Article
Chemistry, Multidisciplinary
Ammar Yahya Daeef, Ali Al-Naji, Ali K. Nahar, Javaan Chahl
Summary: Malware is a significant threat to modern businesses, and it is crucial to eliminate it from computer systems. A lightweight solution using artificial intelligence at the edge of the IT system is the most responsive option. This study used visualization analysis and Jaccard similarity to uncover patterns in API calls for high malware detection rates and quick execution. The results showed that random forest (RF) performed similarly to long short-term memory (LSTM) and deep graph convolutional neural networks (DGCNNs), indicating potential for real-time inference on edge devices.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Satheesh Kumar Sasidharan, Ciza Thomas
Summary: This paper introduces a new behavioral method for Android malware detection and classification, which decompiles the Android malware dataset to identify suspicious API classes/methods and generates an encoded list. It creates multiple sequence alignment for different malware families using the encoded patterns and applies it to generate profile hidden Markov model. The model classifies unknown applications as benign or malicious based on the log likelihood score, achieving an accuracy of 94.5%.
PERVASIVE AND MOBILE COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Gianni D'Angelo, Eslam Farsimadan, Massimo Ficco, Francesco Palmieri, Antonio Robustelli
Summary: The emergence of new and sophisticated malware targeting Android-based IoT devices poses security risks and the need for effective detection models and strategies. Federated Learning-based solutions, which use Machine Learning models without sharing user data, are being developed. However, these methods are affected by non-independent and identically distributed data. Privacy-preserving approaches using Markov chains and associative rules are proposed to handle malware classification in the IoT scenario. The approach achieves high accuracy and comparable runtime performance with centralized methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Gianni D'Angelo, Massimo Ficco, Francesco Palmieri
Summary: The study introduces an algorithm based on recurring subsequences alignment to infer malware behaviors, which can operate within dynamic analysis scenarios and shows excellent classification performance in experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Anson Pinhero, M. L. Anupama, P. Vinod, C. A. Visaggio, N. Aneesh, S. Abhijith, S. AnanthaKrishnan
Summary: With the rapid growth of malware, automatic classification faces challenges, this study explores a new approach combining malware visualization and deep learning classification, successfully improving classification accuracy and efficiency.
COMPUTERS & SECURITY
(2021)
Article
Materials Science, Characterization & Testing
Agnimitra Sengupta, Sudeepta Mondal, S. Ilgin Guler, Parisa Shokouhi
Summary: Impact echo (IE) is a popular nondestructive evaluation technique for detecting defects in concrete bridge decks. Traditional frequency domain analysis is limited in performance when dealing with limited training data, while time-frequency analysis and machine learning methods offer improved classification accuracy. This study explores a hybrid model combining hidden Markov model (HMM) and time-frequency analysis, which shows improved reliability in classification under different data conditions.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2022)
Article
Biotechnology & Applied Microbiology
Sakib Ferdous, Ibne Farabi Shihab, Nigel F. Reuel
Summary: Assigning enzyme commission (EC) numbers using sequence information alone has been explored through various algorithms, with performance benchmarks showing the best accuracy in the range of 300-450 amino acids. Among the classifiers, ECpred demonstrated the best consistency in feature space, indicating its reliability in predicting enzyme classifications. This research provides insights into optimal design spaces for generating new synthetic enzymes and the common ranges of amino acid composition in annotated enzymes.
BIOCHEMICAL ENGINEERING JOURNAL
(2022)
Article
Computer Science, Information Systems
Adi Lichy, Ofek Bader, Ran Dubin, Amit Dvir, Chen Hajaj
Summary: Internet traffic classification is important for QoE, QoS, intrusion detection, and traffic-trend analyses. Although there is no guarantee that DL-based solutions outperform ML-based ones, DL-based models have become the common default. This paper compares well-known DL-based and ML-based models and shows that, in the case of malicious traffic classification, state-of-the-art DL-based solutions do not necessarily outperform classical ML-based ones.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Information Systems
Mao Xiao, Chun Guo, Guowei Shen, Yunhe Cui, Chaohui Jiang
Summary: This paper presents a malware classification method based on PE files, using a new visualization method and deep learning technology to improve the accuracy and efficiency of malware classification.
