Towards An Automated Forensic Examiner (AFE) Based Upon Criminal Profiling & Artificial Intelligence

Digital forensics plays an increasingly important role within society as the approach to the identification of criminal and cybercriminal activities. It is however widely known that a combination of the time taken to undertake a forensic investigation, the volume of data to be analysed and the number of cases to be processed are all significantly increasing resulting in an ever growing backlog of investigations and mounting costs. Automation approaches have already been widely adopted within digital forensic processes to speed up the identification of relevant evidence – hashing for notable files, file signature analysis and data carving to name a few. However, to date, little research has been undertaken in identifying how more advanced techniques could be applied to perform “intelligent” processing of cases. This paper proposes one such approach, the Automated Forensic Examiner (AFE) that seeks to apply artificial intelligence to the problem of sorting and identifying relevant artefacts. The proposed approach utilises a number of techniques, including a technical competency measure, a dynamic criminal knowledge base and visualisation to provide an investigator with an in depth understanding of the case. The paper also describes how its implementation within a cloud based infrastructure will also permit a more timely and cost effective solution

E- Crime Behaviour of Internet Users

Electronic crime or cyber crime refers to crimes that can only be committed using information technology such as phishing, data theft and payment fraud. Software called crime ware makes it easy to find and target the victims. E-commerce websites in particular are often seen as the "sweet spots", especially by organized criminals. Whilst some one-off attacks may be the result of disgruntled customers and organized attacks are more likely to be undertaken internally by staff or externally by organized criminals. This paper compiles e-crime nature, types, provides detailed review of work done in e-crime prevention and gives analysis of e-crime behavior of internet users based on a primary survey

Automated Digital Forensics and Computer Crime Profiling

Edited version embargoed until 01.12.2017 Full version: Access restricted permanently due to 3rd party copyright restrictions. Restriction set on 08.12.2016 by SC, Graduate SchoolOver the past two decades, technology has developed tremendously, at an almost exponential rate. While this development has served the nation in numerous different positive ways, negatives have also emerged. One such negative is that of computer crime. This criminality has even grown so fast as to leave current digital forensic tools lagging behind in terms of development, and capabilities to manage such increasing and sophisticated types of crime. In essence the time taken to analyse a case is huge and increasing, and cases are not fully or properly investigated. This results in an ever-increasing number of pending and unsolved cases pertaining to computer crime. Digital forensics has become an essential tool in the fight against computer crime, providing both procedures and tools for the acquisition, examination and analysis of digital evidence. However, the use of technology is expanding at an ever-increasing rate, with the number of devices a single user might engage with increasing from a single device to 3 or more, the data capacity of those devices reaching far into the Terabytes, and the nature of the underlying technology evolving (for example, the use of cloud services). This results in an incredible challenge for forensic examiners to process and analyse cases in an efficient and effective manner. This thesis focuses upon the examination and analysis phases of the investigative process and considers whether automation of the process is possible. The investigation begins with researching the current state of the art, and illustrates a wide range of challenges that are facing the digital forensics investigators when analysing a case. Supported by a survey of forensic researchers and practitioners, key challenges were identified and prioritised. It was found that 95% of participants believed that the number of forensic investigations would increase in the coming times, with 75% of participants believing that the time consumed in such cases would increase. With regards to the digital forensic sophistication, 95% of the participants expected a rise in the complexity level and sophistication of digital forensics. To this end, an automated intelligent system that could be used to reduce the investigator’s time and cognitive load was found to be a promising solution. A series of experiments are devised around the use of Self-Organising Maps (SOMs) – a technique well known for unsupervised clustering of objects. The analysis is performed on a range of file system and application-level objects (e.g. email, internet activity) across four forensic cases. Experiment evaluations revealed SOMs are able to successfully cluster forensic artefacts from the remaining files. Having established SOMs are capable of clustering wanted artefacts from the case, a novel algorithm referred to as the Automated Evidence Profiler (AEP), is proposed to encapsulate the process and provide further refinement of the artefact identification process. The algorithm led to achieving identification rates in examined cases of 100% in two cases and 94% in a third. A novel architecture is proposed to support the algorithm in an operational capacity – considering standard forensic techniques such as hashing for known files, file signature analysis, application-level analysis. This provides a mechanism that is capable of utilising the A E P with several other components that are able to filter, prioritise and visualise artefacts of interest to investigator. The approach, known as Automated Forensic Examiner (AFE), is capable of identifying potential evidence in a more efficient and effective manner. The approach was evaluated by a number of experts in the field, and it was unanimously agreed that the chosen research problem was one with great validity. Further to this, the experts all showed support for the Automated Forensic Examiner based on the results of cases analysed