Impact

Outreach and Media. References to our work from media outlets.


References in the Media

  • 22/05/2023: “Digital Forensic Science links science and crime”,
    Article in The Time of India - Education Times, Print Edition.
  • 14/03/2023: “Artificial Intelligence in Education - AIED”,
    Article in Medium.
  • 25/02/2021: “To Allow, or Deny?”,
    Article in Faculti about our work on Android permissions.
  • 10/04/2018: “The endless game of cat and mouse with spammers”,
    Eureka, Kyocera, now archived. A discussion about bots on social media.
  • 04/04/2018: “Impact of User Data Privacy Management Controls on Mobile Device Investigations”,
    Article in HAL open science.
  • 09/03/2017: “MaMaDroid: Detecting Android malware by building Markov chains of behavorial models”,
    the morning paper. A presentation of our Android malware detection system.
  • 08/11/2016: “Smartphone Message Sentiment Analysis”,
    Article in HAL open science.


Influence and Impact

Mariconti, E. et al. 2017. MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models. 24th Network and Distributed System Security Symposium (NDSS 2017) (2017), 1–15.



Andriotis, P. et al. 2017. A Comparative Study of Android Users’ Privacy Preferences Under the Runtime Permission Model. Human Aspects of Information Security, Privacy and Trust (Cham, 2017), 604–622.



Andriotis, P. et al. 2014. Complexity Metrics and User Strength Perceptions of the Pattern-Lock Graphical Authentication Method. Human Aspects of Information Security, Privacy, and Trust (Cham, 2014), 115–126.



Onwuzurike, L. et al. 2019. MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version). ACM Transactions on Privacy and Security. 22, 2 (Apr. 2019). DOI:https://doi.org/10.1145/3313391.



Andriotis, P. et al. 2014. Smartphone Message Sentiment Analysis. Advances in Digital Forensics X (Berlin, Heidelberg, 2014), 253–265.



Andriotis, P. et al. 2016. A study on usability and security features of the Android pattern lock screen. Information and Computer Security. 24, 1 (2016), 53–72. DOI:https://doi.org/10.1108/ICS-01-2015-0001.



Andriotis, P. et al. 2013. JPEG steganography detection with Benford’s Law. Digital Investigation. 9, 3 (2013), 246–257. DOI:https://doi.org/10.1016/j.diin.2013.01.005.



Tryfonas, T. et al. 2016. Mass Surveillance in Cyberspace and the Lost Art of Keeping a Secret. Human Aspects of Information Security, Privacy, and Trust (Cham, 2016), 174–185.



Andriotis, P. et al. 2016. Highlighting Relationships of a Smartphone’s Social Ecosystem in Potentially Large Investigations. IEEE Transactions on Cybernetics. 46, 9 (Sep. 2016), 1974–1985. DOI:https://doi.org/10.1109/TCYB.2015.2454733.



Andriotis, P. and Tryfonas, T. 2016. Impact of User Data Privacy Management Controls on Mobile Device Investigations. Advances in Digital Forensics XII (Cham, 2016), 89–105.



Andriotis, P. et al. 2016. Permissions snapshots: Assessing users’ adaptation to the Android runtime permission model. 2016 IEEE International Workshop on Information Forensics and Security (WIFS) (Dec. 2016), 1–6.



Andriotis, P. et al. 2018. Studying users’ adaptation to Android’s run-time fine-grained access control system. Journal of Information Security and Applications. 40, (2018), 31–43. DOI:https://doi.org/10.1016/j.jisa.2018.02.004.



Anastasopoulou, K. et al. 2017. Privacy Decision-Making in the Digital Era: A Game Theoretic Review. Human Aspects of Information Security, Privacy and Trust (Cham, 2017), 589–603.


Andriotis, P. et al. 2013. A Pilot Study on the Security of Pattern Screen-lock Methods and Soft Side Channel Attacks. Proceedings of the Sixth ACM Conference on Security and Privacy in Wireless and Mobile Networks (New York, NY, USA, 2013), 1–6.


Andriotis, P. et al. 2012. Forensic analysis of wireless networking evidence of Android smartphones. 2012 IEEE International Workshop on Information Forensics and Security (WIFS) (Dec. 2012), 109–114.


Andriotis, P. et al. 2013. Multilevel visualization using enhanced social network analysis with smartphone data. International Journal of Digital Crime and Forensics (IJDCF). 5, 4 (2013), 34–54. DOI:https://doi.org/10.4018/ijdcf.2013100103.


Li, S. et al. 2016. Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network. IEEE Transactions on Cybernetics. 46, 8 (Aug. 2016), 1749–1759. DOI:https://doi.org/10.1109/TCYB.2016.2537649.


Li, S. et al. 2015. Real-Time Monitoring of Privacy Abuses and Intrusion Detection in Android System. Human Aspects of Information Security, Privacy, and Trust (Cham, 2015), 379–390.


Andriotis, P. and Oikonomou, G. 2015. Messaging Activity Reconstruction with Sentiment Polarity Identification. Human Aspects of Information Security, Privacy, and Trust (Cham, 2015), 475–486.


Andriotis, P. et al. 2015. A Framework for Describing Multimedia Circulation in a Smartphone Ecosystem. Advances in Digital Forensics XI (Cham, 2015), 251–267.


Aboluwarin, O. et al. 2016. Optimizing Short Message Text Sentiment Analysis for Mobile Device Forensics. Advances in Digital Forensics XII (Cham, 2016), 69–87.


Li, S. et al. 2019. Distributed consensus algorithm for events detection in cyber-physical systems. IEEE Internet of Things Journal. 6, 2 (2019), 2299–2308. DOI:https://doi.org/10.1109/JIOT.2019.2906157.


Andriotis, P. and Takasu, A. 2018. Emotional Bots: Content-based Spammer Detection on Social Media. 2018 IEEE International Workshop on Information Forensics and Security (WIFS) (Dec. 2018), 1–8.


Andriotis, P. et al. 2014. On the Development of Automated Forensic Analysis Methods for Mobile Devices. Trust and Trustworthy Computing (Cham, 2014), 212–213.


McCarthy, A. et al. 2021. Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks. 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) (2021), 1–8.

Read, H. et al. 2015. An Extensible Platform for the Forensic Analysis of Social Media Data. Human Aspects of Information Security, Privacy, and Trust (Cham, 2015), 404–414.

Andriotis, P. and Takasu, A. 2020. To Allow, or Deny? That is the Question. HCI for Cybersecurity, Privacy and Trust (Cham, 2020), 287–304.

Andriotis, P. et al. 2022. Bu-Dash: A Universal and Dynamic Graphical Password Scheme. HCI for Cybersecurity, Privacy and Trust (Cham, 2022), 209–227.

McCarthy, A. et al. 2022. Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey. Journal of Cybersecurity and Privacy. 2, 1 (2022), 154–190. DOI:https://doi.org/10.3390/jcp2010010.

Nemorin, S. et al. 2023. AI hyped? A horizon scan of discourse on Artificial Intelligence in Education (AIED) and development. Learning, Media and Technology. 48, 1 (2023), 38–51. DOI:https://doi.org/10.1080/17439884.2022.2095568.

Andriotis, P. et al. 2023. Bu-Dash: a universal and dynamic graphical password scheme (extended version). International Journal of Information Security. 22, 2 (2023), 381–401. DOI:https://doi.org/10.1007/s10207-022-00642-2.

McCarthy, A. et al. 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, (2023), 103398. DOI:https://doi.org/10.1016/j.jisa.2022.103398.