COMPUTERS & SECURITY
(2021)
Article
Chemistry, Multidisciplinary
Norah Abanmi, Heba Kurdi, Mai Alzamel
Summary: The prevalence of malware attacks targeting IoT systems has raised concerns and emphasized the need for effective detection and defense mechanisms. However, detecting malware, especially those with new or unknown behaviors, is challenging. The main issue lies in its ability to hide, making it difficult to detect. Moreover, limited information on malware families restricts the availability of big data for analysis. This paper introduces a new Profile Hidden Markov Model (PHMM) for app analysis and classification in Android systems, while also dynamically identifying suspicious calls to reduce the risk of code execution. The experimental results demonstrate that the proposed Dynamic IoT malware Detection in Android Systems using PHMM (DIP) outperforms eight rival malware detection frameworks, achieving up to 96.3% accuracy at a 5% False Positive Rate (FP rate), 3% False Negative Rate (FN rate), and 94.9% F-measure.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Lara Saidia Fasci, Marco Fisichella, Gianluca Lax, Chenyi Qian
Summary: Recently, visualization-based approaches have been used together with signature-based techniques to detect variants of malware files. By modifying some bytes of executable files, attackers can modify the signature and evade signature-based detectors. In this paper, we propose a GAN-based architecture that allows attackers to generate malware variants in which the malware patterns found by visualization-based approaches are hidden, resulting in a new version of the malware that cannot be detected by both signature-based and visualization-based techniques. Experiments on a well-known malware dataset show a 100% success rate in generating new malware variants that are not detected by the state-of-the-art visualization-based technique.
COMPUTERS & SECURITY
(2023)
Article
Engineering, Chemical
Nabanita Dutta, Kaliannan Palanisamy, Paramasivam Shanmugam, Umashankar Subramaniam, Sivakumar Selvam
Summary: This study aims to compare the lifecycle costs (LCC) of pumping systems in different flow rates under healthy and faulty conditions, and determine the best AI-based machine learning algorithm for cost reduction after fault detection. The results indicate that the hybrid SVM-HMM model can effectively predict early-stage faults, leading to significant reductions in energy costs.
Review
Computer Science, Artificial Intelligence
Monika Sharma, Ajay Kaul
Summary: This article examines the detection methods of malware on Android mobile devices and provides an in-depth review of previous experiments using machine learning. It thoroughly analyzes the origins, evolution, and sustainability of Android malware detection, and suggests possible research paths.
Article
Computer Science, Information Systems
Vasilis Vouvoutsis, Fran Casino, Constantinos Patsakis
Summary: Malware authors constantly improve their code to evade analysis, making detection difficult. This research proposes complementing sandbox execution with binary emulation frameworks, achieving high accuracy and low computational overhead.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Yude Bai, Zhenchang Xing, Duoyuan Ma, Xiaohong Li, Zhiyong Feng
Summary: This paper conducts extensive experiments on Android malware family classification, showing that different classification methods perform similarly with neural network model slightly outperforming others. Features are the most important factor for classification, especially for enhancing API features on larger datasets. Furthermore, the model exhibits transferability across different malware datasets based on various transfer learning tasks.
Article
Computer Science, Information Systems
Muhammad Afzal, Beom Joo Park, Maqbool Hussain, Sungyoung Lee
Article
Computer Science, Interdisciplinary Applications
Brian S. Alper, Joanne Dehnbostel, Muhammad Afzal, Vignesh Subbian, Andrey Soares, Ilkka Kunnamo, Khalid Shahin, Robert C. McClure
Summary: The COVID-19 crisis has accelerated the development of infrastructure for electronic data exchange, specifically in scientific and informatics fields, to improve the identification, processing, and reporting of scientific findings. The use of new standards and tools, such as the Fast Healthcare Interoperability Resources (FHIR) and the EBMonFHIR project, is overcoming interoperability issues in evidence-based medicine. This effort aims to make scientific communication more efficient and detailed, ultimately reducing costs and improving health outcomes, quality of life, and satisfaction among healthcare professionals and patients.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Health Care Sciences & Services
Muhammad Afzal, Maqbool Hussain, Jamil Hussain, Jaehun Bang, Sungyoung Lee
Summary: This study aims to categorize COVID-19 information resources into a defined structure to facilitate resource identification, track information workflows, and guide contextual dashboard design and development. By organizing resources at primary, secondary, and tertiary levels, a conceptual framework was developed to access global initiatives with enriched metadata and track interactions between different resources. This three-level structure allows for consistent organization and management of existing and future COVID-19 knowledge resources.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Imran Ihsan, M. Abdul Qadir
Summary: In recent scientific advancements, Artificial Intelligence and Natural Language Processing play a key role in classifying documents and extracting information. This research focuses on understanding the reasons behind citations using an ontology-based approach, with an emphasis on sentiment analysis and collaborative meanings. By annotating citation texts and automatically extracting reasons, the study calculates accuracy in both publicly available and manually curated corpora.
Article
Computer Science, Interdisciplinary Applications
Humaira Waqas, Muhammad Abdul Qadir
Summary: Author name ambiguity is a significant challenge for digital libraries and scholarly data search engines, affecting the accuracy of authorship data provided. Traditional solutions are complex, feature dependent, and fail to effectively disambiguate authors with similar names but different citation numbers. A proposed multi-layer heuristics-based clustering framework addresses this issue by utilizing global and structure aware features, and incorporating contextual information for grouping similar publications. Experimental results demonstrate the framework's superior performance compared to other existing approaches, achieving an overall pF1 of 93.3% with only three features.
