Security analysis for authentication and authorisation in mobile phone

Lewandowski, Piotr; Felkner, Anna; Janiszewski, Marek

doi:10.15199/48.2019.08.29

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…It possesses a new capability to prompt a fake alert dialogue to phish user credentials, hijack opening specifics URLs, and read and write data in the user clipboard. Work by [12] found AceDeciever that infects any Apple device connected to infected PC (personal computer) were capable of obtaining Apple ID (identification) and password. Finally, work by [13] found Keyraider worm, where it intercepted iTunes traffic and stole user login credentials, GUID (Globally Unique Identifier) devices Apple requests push service certificates and private keys, and iTunes receipts for purchase.…”

Section: A Ios Malware Attacksmentioning

confidence: 99%

Mobile Malware Classification for iOS Inspired by Phylogenetics

Husainiamer¹,

Saudi²,

Ahmad³

et al. 2021

IJACSA

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Cyber-attacks such as ransomware, data breaches, and phishing triggered by malware, especially for iOS (iPhone operating system) platforms, are increasing. Yet not much works on malware detection for the iOS platform have been done compared to the Android platform. Hence, this paper presents an iOS malware classification inspired by phylogenetics. It consists of mobile behaviour, exploits, and surveillance features. The new iOS classification helps to identify, detect, and predict any new malware variants. The experiment was conducted by using hybrid analysis, with twelve (12) malwares datasets from the Contagio Mobile website. As a result, twenty-nine (29) new classifications have been developed. One hundred (100) anonymous mobile applications (50 from the Apple Store and 50 from iOS Ninja) have been used for evaluation. Based on the evaluation conducted, 13% of the mobile applications matched with the developed classifications. In the future, this work can be used as guidance for other researchers with the same interest.

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Section: A Ios Malware Attacksmentioning

confidence: 99%