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Development and exploitation of technology have led to the further expansion and complexity of digital crimes. On the other hand, the growing volume of data and, subsequently, evidence is a severe challenge in digital forensics. In recent years, the application of machine learning techniques to identify and analyze evidence has been on the rise in different digital forensics domains. This paper offers a systematic literature review of the research published in major academic databases from January 2010 to December 2021 on the application of machine learning in digital forensics, which was not presented yet to the best of our knowledge as comprehensive as this. The review also identifies the domains of digital forensics and machine learning methods that have received the most attention in the previous papers and finally introduces remaining research gaps. Our findings demonstrate that image forensics has obtained the greatest benefit from using machine learning methods, compared to other forensic domains. Moreover, CNN-based models are the most important machine learning methods that are increasingly being used in digital forensics. We present a comprehensive mind map to provide a proper perspective for valuable analytical results. Furthermore, visual analysis has been conducted based on the keywords of the papers, providing different thematic relevance topics. This research will give digital forensics investigators, machine learning developers, security researchers, and enthusiasts a broad view of the application of machine learning in digital forensics., Comment: 99 pages, 10 figures, 3 tables

Telecare medical information systems are gaining rapid popularity in terms of providing the delivery of online health-related services such as online remote health profile access for patients and doctors. Due to being installed entirely on Internet, these systems are exposed to various security and privacy threats. Hence, establishing a secure key agreement and authentication process between the patients and the medical servers is an important challenge. Recently, Khatoon et.al proposed an ECC-based unlink-able authentication and key agreement method for healthcare related application in smart city. In this article, we provide a descriptive analysis on their proposed scheme and prove that Khatoon et al.'s scheme is vulnerable to known-session-specific temporary information attack and is not able to provide perfect forward secrecy., Comment: 5 pages, 1 table, 1 figure

Advances in the sensing capabilities of smartphones have resulted in the emergence of participatory sensing. In participatory sensing, ordinary citizens are recruited to collect sensor data from nearby environments which are then analysed to provide useful information. The information credibility is predominantly dependent on sufficient participation. There are however, various costs associated with contributing data including time, phone battery and bandwidth consumption and potential exposure to privacy threats. These issues may dissuade participants from contributing, thus decreasing the data quality. The integration of social networks with participatory sensing, referred to as social participatory sensing is a potential solution since it provides access to social network members as participants. This integration however, raises new challenges. First, is the potential sparseness of the requester's friendship graph which affects the ability to recruit sufficient contributors. Second, is the identification of well-suited participants who can fulfil the task's requirements. Third, is assessing the trustworthiness of provided contributions.In this thesis, we propose an innovative framework comprising novel strategies that address the aforementioned issues. We first present a recruitment scheme that addresses the participation sufficiency issue by utilising friendship relations to provide access to adequate participants. The scheme also identifies credible communication paths to preserve the integrity and privacy of messages. Next, we design a participant selection scheme to select well-suited participants from a wider pool. Our scheme also prevents collusion among the selected group. Finally, we present a trust assessment scheme for comprehensive trust evaluation encompassing all personal and social influential parameters. The trust scores are then used to update the participants' reputations. The proposed ideas have been experimentally validated on real-world datase

Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A reputation score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate reputation scores.

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture., Comment: accepted in DCOSS 2013