2 research outputs found
The Coming Era of AlphaHacking? A Survey of Automatic Software Vulnerability Detection, Exploitation and Patching Techniques
With the success of the Cyber Grand Challenge (CGC) sponsored by DARPA, the
topic of Autonomous Cyber Reasoning System (CRS) has recently attracted
extensive attention from both industry and academia. Utilizing automated system
to detect, exploit and patch software vulnerabilities seems so attractive
because of its scalability and cost-efficiency compared with the human expert
based solution. In this paper, we give an extensive survey of former
representative works related to the underlying technologies of a CRS, including
vulnerability detection, exploitation and patching. As an important supplement,
we then review several pioneer studies that explore the potential of machine
learning technologies in this field, and point out that the future development
of Autonomous CRS is inseparable from machine learning.Comment: 8 pages, 2 figures, DSC2018, to be publishe
An Overview of Attacks and Defences on Intelligent Connected Vehicles
Cyber security is one of the most significant challenges in connected
vehicular systems and connected vehicles are prone to different cybersecurity
attacks that endanger passengers' safety. Cyber security in intelligent
connected vehicles is composed of in-vehicle security and security of
inter-vehicle communications. Security of Electronic Control Units (ECUs) and
the Control Area Network (CAN) bus are the most significant parts of in-vehicle
security. Besides, with the development of 4G LTE and 5G remote communication
technologies for vehicle-toeverything (V2X) communications, the security of
inter-vehicle communications is another potential problem. After giving a short
introduction to the architecture of next-generation vehicles including
driverless and intelligent vehicles, this review paper identifies a few major
security attacks on the intelligent connected vehicles. Based on these attacks,
we provide a comprehensive survey of available defences against these attacks
and classify them into four categories, i.e. cryptography, network security,
software vulnerability detection, and malware detection. We also explore the
future directions for preventing attacks on intelligent vehicle systems