The authentication of digital video recording plays a very important role in forensic science as well as for other crime investigation purposes. The field of forensic examination of digital video is continuously facing new challenges. At present the authentication of the video is carried out on the basis of pixel-based analysis. Due to the change in technology, it was felt that a new approach is required for the authentication of digital video recordings. In the present work a new approach i.e. analysis of media Information and structural analysis of video containers (boxes/ atoms) of mp4 file format have been applied for identification of original and edited videos. This work is limited only for Mp4 file format because the MP4 compressed format is widely used in most of the mobile phone for video recording and transmission purposes. For this purpose, we recorded more than 200 video samples using more than 20 different mobile phones of different make and models and more than 12 video editors, which are available in open source used for editing purpose. The original and edited MP4 video files were analyzed for their different metadata and structural contents analysis of different file containers (boxes/atoms) using different freeware tools. The details of the work are described below.
Shill bidding is a fraudulent act whereby a seller places bids on his/her own auction to drive up the final price for the winning bidder. eBay currently masks bidder usernames in auctions and has adopted a 30-Day Bid Summary that restricts the availability of data on the previous auctions a bidder has participated in. This has made shill bidding detection more difficult, as tracking users across multiple auctions is essential in identifying shills. This paper presents two pieces of software to aid in the detection of shill bidding in light of masked bidder usernames and limited bid history data. First, we propose the Auction Data Collector to automatically acquire vital eBay auction data required for shill bidding detection. Second, we propose the BidderLinker Algorithm, which links bidders across multiple auctions they have bid on with the same seller by utilising the 30-Day Bid Summary. The 30-Day Bid Summary provides information about bidders that can be used to uniquely identify them, even if their usernames are masked. This allows for shill detection algorithms to be applied to the data gathered from multiple auctions with the same seller, where previously this was not possible.
The emergence and rapid development in complexity and popularity of Android mobile phones has created proportionate destructive effects from the world of cyber-attack. Android based device platform is experiencing great threats from different attack angles such as DoS, Botnets, phishing, social engineering, malware and others. Among these threats, malware attacks on android phones has become a daily occurrence. This is due to the fact that Android has millions of user, high computational abilities, popularity, and other essential attributes. These factors influence cybercriminals (especially malware writers) to focus on Android for financial gain, political interest, and revenge. This calls for effective techniques that could detect these malicious applications on android devices. The aim of this paper is to provide a systematic review of the malware detection techniques used for android devices. The results show that most detection techniques are not very effective to detect zero-day malware and other variants that deploy obfuscation to evade detection. The critical appraisal of the study identified some of the limitations in the detection techniques that need improvement for better detection.
