Data recovery is a significant problem that presents a real challenge to forensics investigators today. File carvers have traditionally helped mitigate these difficulties. However, two issues still present significant challenges -1) Prior knowledge of file types is required for building file carvers, and 2) fragmentation prevents file carvers from successful recovery. In previous research, we proposed a framework for recovering deleted files without prior knowledge of file types and with the existence of fragmentation. In this paper, we introduce the design and a functioning implementation of our system by modifying an exFat filesystem running on top of FUSE. Evaluation of the overhead of our filesystem shows only a 5% decrease in performance in write operations when compared to an unmodified exFat filesystem, and almost identical read measurements. Our system also shows significantly better recovery rates in the presence of fragmentation when compared to two selected file carvers.

Solar energy is getting a lot of traction due to the reduced cost and friendlier to the environment compared to fossil fuel. It is essential to inspect the PV farms to ensure that the correct capacity produced through early PV fault detection. We proposed a full autonomous solution, where the drone mission is programmed to follow a specific Global Positioning System (GPS) waypoints. The collected videos will undergo various image processing techniques to detect and track the PV panels. In this paper, we tried two different PV panel detection approaches. Both detections gave acceptable results. The first detection relies on various image processing techniques. The second detection relies on deep learning architecture called mask Region-based Convolution Neural Network (R-CNN). After that, we track the PV panels in every frame using camera data alone. The advantage of tracking the PV panels is to ensure unrepeated PV panel through tagging even if the drone flies over the panel again since each PV panel will be associated with a tag. The next step will be to test the PV panel’s proposed detection and tracking algorithm on a larger solar farm.

Abstract-Recovering deleted information from a hard disk has been a long standing problem. The computer forensics community has addressed information recovery through the development of file carving techniques. Two issues, however, still present significant challenges to their on-going efforts -1) Prior knowledge of file types is required for building file carvers including file headers and footers, and 2) fragmentation prevents file carvers from successful recovery. As a solution, we propose a forensics file system that embeds a special identifier in every cluster that is either currently allocated or was in the past. The identifier keeps track of every cluster mapping the clusters to a single file irrespective of the file status -existing or deleted. We modified an exFAT implementation on FUSE to implement our forensics file system. We also propose a hashing mechanism that can detect malicious or accidental manipulation of a cluster's identifier. In addition, we introduce the concept of multi-version recovery, where multiple instances of a file can be recovered based on a cluster specific timestamps inserted during the write operation. Finally, using controlled experiments we have been able to verify that our proposed file system successfully recovers all deleted files in our test environment.

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