Cyber-security internals of a Skoda Octavia vRS:a hands on approach

The convergence of information technology and vehicular technologies are a growing paradigm, allowing information to be sent by and to vehicles. This information can further be processed by the Electronic Control Unit (ECU) and the Controller Area Network (CAN) for in-vehicle communications or through a mobile phone or server for out-vehicle communication. Information sent by or to the vehicle can be life-critical (e.g. breaking, acceleration, cruise control, emergency communication, etc. . . ). As vehicular technology advances, in-vehicle networks are connected to external networks through 3 and 4G mobile networks, enabling manufacturer and customer monitoring of different aspects of the car. While these services provide valuable information, they also increase the attack surface of the vehicle, and can enable long and short range attacks. In this manuscript, we evaluate the security of the 2017 Skoda Octavia vRS 4x4. Both physical and remote attacks are considered, the key fob rolling code is successfully compromised, privacy attacks are demonstrated through the infotainment system, the Volkswagen Transport Protocol 2.0 is reverse engineered. Additionally, in-car attacks are highlighted and described, providing an overlook of potentially deadly threats by modifying ECU parameters and components enabling digital forensics investigation are identified

UAS for Public Safety Operations: A Comparison of UAS Point Clouds to Terrestrial LIDAR Point Cloud Data using a FARO Scanner

Unmanned Aircraft Systems (UAS) can be useful tools for public safety agencies during crime or vehicle accident scene investigations if it can provide value to the resource-constrained agency. The speed of data collection, while minimizing first responder risk, while sustaining an acceptable level of accuracy and precision compared to other tools is where the agency may find value. During a recent homicide investigation in Florida, a UAS provided saved 81% in law enforcement labor hours with an acceptable level of accuracy compared to traditional methods. The purpose of this research was to compare UAS to determine if there were differences in accuracy and precision compared to a FARO terrestrial laser scanner in a crime scene reconstruction scenario. UAS registered point clouds were generated in Pix4Dmapper from a DJI Mavic Pro, Mavic 2 Enterprise Dual, Inspire 1, Inspire 2, Phantom 4 Professional, Parrot Anafi, and Bebop 2 at flying heights of 82, 100, 150, 200, and 250 feet respectively in a grid, double grid, circle, and double grid + circle flight pattern and compared to a FARO terrestrial laser scanner. The UAS point cloud accuracy (M = 33.2mm, SD = 6.4mm), compared to the FARO point cloud t(139) = 56.5, p = 0.00 was determined to be not as accurate as the 2.6mm-accurate FARO scanner point cloud; however, may still have an acceptable level of accuracy for investigators. An analysis of variance showed a flying height of 100 feet AGL yielded the most precision and accuracy combined when compared to other flying heights. The double grid + circle flight pattern had smaller RMS errors compared to the other flight patterns. There was also a significant difference by the UAS aircraft model used. The P4P had a smaller RMS error compared to the six other aircraft examined

An Exploratory Study of Burial Identification Using Historic Human Remains Detection Dog Alerts and Inorganic Soil Analyses

One point at which forensic science and historical archaeology intersect, and the focus of this thesis, is using the decidedly forensic avenues of trained dogs, probing, and chemical analyses of soils, informed by archaeological survey, to locate burials. Human remains detection dogs have proven to be a nonintrusive and effective method for identifying or confirming historic unmarked burial locations. Inorganic soil analyses have been demonstrated in prior research to show variations in grave soil. For this research, the hypothesis that is explored is that a corpse will chemically alter the soil in or on which it is placed to a degree that is detectable using inorganic chemical analyses, after many decades or even centuries, and that the inorganic chemical profile associated with grave soil will correspond with canine alerts. If certain elements do co-occur with dog alerts, then testing for their presence in soil may be a reliable and less costly method on its own or potentially could be employed as a second source of evidence for burials identified by dog alerts or other methods of detection. In an effort to gauge the reliability and agreement of these methods, Historic Human Remains Detection (HHRD) dog alerts were recorded and corresponding soil samples were attempted in five case studies at geographically distinct sites of potential burials, 100 to 1,100 years old. Soil samples were tested using inductively coupled plasma optical emission spectrometry (ICP-OES) elemental analysis to determine their inorganic composition. Three of these sites were previously reported as inconclusive and were reanalyzed here. Results showed that there appeared to be some correspondence between HHRD dog alerts and inorganic soil profiles consistent with that reported in other studies. Although further and more robust research on inorganic soil analysis is required to confirm its validity and reliability, this thesis concludes that appropriate surface soil analyses appear to have potential as a minimally invasive tool to help identify historic human burials, particularly those burials that have been located with the use of HHRD dog investigations

Improving landslide detection from airborne laser scanning data using optimized Dempster-Shafer

© 2018 by the authors. A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster-Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset "A" for implementation and subset "B" for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions