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Fully funded PhD - Android Malware and Ransomware Detection using Deep Learning

Details

Deadline
Research Field
Professions and applied sciences
Funding Type
Funding
Career Stage
22
European Research Programme
Not funded by an EU programme

About

Outline

Advert Reference: RDF20/EE/CIS/ISSAC (https://www.findaphd.com/phds/project/?p116088)

Android OS is extremely popular since the last few years and it is predominantly used in smartphones and in the Internet of Things (IoT) devices. This has created an opportunity to be an effective target of malicious apps. Thus, there is a need for effective and portable malware and ransomware detection solutions. Malware tricks one into installing software that allows scammers to access their files and track what they are doing, while ransomware demands payment to ‘unlock’ your computer or files. Ransomware is a type of malware that blocks or limits access to your computer or files, and demands a ransom be paid to be unlocked.

In this research, we propose a deep learning approach for Android malware and ransomware detection. Using the raw sequence of the app’s API method calls, our approach will extract and learn the malicious and the benign patterns from the actual samples of datasets to detect Android malware. We will use deep neural network or similar approach, which uses permissions combination, intent filters, invalid certificate, the existence of APK file in the asset folder, API calls etc. as features to construct a deep learning network that can identify malicious from the benign ones. We will examine API packages’ calls as leading indicator of ransomware activity to discriminate ransomware with high accuracy before it harms the user’s device. We will use deep learning to identify a set of novel features with high discriminative power for separating ransomware and benign samples. Experiments would be done on multiple malware and ransomware datasets to prove that the proposed deep learning techniques would work effectively. Optimization of deep learning using bio-inspired metaheuristics algorithms would be applied to make the classification accuracy even better.

This project is supervised by Dr Biju Issac (bissac@ieee.org). The second supervisor is Dr Longzhi Yang. Interested students please contact Dr Biju Issac by email to discuss the proposal.

For further details of how to apply, entry requirements and the application form, see

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF20/EE/CIS/ISSAC) will not be considered.

Deadline for applications: Friday 24 January 2020

Start Date: 1 October 2020

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

What is funded

The studentship is available to Home/EU/ Worldwide students where a full stipend, paid for three years at RCUK rates (for 2019/20, this is £15,009 pa) and full fees.

Duration

Three years

Eligibility

Please note eligibility requirement:

• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.

• Appropriate IELTS score, if required.

• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

Organisation

Organisation name
Northumbria University
Organisation Country
More Information
Disclaimer:

The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.