Hashida Haidros Rahima Manzil
@st.joseph engineering college, mangalore
Assistant Professor
EDUCATION
RESEARCH FELLOW: 2019 - Thesis Submitted in 2024
Pursuing PhD on cyber security and machine learning from Central University of Kerala since October 21'st, 2019
MASTER OF COMPUTER APPLICATIONS (MCA): 2011-2014
Completed MCA from National Institute of Electronics and Information Technology, Calicut (NIELIT Calicut) with 8.29 CGPA
BACHELORS DEGREE IN COMPUTER SCIENCE: 2008-2011
Completed B.Sc from Providence Women’s College, Calicut with 88%
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Hashida Haidros Rahima Manzil and S. Manohar Naik
Elsevier BV
Hashida Haidros Rahima Manzil and S. Manohar Naik
Springer Science and Business Media LLC
Hashida Haidros Rahima Manzil and S. Manohar Naik
Springer Science and Business Media LLC
AbstractMalware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.
Hashida Haidros Rahima Manzil and Manohar Naik S
IEEE
Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively.
Hashida Haidros Rahima Manzil and Manohar S Naik
IEEE
In the context of the COVID-19 pandemic the malicious actors actively creating COVID-themed android malicious apps and without much attention user may often grant all the required permissions to install those fake apps. The Android permissions are crucial sources of vulnerability. This vulnerability often leads to major privacy threats. In this work COVID-themed android malwares were collected and analyzed to develop a detection framework based on the static feature permission and machine learning techniques. The proposed system analyses 100 COVID-themed fake applications which released in 2020. The sensitive permissions are selected using Recursive Feature Elimination (RFE) technique. The study shows better accuracy of 0.830 and 0.812 with Decision tree classifier and Random forest classifier respectively.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
1. ‘Detection Approaches for Android Malware: Taxonomy and Review Analysis’, in Expert Systems With Applications’ on 01.11.2023, DOI:
2. ‘Android Ransomware Detection using a Novel Hamming Distance based Feature Selection’, in Journal of Computer Virology and Hacking Techniques’ on 23.06.2023, DOI:
3. ‘Android Malware Category Detection using a Novel Feature Vector-Based Machine Learning Model’ in an ESCI journal named Cybersecurity on 09.03.2023, DOI: 10.1186/s42400-023-00139-y
4. ‘DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques in IEEE Digital explorer on 17.03.2023, DOI:10.1109/.
5. ‘COVID-Themed Android Malware Analysis and Detection Framework Based on Permissions’ in Association with IEEE International Conference for Advancement in Technology (ICONAT), 2022, pp. 1-5, DOI: 10.1109/
6. ‘An overview of Cyber security: Impacts and Mitigations’, ISBN: 978-1-6654-2578-0.
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
• Design Patent: ML based Android Ransomware Detection Device. Ap plication Number: 405816-001, Date of Registration: 27/01/2024, Date of Issue: 01/03/2024.