Umělá inteligence v kybernetické bezpečnosti

Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

National Conference on ‘Renewable Energy, Smart Grid and Telecommunication-2023

Theme of the Conference: “The challenges and opportunities of integrating renewable energy into the grid” The National Conference on Renewable Energy, Smart Grid, and Telecommunication - 2023 is a platform for industry experts, researchers, and policymakers to come together and explore the latest advancements and challenges in the fields of renewable energy, smart grids, and telecommunication. Conference Highlights: In-depth discussions on renewable energy technologies and innovations. Smart grid integration for a sustainable future. The role of telecommunication in advancing renewable energy solutions. Networking opportunities with industry leaders and experts. Presentation of cutting-edge research papers and case studies. Conference topics: Renewable Energy Technologies and Innovations Smart Grid Development and Implementation Telecommunication for Energy Systems Energy Storage and Grid Balancing Policy, Regulation, and Market Dynamics Environmental and Social Impacts of Renewable Energy Energy Transition and Future Outlook Integration of renewable energy into the grid Microgrids and decentralized energy systems Grid cybersecurity and data analytics IoT and sensor technologies for energy monitoring Data management and analytics in energy sector Battery storage technologies and applicationshttps://www.interscience.in/conf_proc_volumes/1087/thumbnail.jp

Analysis of nature inspired algorithms and their application in electrical power engineering

Ker so viri, čas in denar v resničnem svetu vedno omejeni, moramo najti rešitve za optimalno porabo teh pomembnih virov. Za reševanje večine optimizacijskih problemov resničnega sveta potrebujemo mnogokrat zapleteno optimizacijsko orodje. Na naravi osnovani meta-hevristični algoritmi so eni izmed najpogosteje uporabljenih algoritmov za optimizacijo. Algoritem kresničk je eden od teh algoritmov. V tem delu so analizirani optimizacijski algoritmi od tradicionalnih metod do modernih meta-hevrističnih algoritmov, s poudarkom na algoritmih osnovanih na naravi. To delo poskuša predstaviti zgodovino in aplikacijo teh algoritmov. Prvo poglavje predstavi algoritme in analizira bistvo algoritma. Potem se razpravlja osnovno oblikovanje optimizacijskega problema in moderne pristope s pogleda inteligence rojev. Pregledana je kratka zgodovina na naravi osnovanih algoritmov. Drugo poglavje analizira ključne komponente na naravi osnovanih algoritmov s pogleda njihovih evolucijskih operatorjev in funkcionalnosti. Glavni cilj je podati pregled teh algoritmov. V tretjem poglavju se predstavi standardni algoritem kresničk in potem so na kratko predstavljene različice. Analizirane so tudi karakteristike algoritma kresničk. Četrto poglavje predstavi implementacijo algoritma kresničk pri reševanju problema optimalne razporeditve obratovanja elektrarn z minimiziranjem stroškov goriva in upošteva omejitve generatorjev in izgube prenosa. Temu sledi kratek pregled na naravi osnovanih algoritmov v elektroenergetskih sistemih.Because resources, time and money are always limited in real world applications, we have to find solutions to optimally use these valuable resources. To solve most real world optimization problems we need sophisticated optimization tools. Nature inspired meta-heuristic algorithms are among the most widely used algorithms for optimization. Firefly algorithm is one of these algorithms. In this work optimization algorithms are analyzed from traditional methods to modern meta-heuristic algorithms, with an emphasis on nature inspired algorithms. This work is attempts to present the history and applications of these algorithms. The first chapter introduces algorithms and analyzes the essence of the algorithm. Then the general formulation of an optimization problem is discussed and modern approaches in terms of swarm intelligence. A brief history of nature inspired algorithms is reviewed. The second chapter analyzes the key components nature inspired algorithms in terms of their evolutionary operators and functionalities. The main aim is to provide an overview of these algorithms. In the third chapter the standard firefly algorithm is introduced and then the variants are briefly reviewed. The characteristics of firefly algorithm are also analyzed. The forth chapter presents the implementation of firefly algorithm in solving the economic dispatch problem by minimizing the fuel cost and considering the generator limits and transmission losses. This is followed by a short review of applications nature inspired algorithms in power systems