Lightweight Cryptographic and Artificial Intelligence Models for Anti-smishing

Abstract

The utilization of Short Message Service (SMS) in smartphones has expanded quickly during recent years. As text informing is the most prudent choice accessible to arrive at masses, it is broadly utilized for offering buy focal points and as a promoting medium. Roughly billions of instant SMSs are sent each hour worldwide, so the expanding utilization of these messages makes it profoundly suitable for assailants. Over 90% of SMS messages are opened within a few seconds, which shows SMS's importance, making it the focus of cyber-attacks. Smishing is one of the cybercrime types that combine SMS and phishing in which trespassers send SMS containing suspicious links and content to the victims. Usually, smishing directs users to harmful websites to silently download malware or show interfaces like legitimate sites to attract users to fill in their sensitive information, i.e., passwords. This study proposes two automated models for detecting smishing by employing Lightweight cryptographic and artificial intelligence (AI) techniques. For the first model, and since there is no official validation stage in SMS construction, we developed our first contribution using a lightweight cryptographic SMS model to protect SMS and detect possible phishing or fake content. We select the standard, popular signature schemes, and evaluating the performance according to time and signature size overhead. The results showed the proposed model's effectiveness to guarantee the main security goals, authentication, integrity, and non-repudiation. Our second model deals with legacy SMS, which doesn't follow the validation stage, so we collected the SMS dataset for an AI model and manually labeled their classes as phishing or legitimate according to their link content. We preprocessed the SMS dataset to extract the link-based distinguishing features, and we applied feature selection techniques to obtain the best performance. We experimented with two artificial intelligence classifiers and