Search

In recent years, examination of the social media networks has become an integral part of investigations. Law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process needs collection and analysis of the information which is immense, heterogeneous, and spread across multiple social networks. This process is technically intricate due to heterogeneous and unstructured online social networks (OSNs). Hence, creating cognitive challenges and massive workloads for the investigators. Therefore, it is imperative to develop automated and reliable solutions to assist investigators. Capturing the forensic information in the structured form is crucial for automation, sharing, and interoperability. This paper introduces the design of a multi-layer framework; from collection to evidence analysis. The central component of this framework is a hybrid ontology approach that involves multiple ontologies to manage the unstructured data and integrate various social media data collections. This approach aims to find the evidence by automated methods that are trustworthy and therefore admissible in a court of law. [ABSTRACT FROM AUTHOR]

Social Media (SM) evidence is a new and rapidly emerging frontier in digital forensics. The trail of digital information on social media, if explored correctly, can offer remarkable support in criminal investigations. However, exploring social media for potential evidence and presenting these proofs in court is not a straightforward task. Social media evidence must be collected by a legally and scientifically appropriate forensic process and also coincide with the privacy rights of individuals. Following the legal process is a challenging task for legal practitioners and investigators due to the highly dynamic and heterogeneous nature of social media. Forensic investigators can conduct effective investigations and collect legally sound evidence efficiently if they are provided with sophisticated tools to manage the diversity and size of social media content. This article explains the current state of evidence acquisition, admissibility, and jurisdiction in social media forensics. It also describes the immediate challenges for the collection, analysis, presentation, and validation of social media evidence in legal proceedings. Furthermore, the research gaps in the domain and few research objectives with potential research directions are presented. [ABSTRACT FROM AUTHOR]

Abstract: Given the rapidly increasing amount of digital crimes, network forensics plays a significant role in the process of analyzing evidence. Current cyber crime investigation techniques are very costly and time consuming because much effort is needed to analyze the overwhelming amount of evidence in each case. More information is required to understand and analyze the factors involved in cyber crime, such as the intention of the crime. This paper proposes a new algorithm called the Similarity of Attack Intention (SAI), which uses cosine similarity as a distance-based similarity measure to estimate similar cyber crime intentions. The algorithm is based on the Attack Intention Analysis (AIA) algorithm to predict new cyber crime intentions and assigned the probability values for these intentions. A similarity metric for the new cyber crimes intentions with others is generated in order to identify the similar intentions. The evaluation of example cyber crime results shows that our proposed algorithm provides better solutions and increases the possibility value of evidences. [Copyright &y& Elsevier]