Behavioral Model for Live Detection of Apps Based Attack

Smartphones with the platforms of applications are gaining extensive attention and popularity. The enormous use of different applications has paved the way for numerous security threats. The threats are in the form of attacks such as permission control attacks, phishing attacks, spyware attacks, botnets, malware attacks, and privacy leakage attacks. Moreover, other vulnerabilities include invalid authorization of apps, compromise on the confidentiality of data, and invalid access control. In this article, an application-based attack modeling and attack detection are proposed as 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 data usage. Based on the source Internet accessibility, the analysis is performed using three different configurations, Wi-Fi, mobile data, and a combination of the two. The simulation results verify and demonstrate the effectiveness of the proposed model

Botnet · Computer security · Intrusion detection system · Malware · Mobile malware · Permission · Phishing · The Internet · World Wide Web · Advanced Malware Detection Techniques · Computer Science · Internet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection