Review
Computer Science, Information Systems
Tejpal Sharma, Dhavleesh Rattan
Summary: Smartphones have become an essential necessity in daily life due to their widespread usage, but attackers are continuously developing new techniques to steal data, particularly related to privacy. This study aims to report a systematic literature review on malicious application detection in the Android operating system, identifying different techniques and categorizing features for investigation of malicious applications. The research highlights the need for new hybrid techniques to combat malware activities and provides recommendations for future research.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Nektaria Potha, V Kouliaridis, G. Kambourakis
Summary: The paper introduces a sophisticated Extrinsic Random-based Ensemble (ERBE) method for malware detection, showing that it can effectively improve detection results by utilizing multiple external instances and different classification features. Experimental results on AndroZoo benchmark corpora verify the suitability of a random-based heterogeneous ensemble for this task and exhibit the effectiveness of the method, in some cases improving the best reported results by more than 5%.
CONNECTION SCIENCE
(2021)
Article
Computer Science, Information Systems
Alfonso Gomez, Antonio Munoz
Summary: The proliferation of Android-based devices has made them a prime target for attackers. This study presents a supervised learning technique that demonstrates promising results in Android malware detection.
Article
Automation & Control Systems
Wei Yuan, Yuan Jiang, Heng Li, Minghui Cai
Summary: This article focuses on on-device Android malware detection, proposing a lightweight detector based on the broad learning method. The detector mainly uses one-shot computation for model training, achieving higher accuracy than shallow learning models and approaching deep learning models.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Mohammed M. Alani, Ali Ismail Awad
Summary: The Android operating system is widely used, but faces security challenges due to the rapid adoption. This paper presents a lightweight Android malware detection system based on explainable machine learning, achieving an accuracy exceeding 98%.
Article
Computer Science, Artificial Intelligence
Alejandro Guerra-Manzanares, Marcin Luckner, Hayretdin Bahsi
Summary: The study presents a novel method to detect and address concept drift in Android malware detection, maintaining high performance over an extended period and minimizing the need for model retraining efforts.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Harshit Kumar, Biswadeep Chakraborty, Sudarshan Sharma, Saibal Mukhopadhyay
Summary: In this paper, a hardware-based malware detector (XMD) is proposed, which achieves better detection performance compared to currently used hardware performance counter (HPC) detectors. It is demonstrated that adding non-core telemetry channels improves the separability of benign and malware classes, resulting in performance gains. Experimental results show that XMD achieves a detection performance of 86.54% with a false positive rate of 2.9% on a mobile device.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Theory & Methods
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yang Xiang
Summary: Deep Learning (DL) is a disruptive technology that has revolutionized cyber security research, especially in the detection and classification of Android malware. While offering many advantages, DL faces challenges such as choice of architecture, feature extraction, and obtaining high-quality data.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Information Systems
Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak
Summary: The study proposed two novel attack strategies against Android malware detection systems, ultimately achieving the goal of increasing the fooling rate by making minimum modifications to the detection models. The research demonstrates that the proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.
INFORMATION SYSTEMS FRONTIERS
(2021)
Article
Computer Science, Artificial Intelligence
Durmus Ozkan Sahin, Oguz Emre Kural, Sedat Akleylek, Erdal Kilic
Summary: Mobile and wireless technology have made mobile devices an important part of our lives. However, Android, being the leading operating system, is also the most targeted platform by attackers. This study proposes a machine learning-based malware detection system that uses feature selection methods to distinguish Android malware from benign applications.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Syed Ibrahim Imtiaz, Saif ur Rehman, Abdul Rehman Javed, Zunera Jalil, Xuan Liu, Waleed S. Alnumay
Summary: As the use of Android smartphones becomes more widespread, there is an increasing need for more efficient methods to detect and prevent malicious applications from attacking and compromising user devices.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Review
Computer Science, Information Systems
Shaashwat Agrawal, Sagnik Sarkar, Ons Aouedi, Gokul Yenduri, Kandaraj Piamrat, Mamoun Alazab, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
Summary: The rapid development of the Internet and smart devices has led to a surge in network traffic, making the infrastructure more complex and heterogeneous. The predominant usage of mobile phones, wearable devices, and autonomous vehicles generates a huge amount of data every day. Intrusion detection systems play a significant role in ensuring the security and privacy of these devices. Machine Learning and Deep Learning with Intrusion Detection Systems have gained momentum due to their high classification accuracy. However, the need to store and communicate data to a centralized server potentially compromises privacy and security. On the other hand, Federated Learning provides a privacy-preserving decentralized learning technique that trains models locally and transfers parameters to the centralized server. This paper aims to provide a comprehensive review of the use of Federated Learning in intrusion detection systems, discussing various types of IDS, relevant ML approaches, and associated issues. The paper also presents a detailed overview of the implementation of Federated Learning in anomaly detection and identifies the challenges and potential solutions for future research.
