Outputs (22)

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification (2022)
Journal Article
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2023). Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification. Journal of Information Security and Applications, 72, Article 103398. https://doi.org/10.1016/j.jisa.2022.103398

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial mach... Read More about Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification.

AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development (2022)
Journal Article
Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38-51. https://doi.org/10.1080/17439884.2022.2095568

The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the ke... Read More about AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development.

Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey (2022)
Journal Article
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2022). Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey. Journal of Cybersecurity and Privacy, 2(1), 154-190. https://doi.org/10.3390/jcp2010010

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introdu... Read More about Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey.

MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version) (2019)
Journal Article
Onwuzurike, L., Mariconti, E., Andriotis, P., De Cristofaro, E., Ross, G., & Stringhini, G. (2019). MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version). ACM Transactions on Privacy and Security, 22(2), Article 14. https://doi.org/10.1145/3313391

As Android has become increasingly popular, so has malware targeting it, thus motivating the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard... Read More about MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version).

Distributed consensus algorithm for events detection in cyber-physical systems (2019)
Journal Article
Li, S., Zhao, S., Yang, P., Andriotis, P., Xu, L., & Sun, Q. (2019). Distributed consensus algorithm for events detection in cyber-physical systems. IEEE Internet of Things, 6(2), 2299-2308. https://doi.org/10.1109/JIOT.2019.2906157

In the harsh environmental conditions of cyber-physical systems (CPSs), the consensus problem seems to be one of the central topics that affect the performance of consensus-based applications, such as events detection, estimation, tracking, blockchai... Read More about Distributed consensus algorithm for events detection in cyber-physical systems.

Studying users’ adaptation to Android's run-time fine-grained access control system (2018)
Journal Article
Andriotis, P., Stringhini, G., & Sasse, A. (2018). Studying users’ adaptation to Android's run-time fine-grained access control system. Journal of Information Security and Applications, 40, 31-43. https://doi.org/10.1016/j.jisa.2018.02.004

© 2018 Elsevier Ltd The advent of the sixth Android version brought a significant security and privacy advancement to its users. The platform's security model has changed dramatically, allowing users to grant or deny access to resources when requeste... Read More about Studying users’ adaptation to Android's run-time fine-grained access control system.

A comparative study of android users’ privacy preferences under the runtime permission model (2017)
Journal Article
Andriotis, P., Li, S., Spyridopoulos, T., & Stringhini, G. (2017). A comparative study of android users’ privacy preferences under the runtime permission model. Lecture Notes in Artificial Intelligence, 10292 LNCS, 604-622. https://doi.org/10.1007/978-3-319-58460-7_42

© Springer International Publishing AG 2017. Android users recently were given the ability to selectively grant access to sensitive resources of their mobile devices when apps request them at runtime. The Android fine-grained runtime permission model... Read More about A comparative study of android users’ privacy preferences under the runtime permission model.

Privacy decision-making in the digital era: A game theoretic review (2017)
Journal Article
Anastasopoulou, K., Kokolakis, S., & Andriotis, P. (2017). Privacy decision-making in the digital era: A game theoretic review. Lecture Notes in Artificial Intelligence, 10292 LNCS, 589-603. https://doi.org/10.1007/978-3-319-58460-7_41

© Springer International Publishing AG 2017. Information privacy is constantly negotiated when people interact with enterprises and government agencies via the Internet. In this context, all relevant stakeholders take privacy-related decisions. Indiv... Read More about Privacy decision-making in the digital era: A game theoretic review.

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network (2016)
Journal Article
Li, S., Tryfonas, T., Russell, G., & Andriotis, P. (2016). Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network. IEEE Transactions on Cybernetics, 46(8), 1749-1759. https://doi.org/10.1109/TCYB.2016.2537649

© 2015 IEEE. Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess... Read More about Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

A study on usability and security features of the Android pattern lock screen (2016)
Journal Article
Andriotis, P., Oikonomou, G., Mylonas, A., & Tryfonas, T. (2016). A study on usability and security features of the Android pattern lock screen. Information and Computer Security, 24(1), 53-72. https://doi.org/10.1108/ICS-01-2015-0001

© Emerald Group Publishing Limited. Purpose: - The Android pattern lock screen (or graphical password) is a popular user authentication method that relies on the advantages provided by the visual representation of a password, which enhance its memora... Read More about A study on usability and security features of the Android pattern lock screen.