A systematic literature review on cyber threat hunting

Since the term "Cyber threat hunting" was introduced in 2016, there have been a rising trend of proactive defensive measure to create more cyber security. This research will look into peer reviewed literature on the subject of cyber threat hunting. Our study shows an increase in the field with methods of machine learning.\\ Keywords: Cyber threat, Cyber security, threat hunting , security system, data driven, Intel, analytic driven, TTP

Quantum Symbolic Execution

With advances in quantum computing, researchers can now write and run many quantum programs. However, there is still a lack of effective methods for debugging quantum programs. In this paper, quantum symbolic execution (QSE) is proposed to generate test cases, which helps to finding bugs in quantum programs. The main idea of quantum symbolic execution is to find the suitable test cases from all possible ones (i.e. test case space). It is different from the way of classical symbol execution, which gets test cases by calculating instead of searching. QSE utilizes quantum superposition and parallelism to store the test case space with only a few qubits. According to the conditional statements in the debugged program, the test case space is continuously divided into subsets, subsubsets and so on. Elements in the same subset are suitable test cases that can test the corresponding branch in the code to be tested. QSE not only provides a possible way to debug quantum programs, but also avoids the difficult problem of solving constraints in classical symbolic execution

Un análisis descriptivo de la población y de la vivienda en España

Este proyecto se basa en el análisis de la población y el parque inmobiliario en España, analizando las densidades de población y de viviendas. Asimismo se lleva a cabo un estudio histórico de precio de la vivienda y se describen algunas características interesantes de los hogares españoles.Universidad de Sevilla. Grado en Finanzas y Contabilida

Effects of biogas slurry on capsicum spp. growth and control of soil-pathogens.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Revisiting Garg's 2-Approximation Algorithm for the k-MST Problem in Graphs

This paper revisits the 2-approximation algorithm for $k$-MST presented by Garg in light of a recent paper of Paul et al.. In the $k$-MST problem, the goal is to return a tree spanning $k$ vertices of minimum total edge cost. Paul et al. extend Garg's primal-dual subroutine to improve the approximation ratios for the budgeted prize-collecting traveling salesman and minimum spanning tree problems. We follow their algorithm and analysis to provide a cleaner version of Garg's result. Additionally, we introduce the novel concept of a kernel which allows an easier visualization of the stages of the algorithm and a clearer understanding of the pruning phase. Other notable updates include presenting a linear programming formulation of the $k$-MST problem, including pseudocode, replacing the coloring scheme used by Garg with the simpler concept of neutral sets, and providing an explicit potential function.Comment: Proceedings of SIAM Symposium on Simplicity in Algorithms (SOSA) 202

Adversarial Attacks on Online Learning to Rank with Stochastic Click Models

We propose the first study of adversarial attacks on online learning to rank. The goal of the adversary is to misguide the online learning to rank algorithm to place the target item on top of the ranking list linear times to time horizon $T$ with a sublinear attack cost. We propose generalized list poisoning attacks that perturb the ranking list presented to the user. This strategy can efficiently attack any no-regret ranker in general stochastic click models. Furthermore, we propose a click poisoning-based strategy named attack-then-quit that can efficiently attack two representative OLTR algorithms for stochastic click models. We theoretically analyze the success and cost upper bound of the two proposed methods. Experimental results based on synthetic and real-world data further validate the effectiveness and cost-efficiency of the proposed attack strategies