Abstract
As the world strides into the digital world, cybersecurity has become an indispensable part of connected devices. Although we have developed cybersecurity measures that can effectively defend against malicious software, we don’t have an accurate solution against attacks like social engineering attack, scam calls or phishing. In this work, a novel detection system called m-isds(mobilized intrusion and spam detection system) architecture is proposed, borrowing ideas from the widely utilized advanced hybrid intrusion detection system(ids), combining with some novel concepts including machine learning, advanced hashing technologies and pattern-matching technologies that are secure and cryptographically safe to provide a solution to the proposed system with low false-positive rate and privacy infringement while remaining responsive and flexible against all types of attacks. The system aims to scan the content of the whole terminal on the fly, not only containing and defending against the threat of malicious softwares but also alerting the user of possible scams and spams, bringing the security goal of mobile devices to a whole new level.
Reference18 articles.
1. Janis Griffin (2021) What Is an Intrusion Detection System (IDS)? https://logicalread.com/intrusiondetection-system/
2. Wang X., Yu H. (2005). How to Break MD5 and Other Hash Functions. In: Cramer R. (eds) Advances in Cryptology – EUROCRYPT 2005. EUROCRYPT 2005. Lecture Notes in Computer Science, vol 3494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11426639_2
3. Xiaoyun Wang, Hongbo Yu, & Yiqun Lisa Yin (2005). Efficient Collision Search Attacks on SHA-0. In In Crypto (pp. 1–16). Springer-Verlag.
4. Sharfah Ratibah Tuan Mat, Mohd Faizal Ab Razak, Mohd Nizam Mohmad Kahar, Juliza Mohamad Arif, & Ahmad Firdaus (2021). A Bayesian probability model for Android malware detection. ICT Express.
5. APPLE INC. (2021) CSAM Detection Technical Summary. https://www.apple.com/childsafety/pdf/CSAM_Detection_Technical_Summary.pdf