Timing Analysis of Algorithm Substitution Attacks in a Post-Quantum TLS Protocol

Snowden's revelations about mass surveillance brought to public attention devastating attacks on cryptographic algorithm implementations. One of the most prominent subsets of these attacks is called Algorithm Substitution Attacks (ASA), where a subverted implementation leaks sensitive information. Recently, it has been proposed to modify TLS implementations to use Post-Quantum Cryptography (PQC). In this paper, we propose and analyze ASA in two PQC schemes that can be used in TLS. We attacked the Kyber Key Encapsulation Mechanism (KEM) and Falcon Signature and successfully deployed them in a TLS implementation. Results show that timing analysis can distinguish our Falcon subversion, but it is not enough to detect our attacks deployed in TLS.

About Features Of The Mechanism Of Activity Of Law-Enforcement Bodies On Preventive Maintenance Of Offences Among Earlier Offenders Of Persons

In article questions of the mechanism of activity of law-enforcement bodies on preventive maintenance of offences among earlier offenders of persons are considered, the special attention is turned on the analysis of algorithm of preventive measures spent by inspectors of preventive maintenance in the given sphere.

Contextualized mobile game-based learning application for computing education

This paper intends to present an overview of a mobile game-based learning application, BaghLearn that develops and upskills programming and algorithmic knowledge by cross-curricular capabilities through a traditional world-based game. The focus of this research was to explore the learning effectiveness of BaghLearn on students. Mixed method research approach was applied to collect, process and analyze the research data in which undergraduate students who had some prior knowledge or had taken algorithm courses were instructed to test the mobile game learning application. This study exhibits the idea of integrating learning with contextual mobile game as an effective approach in understanding the influence of games towards cognitive achievements of students in computing education. In addition, there are no major requirements for the use of this application (can be maintained in resource-constrained contexts such as Nepal), which makes it expressively satisfying and useful for students who are relentlessly using mobile devices. Besides, this study evaluated the influence of BaghLearn towards learning of the design and analysis of algorithm course, which is a compulsory course for most undergraduate computing education program. Furthermore, the study findings can be used as a guideline for developing learning solutions and usability evaluation of such solutions, especially for infrastructure-constrained contexts. Students using the BaghLearn opined that the application is easy to use, supportive and lead to improved learning satisfaction.

EEG-Based BCI Emotion Recognition: A Survey

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.