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Intrusion Detection System for Android: Linux Kernel System Salls Analysis
KTH, School of Information and Communication Technology (ICT).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Smartphones provide access to a plethora of private information potentially leading to financial and personal hardship, hence they need to be well protected. With new Android malware obfuscation and evading techniques, including encrypted and downloaded malicious code, current protection approaches using static analysis are becoming less effective. A dynamic solution is needed that protects Android phones in real time. System calls have previously been researched as an effective method for Android dynamic analysis. However, these previous studies concentrated on analysing system calls captured in emulated sandboxed environments, which does not prove the suitability of this approach for real time analysis on the actual device.

This thesis focuses on analysis of Linux kernel system calls on the ARMv8 architecture. Given the limitations of android phones it is necessary to minimise the resources required for the analyses, therefore we focused on the sequencing of system calls. With this approach, we sought a method that could be employed for a real time malware detection directly on Android phones. We also experimented with different data representation feature vectors; histogram, n-gram and co-occurrence matrix. All data collection was carried out on a real Android device as existing Android emulators proved to be unsuitable for emulating a system with the ARMv8 architecture. Moreover, data were collected on a human controlled device since reviewed Android event generators and crawlers did not accurately simulate real human interactions.

The results show that Linux kernel sequencing carry enough information to detect malicious behaviour of malicious applications on the ARMv8 architecture. All feature vectors performed well. In particular, n-gram and co-occurrence matrix achieved excellent results. To reduce the computational complexity of the analysis, we experimented with including only the most commonly occurring system calls. While the accuracy degraded slightly, it was a worthwhile trade off as the computational complexity was substantially reduced.

Abstract [sv]

Smartphones ger tillgång till en uppsjö av privat information som potentiellt kan leda till finansiella och personliga svårigheter. Därför måste de vara väl skyddade. En dynamisk lösning behövs som skyddar Android-telefoner i realtid. Systemanrop har tidigare undersökts som en effektiv metod för dynamisk analys av Android. Emellertid fokuserade dessa tidigare studier på systemanrop i en emulerad sandbox miljö, vilket inte visar lämpligheten av detta tillvägagångssätt för realtidsanalys av själva enheten.

Detta arbete fokuserar på analys av Linux kärnan systemanrop på ARMv8 arkitekturen. Givet begränsningarna som existerar i Android-telefoner är det väsentligt att minimera resurserna som krävs för analyserna. Därför fokuserade vi på sekvenseringen av systemanropen. Med detta tillvägagångssätt sökte vi en metod som skulle kunna användas för realtidsdetektering av skadliga program direkt på Android-telefoner. Vi experimenterade dessutom med olika funktionsvektorer för att representera data; histogram, n-gram och co-occurrence matriser. All data hämtades från en riktig Android enhet då de existerande Android emulatorerna visade sig vara olämpliga för att emulera ett system med ARMv8 arkitekturen.

Resultaten visar att Linus kärnans sekvensering har tillräckligt med information för att upptäcka skadligt beteende av skadliga applikationer på ARMv8 arkitekturen. Alla funktionsvektorer presterade bra. N-gram och cooccurrence matriserna uppnådde till och med lysande resultat. För att reducera beräkningskomplexiteten av analysen, experimenterade vi med att enbart använda de vanligaste systemanropen. Fast noggrannheten minskade lite, var det värt uppoffringen eftersom beräkningskomplexiteten reducerades märkbart.

Place, publisher, year, edition, pages
2017. , p. 93
Series
TRITA-ICT-EX ; 2017:142
Keywords [en]
Android, security, malware, detection, system calls, ARM
Keywords [sv]
Android, säkerhet, malware, detektion, systemanrop
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-222382OAI: oai:DiVA.org:kth-222382DiVA, id: diva2:1181270
Subject / course
Electrical Engineering
Educational program
Master of Science -Security and Mobile Computing
Supervisors
Examiners
Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-02-08Bibliographically approved

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