Article
Environmental Sciences
Asim Abbas, Muhammad Afzal, Jamil Hussain, Taqdir Ali, Hafiz Syed Muhammad Bilal, Sungyoung Lee, Seokhee Jeon
Summary: The study introduces a comprehensive rule-based system for automatic extraction of clinical concepts from unstructured clinical narrative documents with higher accuracy and transparency. The system's performance comparison showed an average F1-score of 72.94%, significantly outperforming existing baseline systems, especially in terms of problem-related concepts.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Environmental Sciences
Syed Imran Ali, Su Woong Jung, Hafiz Syed Muhammad Bilal, Sang-Ho Lee, Jamil Hussain, Muhammad Afzal, Maqbool Hussain, Taqdir Ali, Taechoong Chung, Sungyoung Lee
Summary: Clinical decision support systems (CDSSs) are the latest technological transformation in healthcare, assisting clinicians in complex decision-making. This study proposes a CDSS for clinicians managing end-stage renal disease patients, aiming to aid in dosage prescription. The evaluation shows high compliance and positive user experience.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Multidisciplinary Sciences
Raabia Mumtaz, Muhammad Abdul Qadir, Asif Saeed
Summary: Meaningful Information extraction is a crucial task, and it requires annotated datasets which are scarce. This manuscript presents a dataset, CustFRE, for extracting family relations from text, which can be used as a benchmark for evaluating and training family relation extraction systems.
Article
Computer Science, Artificial Intelligence
Raabia Mumtaz, Muhammad Abdul Qadir
Summary: This paper introduces a system CustRE for identifying and classifying family relations from English text. By using rules, regular expressions, and co-reference rules, it successfully extracts explicit and implicit family relations mentioned in the text.
KNOWLEDGE AND INFORMATION SYSTEMS
(2022)
Review
Chemistry, Multidisciplinary
Jamil Hussain, Zahra Azhar, Hafiz Farooq Ahmad, Muhammad Afzal, Mukhlis Raza, Sungyoung Lee
Summary: This study proposes a user experience quantification model to understand customer satisfaction from online reviews. The model consists of three steps: selecting relevant reviews, extracting user experience dimensions, and mapping them to a customer satisfaction model. The results show that the proposed method performs well in terms of accuracy and topic coherence.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Asif Muhammad, Muhammad Abdul Qadir
Summary: The study presented a job scheduler MF-Storm based on max-flow min-cut algorithm to achieve near-optimal schedule for maximizing throughput. The scheduler considers the processing and communication demands, available computational and communicational resources in a heterogeneous cluster to dynamically schedule streaming applications with minimized scheduling cost.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Information Systems
Musarat Hussain, Chi Cheng, Rui Xu, Muhammad Afzal
Summary: Phishing scams are on the rise and require rapid, precise, and low-cost prevention measures. CNN-Fusion, a character-level convolutional neural network, is proposed as an effective and lightweight method for detecting phishing URLs. It utilizes parallel one-layer CNN variants with different-sized kernels and applies techniques like SpatialDropout1D and max-over time pooling to enhance its robustness and feature selection. Evaluation on publicly available datasets and against AI adversarial attacks shows superior performance compared to existing methods with significantly reduced training time and memory consumption, achieving an average accuracy above 99%.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Cynthia Lokker, Elham Bagheri, Wael Abdelkader, Rick Parrish, Muhammad Afzal, Tamara Navarro, Chris Cotoi, Federico Germini, Lori Linkins, R. Brian Haynes, Lingyang Chu, Alfonso Iorio
Summary: Deep learning models, especially variants of Bidirectional Encoder Representations from Transformers (BERT), can accurately identify high-quality evidence with high clinical relevance in the biomedical literature. This improves the efficiency of evidence discovery for clinical practice.
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Kashif Javed, Naveed Anwar Bhatti, Mohammad Imran
Summary: The proliferation of IoT devices has increased their usage across various applications. To address the environmental and economic concerns of replacing conventional power supplies, TPESs utilize ambient energy. However, the non-uniform availability of ambient energy leads to frequent system reboots, and excessive energy consumption due to a high number of checkpoints. This research proposes a novel sleep mode-enabled multi-optimized intermittent computing method that reduces the number of checkpoints by combining data sampling and memoization.
JOURNAL OF SYSTEMS ARCHITECTURE
(2023)
Article
Computer Science, Information Systems
Hyeong Won Yu, Muhammad Afzal, Maqbool Hussain, Hyungju Kwon, Young Joo Park, June Young Choi, Kyu Eun Lee
Summary: This study aimed to identify mutations in genes that co-exist with mutated BRAF in papillary thyroid carcinoma (PTC) and analyze their frequency and clinical relevance. Results revealed that mutations in ALK, ATM, COL1A1, MSTIR, PRKCA, and WNK1 most commonly coincide with mutated BRAF in PTC.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)