COMPUTER COMMUNICATIONS
(2022)
Article
Chemistry, Analytical
Hasan Alkahtani, Theyazn H. H. Aldhyani
Summary: With the rapid expansion of smartphone usage, malicious attacks against Android mobile devices are increasing. This study successfully detected malware in Android applications using machine learning and deep learning approaches, and demonstrated their efficiency compared to existing security systems.
Review
Computer Science, Information Systems
Abdulaziz Alzubaidi
Summary: The widespread use of smartphones has led to the development of numerous applications, but has also made them vulnerable to malware attacks. This paper discusses the concepts and risks associated with malware, as well as the current methods and mechanisms used to detect malware.
Article
Chemistry, Multidisciplinary
Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun, Zubair O. Alanamu
Summary: As mobile and internet technology advances, new mobile security risks emerge. Conventional machine learning algorithms perform poorly in detecting malicious apps due to imbalanced datasets. In this study, a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm was developed. The proposed FPA outperforms baseline classifiers and existing ML-based Android malware detection models, with an accuracy of 98.94% and an AUC value of 0.999. Further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Innocent Uzougbo Onwuegbuzie, Shukor Abd Razak, Ismail Fauzi Isnin, Arafat Al-dhaqm, Nor Badrul Anuar
Summary: This article proposes a Prioritized Shortest Path Computation Mechanism (PSPCM) to address the issues of heterogeneous data and inefficient power management. The mechanism prioritizes different classes of data based on their priority and routes them through the shortest and power-efficient paths. PSPCM outperforms related mechanisms with higher Packet Delivery Ratio (PDR) and lower power consumption, resulting in better battery saving and prolonged operational lifetime while accommodating data with varying priorities.
Article
Environmental Sciences
Ali Feizollah, Nor Badrul Anuar, Riyadh Mehdi, Ahmad Firdaus, Ainin Sulaiman
Summary: This study analyzes the discourse related to halal vaccines on Facebook and Twitter using aspect-based sentiment analysis and text emotion analysis. It finds differences in the discussion and sentiment towards halal vaccines between the two platforms.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Mathematics, Interdisciplinary Applications
Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad Shiraz
Summary: The study aims to enhance classification accuracy using effective feature selection algorithms, utilizing different feature selection methods and SVM classifier to classify data, achieving high accuracy on microarray and heart disease datasets.
Article
Computer Science, Information Systems
Rahat Ullah, Abdullah Gani, Muhammad Shiraz, Imran Khan Yousufzai, Khalid Zaman
Summary: This paper proposes an Auction Mechanism-based Sectored-FFR (AMS-FFR) scheme for optimally distributing bandwidth resources to individual users in realistic multicellular network deployments. Simulation results show that the presented scheme outperforms prevailing FFR schemes in terms of achievable throughput, average sum rate, and user satisfaction.
Article
Computer Science, Information Systems
Mohamed M. Mostafa, Ali Feizollah, Nor Badrul Anuar
Summary: This study provides a bibliometric analysis on YouTube as an information source, revealing the development trends and key topics in YouTube research. The findings show that YouTube research underwent rapid growth from 2008 to 2017 and has entered a stage of consolidation and stabilization from 2017 to 2021. The USA, Turkey, and the UK are the countries with the highest number of publications, while a small number of journals published the majority of relevant papers. Keyword analysis indicates that video sharing, web-based learning, and COVID-19 are the hot topics in research. The results of Multiple Correspondence Analysis (MCA) classify the research into three conceptual clusters: user-generated content, health and medical issues, and information quality.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Rahim Khan, Jason Teo, Mian Ahmad Jan, Sahil Verma, Ryan Alturki, Abdullah Ghani
Summary: This article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Simulation results validate its exceptional performance compared to field proven approaches.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Business
Hamza H. M. Altarturi, Adibi Rahiman Md Nor, Noor Ismawati Jaafar, Nor Badrul Anuar
Summary: Given the challenges imposed by the COVID-19 pandemic, agribusinesses are urged to adopt e-commerce and advanced technologies to market and sell agricultural products. Agricultural e-commerce utilizing advanced technologies not only facilitates sustainable economic growth and gender equality but also enables farmers to access new markets, bypass intermediaries, and reduce waste. However, the effectiveness of agricultural e-commerce depends on addressing the emerging challenges from both agricultural and e-commerce perspectives. This study aims to provide a comprehensive roadmap of technological advancements and challenges in agricultural e-commerce, along with proposing a conceptual architecture for best practices.
