the evaluation conducted, 13% of the mobile applications matched with the developed classifications. In the future, this work can be used as guidance for other researchers with the same interest.

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