Biblio

Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.

One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare a defense. The ability to anticipate attacks and prepare defenses before they occur would be a significant scientific and technological development with practical applications in cybersecurity. In this paper, we present a method to augment machine learning-based malware detection systems by predicting signatures of future malware variants and injecting these variants into the defensive system as a vaccine. Our method uses deep learning to learn patterns of malware evolution from family histories. These evolution patterns are then used to predict future family developments. Our experiments show that a detection system augmented with these future malware signatures is able to detect future malware variants that could not be detected by the detection system alone. In particular, it detected 11 new malware variants without increasing false positives, while providing up to 5 months of lead time between prediction and attack.