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Android Malware and Ransomware Detection using Deep Learning (Advert Reference: RDF20/EE/CIS/ISSAC)

  • Full or part time
  • Application Deadline
    Friday, January 24, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Android OS is extremely popular since the last few years and it is predominantly used in smart phones 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. The second supervisor is Dr Longzhi Yang.

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.

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.



Funding Notes

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.

References

Sharma, R., Issac, B. & K., Kalita, H. R. (2019). Intrusion Detection and Response System Inspired by the Defense Mechanism of Plants, IEEE Access, IEEE, ISSN 2169-3536, vol. 7, 52427-52439.

Imam, N., Issac, B. & and Jacob, S.M., (2019). A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift, International Journal of Computational Intelligence and Applications (IJCIA), Imperial College Press (World Scientific Europe), 18(2), ISSN 1469-0268, 17 pages.

Sharma, R. K., Kalita, H. R. & Issac, B. (2018). Are Machine Learning Based Intrusion Detection System Always Secure? An Insight Into Tampered Learning, Journal of Intelligent and Fuzzy Systems, IOS Press, ISSN 1064-1246, 35(3), pp. 3635-3651.

Shah, S. A. R., Issac, B. & Jacob, S. M. (2018). Intelligent Intrusion Detection System through Combined and Optimized Machine Learning, International Journal of Computational Intelligence and Applications (IJCIA), Imperial College Press (World Scientific Europe), ISSN 1469-0268, 17(2), 17 pages.

Shah, S. A. R., & Issac, B. (2018). Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System, Future Generation Computer Systems, Elsevier, ISSN 0167-739X, Vol. 80, 157-170.

Sharma, R. K., Kalita, H. R. & Issac, B. (2016). Plant-based Biologically Inspired Intrusion Response Mechanism: An insight into the proposed model PIRIDS, Journal of Information Assurance and Security (JIAS), ISSN 1554-1010, 11(6), 340-347.

Gao, X., Zhou, C., Chao, F., Yang, L., Lin, C., Xu, T., Shang, C., Shen, Q. (2019). A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution, Knowledge-Based Systems, 182. p. 104802.

Wei, B., Hamad, R., Yang, L., He, X., Wang, H., Gao, B., Woo, W. (2019). A Deep-Learning-Driven Light-Weight Phishing Detection Sensor, Sensors, 19 (19).

N Naik, P Jenkins, R Cooke, L Yang (2018). Honeypots that bite back: A fuzzy technique for identifying and inhibiting fingerprinting attacks on low interaction honeypots, Proceedings of 2018 IEEE International Conference on Fuzzy Systems.

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