Muttukrishnan Rajarajan

Dilara Acarali, Muttukrishnan Rajarajan, Doron Chema et al.

Traditional power grids are evolving to keep pace with the demands of the modern age. Smart grids contain integrated IT systems for better management and efficiency, but in doing so, also inherit a plethora of cyber-security threats and vulnerabilities. Denial-of-Service (DoS) is one such threat. At the same time, the smart grid has particular characteristics (e.g. minimal delay tolerance), which can influence the nature of threats and so require special consideration. In this paper, we identify a set of possible smart grid-specific DoS scenarios based on current research, and analyse them in the context of the grid components they target. Based on this, we propose a novel target-based classification scheme and further characterise each scenario by qualitatively exploring it in the context of the underlying grid infrastructure. This culminates in a smart grid-centric analysis of the threat to reveal the nature of DoS in this environment.

Yogachandran Rahulamathavan, Safak Dogan, Xiyu Shi et al.

Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information. Privacy-preserving scalar product is one of the critical algorithms in many private applications. The state-of-the-art privacy-preserving scalar product schemes use either computationally intensive homomorphic (public-key) encryption techniques such as Paillier encryption to achieve strong security (i.e., 128-bit) or random masking technique to achieve high efficiency for low security. In this paper, lattice structures have been exploited to develop an efficient privacy-preserving system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have been provided along with a concrete set of parameters to achieve 128-bit and 256-bit security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomisation technique at 128-bit security.

Arslan Ashraf, Abdul Aziz, Umme Zahoora et al.

Detection and analysis of a potential malware specifically, used for ransom is a challenging task. Recently, intruders are utilizing advanced cryptographic techniques to get hold of digital assets and then demand a ransom. It is believed that generally, the files comprise of some attributes, states, and patterns that can be recognized by a machine learning technique. This work thus focuses on the detection of Ransomware by performing feature engineering, which helps in analyzing vital attributes and behaviors of the malware. The main contribution of this work is the identification of important and distinct characteristics of Ransomware that can help in detecting them. Finally, based on the selected features, both conventional machine learning techniques and Transfer Learning based Deep Convolutional Neural Networks have been used to detect Ransomware. In order to perform feature engineering and analysis, two separate datasets (static and dynamic) were generated. The static dataset has 3646 samples (1700 Ransomware and 1946 Goodware). On the other hand, the dynamic dataset comprised of 3444 samples (1455 Ransomware and 1989 Goodware). Through various experiments, it is observed that the Registry changes, API calls, and DLLs are the most important features for Ransomware detection. Additionally, important sequences are found with the help of the N-Gram technique. It is also observed that in the case of Registry Delete operation, if a malicious file tries to delete registries, it follows a specific and repeated sequence. However, for the benign file, it doesnt follow any specific sequence or repetition. Similarly, an interesting observation made through this study is that there is no common Registry deleted sequence between malicious and benign files. And thus this discernible fact can be readily exploited for Ransomware detection.