Behavioral Model For Live Detection of Apps Based Attack

TL;DR

This paper proposes a behavioral model for detecting app-based attacks on smartphones by analyzing parameters like power, battery, and data usage across different network configurations, enhancing security against various threats.

Contribution

It introduces a novel attack vulnerability model and a behavioral analysis scheme (ABMA) for real-time intrusion detection on smartphones.

Findings

The ABMA scheme effectively detects app-based attacks.

Analysis across WiFi and mobile data configurations shows robustness.

Simulation results confirm the model's effectiveness.

Abstract

Smartphones with the platforms of applications are gaining extensive attention and popularity. The enormous use of different applications has paved the way to numerous security threats. The threats are in the form of attacks such as permission control attacks, phishing attacks, spyware attacks, botnets, malware attacks, privacy leakage attacks. Moreover, other vulnerabilities include invalid authorization of apps, compromise on the confidentiality of data, invalid access control. In this paper, an application-based attack modeling and attack detection is proposed. Due to A novel attack vulnerability is identified based on the app execution on the smartphone. The attack modeling involves an end-user vulnerable application to initiate an attack. The vulnerable application is installed at the background end on the smartphone with hidden visibility from the end-user. Thereby, accessing the confidential information. The detection model involves the proposed technique of an Application-based Behavioral Model Analysis (ABMA) scheme to address the attack model. The model incorporates application-based comparative parameter analysis to perform the process of intrusion detection. The ABMA is estimated by using the parameters of power, battery level, and the data usage. Based on the source internet accessibility, the analysis is performed using three different configurations as, WiFi, mobile data, and the combination of the two. The simulation results verify and demonstrates the effectiveness of the proposed model.