Typosquat Cyber Crime Attack Detection via Smartphone

Abstract

A Smartphone is a multi-purpose device that can act as both mediums of communications and entertainment due to the availability of various sensors and services, such as SMS, NFC and Bluetooth. Through these functionalities, Smartphone owner can exchange information to each other by sharing links or even files. However, an attacker see these as an advantage to perform an Advanced Persistent Threat (APT) attack. APT is an attack which incorporates both social engineering attack and malware. In this paper, the authors will shed light on how APT attack through spear phishing can occur in Smartphone and how to detect it. First, the authors will examine the tactics that can be used by the attacker to perform a successful social engineering attack. Then, based on the discussion that has been made, the authors have used a machine learning algorithm to classify whether a certain URL is a phish or not. Lastly, the authors have evaluated the propose technique using machine learning and obtained more than 90% accuracy. This proves, that the proposed technique would able to help mitigating APT attack through spear phishing in the Smartphone.