Firmware Attack Detection on IoT Devices Using Deep Binary Pattern Classification Mining (FA-PCM)
Author:
Arul E.ORCID,
Punidha A.ORCID
Publisher
Springer Singapore
Reference17 articles.
1. Kancherla, K., & Mukkamala, S. (2013). Image visualization based malware detection. In 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS) (pp. 40–44). IEEE.
2. Kunwar, R. S., & Sharma, P. (2017). Framework to detect malicious codes embedded with JPEG images over social networking sites. In International Conference on Innovations in information Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE. https://doi.org/10.1007/978-1-5090-3294-5.
3. Arul, E., & Manikandan, V. (2016). Malware detection using higher order statistical parameters. In S. Subramanian, R. Nadarajan, S. Rao, S. Sheen (Eds.), Digital Connectivity – Social Impact. CSI 2016. Communications in computer and information science (Vol. 679). Singapore: Springer. https://doi.org/10.1007/978-981-10-3274-5_4.
4. Burguera, I., Zurutuza, U., & Nadjm-Tehrani, S. (2011). Crowdroid: Behavior-based malware detection system for android. In Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices (pp. 15–26). ACM.
5. Shamili, A. S., Bauckhage, C., & Alpcan, T. (2010). Malware detection on mobile devices using distributed machine learning. In 2010 20th International Conference on Pattern Recognition (ICPR) (pp. 4348–4351). IEEE.