Guided missiles involve the use of a conventional deviated pursuit course like proportional navigation algorithm and its variants, which is optimal when the speed advantage of the guided missile is veryhighandthe targetmaneouvering is minimal. Against the present-day aircraft, which employs fly-by-wire technology for high maneouverability and high speed, missiles needto have a much higher speed advantage or to use a combination of artificial intelligence and modern control algorithms. Results of simulation of pursuit and evasion with an autonomous intelligent agent incorporated in the control loop are presented.
The recent technological advancement with less implementation cost has completely changed the scenario of information being generated, stored, or manipulated digitally by using the storage devices. Today the criminal activities are directly or indirectly associated with the storage devices, thus it is now recognized as an essential evidence in court-of-law throughout the world. However, the massive capacities of modern storage devices significantly increase the time and effort required to preserve and analyze the evidence. This paper proposes a methodology based on the random sector sampling and k-means clustering approaches to efficiently identify and evaluate the significant data regions instead of the entire storage drive. The random sampling and clustering methods efficiently reveal the unseen resident data pattern and intelligence about the regions of the drive which may be of investigator's interest. Experiments involving storage drives of various capacities demonstrate the efficacy of the methodology. Moreover, we generalize our discussion to represent that the extracted hidden patterns of storage drive data can assist an investigator in achieving the desired performance requirements. KEYWORDS k-means clustering, large storage drives, random sector sampling, selective examination, significant regions identification, storage drive forensics, stored data pattern 1 Security Privacy. 2018;1:e40.wileyonlinelibrary.com/journal/spy2
The ever increasing capacity of storage devices is becoming a formidable obstruction to the digital forensic community due to its substantial investigation time and preservation space requirements. However, the investigation process becomes more complicated, whenever the fragmented or partially overwritten drives need forensic consideration. It is becoming extremely necessary to unfold novel methods the examination of storage drives involving a bulk volume of data. In this paper, the differential evolution (DE) technique is utilized with an unsupervised mean-shift clustering approach in the field of digital forensics for intelligently locating the traces of target file in suspected storage drives or raw images. The proposed methodology leverages the drives' geometrical information in DE for obtaining random sector samples followed by fitness value and sector hash computation. The clustering algorithm evaluates the obtained intelligence and assists in estimating the regions of forensic significance in the drive, instead of considering the entire drive contents. As an outcome, a proof-of-concept Python based command line tool has been developed and released to support the study and further enhancement. The experiments and case studies demonstrated using the drives of different capacities demonstrates the efficacy of the proposed method.
K E Y W O R D Sdifferential evolution, digital forensics, huge data volumes, mean shift clustering, storage drive, target file 1 Security Privacy. 2019;2:e71.wileyonlinelibrary.com/journal/spy2
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.