A semanticbased methodology for digital forensics analysis Abstract   Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cybersecurity. Investigators use a variety of techniques and proprietary software forensics applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully analysed and documented in a xe2x80x9cfinding reportxe2x80x9d in preparation for legal proceedings that involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of fraudulent activities. In this work, a new methodology is proposed to support investigators during the analysis process, correlating evidence found through different forensics tools. The methodology was implemented through a system able to add semantic assertion to data generated by forensics tools during extraction processes. These assertions enable more effective access to relevant information and enhanced retrieval and reasoning capabilities.

On the Efficacy of Using Android Debugging Bridge for Android Device Forensics Mobile device forensics is an expanding field. As mobile devices permeate society, the ability to effectively extract forensic data from such devices is critical. Malware for mobile devices is also a growing threat. The efficacy of forensic tools is of critical importance to both researchers and practitioners. Evaluating a wide range of tools enables the selection of the appropriate tool for a given investigation. One method of evaluating forensic tools is to analyze the tools ability to detect mobile malware. Many commercial and open source forensic tools do not effectively find malware on a mobile device. This gap in capabilities requires a manual approach to extracting such data. This current study documents an experimental study to determine the efficacy of specific commercial tools in identifying malware on an Android phone. Then a methodology for utilizing the Android Debugging Bridge to identify suspect files that have a high probability of being malware. That methodology is expanded into a generalized forensic analysis approach utilizing the Android Debugging Bridge.)