Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.

Abstract--As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. The Controller Area Network OBD-II diagnostic data is easy to access via the OBD-II port, (CAN) protocol was firstly developed by Bosch in the as all modern cars are equipped with the OBD-II diagnostic 1980s [1] and serves as the de facto standard protocol for connecting system. OBD-II diagnostic data can be converted into humanreadable ECUs embedded in cars [3]-[5]. The standard structure accurate vehicle data with public formulas to be used of the CAN frame is composed of the start of frame, arbitration in the matching process for associating semantic meanings field, control field, data field, CRC field, ACK field and end with CAN signals. Both OBD-II diagnostic data and regular of frame, as shown in Figure 1. While the CAN protocol has CAN frames can be collected from the OBD-II port. The a standardized frame structure, understanding the protocol's RE systems can leverage both CAN and OBD-II diagnostic utilization for signal transmission remains challenging. This data to create a comprehensive dataset for reverse engineering is because Original Equipment Manufacturers (OEMs) encode purposes, eliminating the need for additional measurement the signals within the CAN frames' data fields (data payloads) equipment like IMUs. in proprietary ways that vary among OEMs, vehicle models, The primary objective of a CAN RE system is to identify the and years [6]. CAN messages frames is the first step to extracting the essential information are structured into frames, and the CAN frames of different to develop autonomous applications or explore automotive CAN IDs have different lengths of the data payload.

Kurt Knutsson discusses new technology developed by researchers who have created a portable, non-invasive system that can decode silent thoughts and turn them into text. Imagine if you could communicate with anyone without saying a word, just by thinking. That's the promise of a new technology developed by researchers from the University of Technology Sydney (UTS), who have created a portable, non-invasive system that can decode silent thoughts and turn them into text. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER The technology, called DeWave, uses an electroencephalogram (EEG) cap to record electrical brain activity through the scalp. It then uses an artificial intelligence (AI) model to segment the EEG wave into distinct units that capture specific characteristics and patterns from the human brain.

It's been called'the future of warfare'. Off-the-shelf unmanned aerial systems (UAS), carrying a'payload' of explosives or biological material, flown by terrorists or enemy armed forces into a crowded building or military base. Now the University of Technology Sydney (UTS) and Sydney ASX-listed defence tech company DroneShield have produced next-generation drone technology to better identify threats from these aggressive UAS. In a partnership funded by the NSW and Australian Governments, UTS and DroneShield – an Australian developer of counter-UAS solutions – have produced an optical system for detection, identification and tracking of fast-moving threats such as nefarious UAS, comprised of a camera and Convolutional Neural Network (CNN). UTS and DroneShield began working together in October 2019 – just a month after one of the most recent examples of aggressive use of drones when the oil facilities at Abqaiq–Khurais in Saudi Arabia were attacked by a swarm of UAS.

Australia's famed Gold Coast, a 43-mile surfing mecca along the country's eastern shores, is looking to use AI-powered drones to warn people of sharks, lest they become chum. The humid, subtropical climate has seen fourteen shark attacks in the last two years that have resulted in two deaths, but a new shark-spotting initiative will officially debut in September after a year of R&D. It involves quad-copters that will fly above the greenish-blue water, relaying video to image-recognition technology that will determine if the footage is of mere dolphins, or something more deadly. And if a Jaws-like creature is confirmed, the drone sounds an alarm and can drop a four-person life raft and communication device that could enable swimmers to call for help. Australia's the Ripper Group is supplying the actual drones, while a team from the University of Technology Sydney developed the AI.