ELECTRONIC COMMERCE RESEARCH
(2023)
Article
Chemistry, Analytical
Khalid Saeed, Wajeeha Khalil, Ahmad Sami Al-Shamayleh, Iftikhar Ahmad, Adnan Akhunzada, Salman Z. ALharethi, Abdullah Gani
Summary: The exponentially growing concern of cyber-attacks on extremely dense underwater sensor networks (UWSNs) and the evolution of UWSNs digital threat landscape has brought novel research challenges and issues. This research implements an active attack in the Adaptive Mobility of Courier Nodes in Threshold-optimized Depth-based Routing (AMCTD) protocol to evaluate its performance. The preliminary research findings show that active attack drastically lowers the AMCTD protocol's performance.
Article
Chemistry, Analytical
Abdelmuttlib Ibrahim Abdalla Ahmed, Siti Hafizah Ab Hamid, Abdullah Gani, Ahmed Abdelaziz, Mohammed Abaker
Summary: The rapid growth of smart devices connected to the Internet of Things (IoT) has raised challenges in terms of interoperability. A service-oriented architecture for IoT (SOA-IoT) has been proposed to address these challenges by integrating web services into sensor networks via IoT-optimized gateways. Trust-based approaches have been found to outperform non-trust-based approaches in service composition for IoT. These approaches use a trust and reputation system to select appropriate service providers based on trust values computed from self-observation and recommendations. However, a formal method for trust-based service composition is lacking in the IoT. This study used a formal method to represent trust-based service management components in the IoT and identified the impact of trust attacks on service composition.
Article
Green & Sustainable Science & Technology
Khalid Saeed, Wajeeha Khalil, Ahmad Sami Al-Shamayleh, Sheeraz Ahmed, Adnan Akhunzada, Salman Z. Alharthi, Abdullah Gani
Summary: This research analyzes the security-based schemes in underwater wireless sensor networks (UWSNs) and categorizes them into five sub-categories. It discusses the major contributions, techniques used, possible future research issues, and implementation tools for each security-based scheme. The identified open research issues and future trends can be further explored by the research community.
Article
Chemistry, Multidisciplinary
Mushtaq Khan, Rahim Khan, Nadir Shah, Abdullah Ghani, Samia Allaoua Chelloug, Wasif Nisar, Jason Teo
Summary: This paper presents a neighborhood-based smart slot allocation scheme for IoT networks, with a focus on mobile devices. The scheme resolves concurrent communication issues by maintaining two types of slots (dedicated and reserved) and assigning them to devices based on a FCFS basis. Simulation results show that the proposed scheme outperforms existing approaches in terms of lifetime, slot allocation, and slot waiting time.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Zainab Alansari, Nor Badrul Anuar, Amirrudin Kamsin, Mohammad Riyaz Belgaum
Summary: This article presents a lightweight system, RPLAD3, for detecting attacks in the RPL protocol. It effectively identifies grayhole, blackhole, and selective forwarding attacks, improves packet delivery ratio, and reduces power consumption.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Shehzad Haider, Wajeeha Khalil, Ahmad Sami Al-Shamayleh, Adnan Akhunzada, Abdullah Gani
Summary: Open source software (OSS) has gained popularity, but it faces quality problems, security issues, and challenges. This research aims to identify and address the risk factors associated with OSS and provide practices for risk mitigation. A systematic literature review and questionnaire survey were conducted to identify risk factors and validate findings. 14 risk factors and 31 practices for mitigating them in OSS development were identified. Focusing on these factors can minimize risks and improve software productivity and reliability.
Article
Multidisciplinary Sciences
Wael F. Elsersy, Nor Badrul Anuar, Mohd Faizal Ab Razak
Summary: This study proposes a framework for detecting Android rooted devices using machine learning classification techniques, and the experimental evaluation shows a high level of accuracy for the framework.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)