Publikace

Článek

10.3390/fi14010028

Katedra počítačových systémů

In wireless ad hoc networks, security and communication challenges are frequently addressed by deploying a trust mechanism. A number of approaches for evaluating trust of ad hoc network nodes have been proposed, including the one that uses neural networks. We proposed to use packet delivery ratios as input to the neural network. In this article, we present a new method, called TARA (Trust-Aware Reactive Ad Hoc routing), to incorporate node trusts into reactive ad hoc routing protocols. The novelty of the TARA method is that it does not require changes to the routing protocol itself. Instead, it influences the routing choice from outside by delaying the route request messages of untrusted nodes. The performance of the method was evaluated on the use case of sensor nodes sending data to a sink node. The experiments showed that the method improves the packet delivery ratio in the network by about 70%. Performance analysis of the TARA method provided recommendations for its application in a particular ad hoc network.

Článek

10.1007/s42979-022-01222-w

Katedra informační bezpečnosti

In this paper we propose a set of algorithms that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext, assuming that we can control the code flow of the encrypting program, e.g., when an application is performing encryption without the user’s permission. The first algorithm makes use of the fact that we can monitor accesses to the AES S-Box and deduce the desired data from these accesses; the approach is suitable to software-based AES implementations, both naïve and optimized. To demonstrate the feasibility of this approach we designed a tool which implements the algorithm for Microsoft Windows running on the Intel x86 architecture. The tool has been successfully tested against a set of applications using different cryptographic libraries and common user applications. We also discuss the options of recovering the same data when hardware-assisted AES implementations on Intel-compatible architectures are used.

Článek

10.1145/3485527

Laboratoř výzkumu programování

As a scientific programming language, Julia strives for performance but also provides high-level productivity features. To avoid performance pathologies, Julia users are expected to adhere to a coding discipline that enables so-called type stability. Informally, a function is type stable if the type of the output depends only on the types of the inputs, not their values. This paper provides a formal definition of type stability as well as a stronger property of type groundedness, shows that groundedness enables compiler optimizations, and proves the compiler correct. We also perform a corpus analysis to uncover how these type-related properties manifest in practice.

Stať ve sborníku

10.4230/LIPICS.MFCS.2023.42

Katedra teoretické informatiky

The \textsc{Hunters and Rabbit} game is played on a graph $G$ where the Hunter player shoots at $k$ vertices in every round while the Rabbit player occupies an unknown vertex and, if it is not shot, must move to a neighbouring vertex after each round. The Rabbit player wins if it can ensure that its position is never shot. The Hunter player wins otherwise. The hunter number $h(G)$ of a graph $G$ is the minimum integer $k$ such that the Hunter player has a winning strategy (i.e., allowing him to win whatever be the strategy of the Rabbit player). This game has been studied in several graph classes, in particular in bipartite graphs (grids, trees, hypercubes...), but the computational complexity of computing $h(G)$ remains open in general graphs and even in more restricted graph classes such as trees. To progress further in this study, we propose a notion of monotonicity (a well-studied and useful property in classical pursuit-evasion games such as Graph Searching games) for the \textsc{Hunters and Rabbit} game imposing that, roughly, a vertex that has already been shot ``must not host the rabbit anymore''. This allows us to obtain new results in various graph classes. More precisely, let the monotone hunter number $mh(G)$ of a graph $G$ be the minimum integer $k$ such that the Hunter player has a monotone winning strategy. We show that $pw(G) \leq mh(G) \leq pw(G)+1$ for any graph $G$ with pathwidth $pw(G)$, which implies that computing $mh(G)$, or even approximating $mh(G)$ up to an additive constant, is \textsf{NP}-hard. Then, we show that $mh(G)$ can be computed in polynomial time in split graphs, interval graphs, cographs and trees. These results go through structural characterisations which allow us to relate the monotone hunter number with the pathwidth in some of these graph classes. In all cases, this allows us to specify the hunter number or to show that there may be an arbitrary gap between $h$ and $mh$, i.e., that monotonicity does not help. In particular, we show that, for every $k\geq 3$, there exists a tree $T$ with $h(T)=2$ and $mh(T)=k$. We conclude by proving that computing $h$ (resp., $mh$) is FPT~parameterised by the minimum size of a vertex cover.

Článek

10.1109/JIOT.2022.3228816

Katedra číslicového návrhu

Explainability and alert reasoning are essential but often neglected properties of intrusion detection systems. The lack of explainability reduces security personnel’s trust, limiting the overall impact of alerts. This paper proposes the BOTA (Botnet Analysis) system, which uses the concepts of weak indicators and heterogeneous meta-classifiers to maintain accuracy compared with state-of-the-art systems while also providing explainable results that are easy to understand. To evaluate the proposed system, we have implemented a demonstration of intrusion weak-indication detectors, each working on a different principle to ensure robustness. We tested the architecture with various real-world and lab-created datasets, and it correctly identified 94.3% of infected IoT devices without false positives. Furthermore, the implementation is designed to work on top of extended bidirectional flow data, making it deployable on large 100 Gbps large-scale networks at the level of Internet Service Providers. Thus, a single instance of BOTA can protect millions of devices connected to end-users’ local networks and significantly reduce the threat arising from powerful IoT botnets.

Článek

10.1016/j.eswa.2023.121164

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous work and thus can be deployed to larger datasets and allows the organization to define a minimum level of exposure for groups of items. We conduct an extensive empirical evaluation indicating that our new framework can increase the exposure of items from disadvantaged groups at a small cost of recommendation accuracy

Článek

10.1162/dint_a_00161

Katedra softwarového inženýrství

While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources. The FAIR Data Point is a metadata repository that follows the DCAT(2) schema, and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers. There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use. Here we describe five specific tools—an installer, a loader, two Web-based interfaces, and an indexer—aimed at maximizing the uptake and utility of the FAIR Data Point.