Review of an Enhanced Authentication Strategy for Multiservice Authorization over Mobile Cloud

Cloud computing has changed the corporate as well as educational industry since it was introduced. The cloud computing is basically cost effective, convenient and on demand service offered to the end users. For instance it lead to cost reserve funds as well as better resource usage and removing the need of specialized technical skill for the users. There exists a huge security concern when utilizing cloud services. The security is extremely vital in cloud computing since individuals and organizations store private information in the cloud and it should likewise be not difficult to utilize the services provided to users. Since the control of services and information required for the regular run of a organization is handled by third party service providers, End user needs to believe the third party cloud service providers and trust that they handle their information in a right way and resources are available as and when needed. There are many approaches proposed for authentication in cloud services. They are intricate, insecure or highly exclusive. In this Paper we have carried of the comparative study of different authentication schemes in cloud computing finally summarize on the basis of different evaluation criteria

Sequence Pattern Mining with Variables

Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than distinct items for each color. It demonstrates this by incorporating variables into Discontinuous Varied Order Sequence Mining (DVSM). The second contribution is the creation of Sequence Mining of Temporal Clusters (SMTC), a new SPM that addresses the interleaving issue common to SPM algorithms. Most SPM algorithms address interleaving by using a distance measure to separate co-occurring sequences. SMTC addresses interleaving by clustering all subsets of temporally close items and deferring the sequencing of mined patterns until the entire dataset if examined. Evaluation of the SPM algorithms on a digital forensics media analysis task results in a 96% reduction in terms to review, 100% detection of true positives and no false positives

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings