Offensive Machine Learning Methods and the Cyber Kill Chain
Turtiainen, H., Costin, A., Polyakov, A., & Hämäläinen, T. (2023). Offensive Machine Learning Methods and the Cyber Kill Chain. In T. Sipola, T. Kokkonen, & M. Karjalainen (Eds.), Artificial Intelligence and Cybersecurity : Theory and Applications (pp. 125-145). Springer. https://doi.org/10.1007/978-3-031-15030-2_6
Päivämäärä
2023Oppiaine
TekniikkaTietotekniikkaSecure Communications Engineering and Signal ProcessingEngineeringMathematical Information TechnologySecure Communications Engineering and Signal ProcessingTekijänoikeudet
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
Cyberattacks are the “new normal” in the hyper-connected and all-digitized modern world, as breaches, denial-of-service, ransomware, and a myriad of other attacks occur every single day. As the attacks and breaches increase in complexity, diversity, and frequency, cybersecurity actors (both ethical and cybercrime) turn to automating these attacks in various ways and for a variety of reasons, including the development of effective and superior cybersecurity defenses. In this chapter, we address innovations in machine learning, deep learning, and artificial intelligence within the offensive cybersecurity fields. We structure this chapter inline with the Lockheed Martin’s Cyber Kill Chain taxonomy in order to cover adequate grounds on this broad topic, and occasionally refer to the more granular MITRE ATT&CK taxonomy whenever relevant.
Julkaisija
SpringerEmojulkaisun ISBN
978-3-031-15029-6Kuuluu julkaisuun
Artificial Intelligence and Cybersecurity : Theory and ApplicationsAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/164482646
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Defensive Machine Learning Methods and the Cyber Defence Chain
Turtiainen, Hannu; Costin, Andrei; Hämäläinen, Timo (Springer, 2023)Cyberattacks are now occurring on a daily basis. As attacks and breaches are so frequent, and the fact that human work hours do not scale infinitely, the cybersecurity industry needs innovative and scalable tools 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 ... -
Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods
Nath, Bhagawan; Hämäläinen, Timo; Ezekiel, Soundararajan (Academic Conferences International Ltd, 2022)Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, ... -
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 ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.