Artificial intelligence in the cyber security environment
Vähäkainu, P., & Lehto, M. (2019). Artificial intelligence in the cyber security environment. In N. van der Waag-Cowling, & L. Leenen (Eds.), ICCWS 2019 : Proceedings of the 14th International Conference on Cyber Warfare and Security (pp. 431-440). Academic Conferences International. The proceedings of the ... international conference on cyber warfare and security.
Julkaistu sarjassa
The proceedings of the ... international conference on cyber warfare and securityPäivämäärä
2019Tekijänoikeudet
© The Author(s), 2019
Artificial Intelligence (AI) is intelligence exhibited by machines. Any system that perceives its environment and
takes actions that maximize its chance of success at some goal may be defined as AI. The family of AI research
is rich and varied. For example, cognitive computing is a comprehensive set of capabilities based on
technologies such as deep learning, machine learning, natural language processing, reasoning and decision
technologies, speech and vision technologies, human interface technologies, semantic technology, dialog and
narrative generation, among other technologies. Artificial intelligence and robotics have steadily growing roles
in our lives and have the potential to transform vital functions of the society. Organizations benefit from the
ability of cognitive systems to improve their expertise quickly and from sharing it to all those who need it. The
know-how of top experts is quickly made available to all, when their subject matter expertise is taught to a
cognitive system. Through repeated use, the system will provide increasingly accurate responses, eventually
eclipsing the accuracy of human experts. With artificial intelligence, comprehension can be outsourced. As the
intelligence of machines improve, they will use deep learning to understand the collective information of
humankind. With the use of digital sensor data, equipment based on artificial intelligence can used to develop
smart advisors, teachers or assistants. As artificial intelligence technology is helping society to advance, there
are risks associated with its use, found in the operating systems, hardware, algorithms, system management,
ethics and liability, and privacy. The study focuses on artificial intelligence threats and risks and how AI may
help to solve cyber security problems. This study uses taxonomy classification principle to classify 12 the most
crucial areas of cyber security. Research method of this study was to gather 11 AI solutions that were divided
into seven different categories of the crucial areas of cyber security represented in introduction chapter. AI
solutions gathered uses artificial intelligence in detecting and predicting information security threats and
anomalies and blocking them. The purpose of this study is to classify AI-based cyber security solutions
gathered and provide information what they can offer in solving problems in the field of cyber security.
...
Julkaisija
Academic Conferences InternationalEmojulkaisun ISBN
978-1-912764-11-2Konferenssi
International Conference on Cyber Warfare and SecurityKuuluu julkaisuun
ICCWS 2019 : Proceedings of the 14th International Conference on Cyber Warfare and SecurityISSN Hae Julkaisufoorumista
2048-9870Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/30945638
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Strategic cyber threat intelligence : Building the situational picture with emerging technologies
Voutilainen, Janne; Kari, Martti (Academic Conferences International, 2020)In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and ... -
On Attacking Future 5G Networks with Adversarial Examples : Survey
Zolotukhin, Mikhail; Zhang, Di; Hämäläinen, Timo; Miraghaei, Parsa (MDPI AG, 2023)The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to ... -
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Peregrine Technical Solutions, 2020)Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine ... -
Artificial Intelligence for Cybersecurity : A Systematic Mapping of Literature
Wiafe, Isaac; Koranteng, Felix N.; Obeng, Emmanuel N.; Assyne, Nana; Wiafe, Abigail; Gulliver, Stephen R. (IEEE, 2020)Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making real-time decisions that can ... -
Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring
Shelke, Palvi; Hämäläinen, Timo (Academic Conferences International Ltd, 2024)The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.