Iranihindia martellata (Senior-White, 1924) is recorded from peninsular Malaysia for the first time. Male and female specimens in the recent collections of forensically important sarcophagid flies were examined and identified based on morphology and DNA sequencing analysis. Male genitalia offer unambiguous species identification characteristics in the traditional taxonomy of flesh flies but the female flies are very similar to one another in general morphology. Female of I. martellata was determined by DNA sequencing (COI and COII) and PCR-RFLP (COI) analysis. Identified females were carefully examined and compared with the morphologically similar species, Liopygia ruficornis (Fabricius, 1794). Female genitalia are re-described and illustrated in this paper.
Fannia prisca Stein, 1918 is newly recorded from peninsular Malaysia. This record is based on 4 male specimens from Mount Berembun, Brinchang, Cameron Highland, Pahang state, peninsular Malaysia. It is previously recorded from China, Mongolia, Korea, Japan, Taiwan, Bonin Island, Thailand and oriental region. The male of Fannia prisca can be differentiated from male Fannia scalaris by the following features: for F. prisca, mid-coxa without spine; mid-tibia normal or without stout triangular ventral projection; and hind tibia usually with 2 av, while F. scalaris has several stout hook-like spines on the anterior margin; mid-tibia with stout triangular ventral projection and hind tibia usually with 3 av. Both F. prisca and F. scalaris can be differentiated from Fannia leucosticta by looking at its hind tibia, which only has 1 av.
With the rapid development of technology, mobile phones have become an essential tool in terms of crime fighting and criminal investigation. However, many mobile forensics investigators face difficulties with the investigation process in their domain. These difficulties are due to the heavy reliance of the forensics field on knowledge which, although a valuable resource, is scattered and widely dispersed. The wide dispersion of mobile forensics knowledge not only makes investigation difficult for new investigators, resulting in substantial waste of time, but also leads to ambiguity in the concepts and terminologies of the mobile forensics domain. This paper developed an approach for mobile forensics domain based on metamodeling. The developed approach contributes to identify common concepts of mobile forensics through a development of the Mobile Forensics Metamodel (MFM). In addion, it contributes to simplifying the investigation process and enables investigation teams to capture and reuse specialized forensic knowledge, thereby supporting the training and knowledge management activities. Furthermore, it reduces the difficulty and ambiguity in the mobile forensics domain. A validation process was performed to ensure the completeness and correctness of the MFM. The validation was conducted using two techniques for improvements and adjustments to the metamodel. The last version of the adjusted metamodel was named MFM 1.2.
Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.