Viliam Lisy

Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals.

Case Studies of Network Defense with Attack Graph Games

The increasing complexity of securing modern computer networks makes decision support systems an important tool for administrators. A challenge many existing tools fail to address is that attackers react strategically to new security measures, adapting their behaviors in response. Game theory provides a methodology for making decisions that takes into account these reactions, rather than assuming static attackers. The authors present an overview of how game theory can be used to inform one type of security decision: how to optimally place honeypots in a network. They demonstrate this approach on a realistic case study and present initial validation results based on a study comparing their approach with human decision makers.

Path Hopping: an MTD Strategy for Quantum-safe Communication

Moving target defense (MTD) strategies have been widely studied for securing computer communication systems. We consider using MTD strategies as a cryptographic mechanism for providing secure communication when the adversary has access to a quantum computer and security is required over a long period of time. We assume Alice and Bob are connected by multiple disjoint paths, not all of which can be eavesdropped by the attacker at the same time. We propose a cryptographic system that uses an MTD strategy that achieves long-term quantum-safe security. We model the system as a Markov chain, and propose two security measures that correspond to two types of adversaries, called risk-taking and risk-averse. Our numerical simulations shows dependencies between system parameters, and leads to new insights, such as quantifying the cost of being a risk-averse adversary.

Path Hopping: An MTD Strategy for Long-Term Quantum-Safe Communication

Moving target defense (MTD) strategies have been widely studied for securing computer systems. We consider using MTD strategies to provide long-term cryptographic security for message transmission against an eavesdropping adversary who has access to a quantum computer. In such a setting, today’s widely used cryptographic systems including Diffie-Hellman key agreement protocol and RSA cryptosystem will be insecure and alternative solutions are needed. We will use a physical assumption, existence of multiple communication paths between the sender and the receiver, as the basis of security, and propose a cryptographic system that uses this assumption and an MTD strategy to guarantee efficient long-term information theoretic security even when only a single path is not eavesdropped. Following the approach of Maleki et al., we model the system using a Markov chain, derive its transition probabilities, propose two security measures, and prove results that show how to calculate these measures using transition probabilities. We define two types of attackers that we call risk-taking and risk-averse and compute our proposed measures for the two types of adversaries for a concrete MTD strategy. We will use numerical analysis to study tradeoffs between system parameters, discuss our results, and propose directions for future research.

Utility-based model for classifying adversarial behaviour in multi-agent systems

Interactions and social relationships among agents are an important aspect of multi-agent systems. In this paper, we explore how such relationships and their relation to agentpsilas objectives influence agentpsilas decision-making. Building on the framework of stochastic games, we propose a classification scheme, based on a formally defined concept of interaction stance, for categorizing agentpsilas behaviour as self-interested, altruistic, competitive, cooperative, or adversarial with respect to other agents in the system. We show how the scheme can be employed in defining behavioural norms, capturing social aspects of agentpsilas behaviour and/or in representing social configurations of multi agent systems.