Article
Computer Science, Information Systems
Sajad Hamzenejadi, Mahdieh Ghazvini, Seyedamiryousef Hosseini
Summary: With the increasing number of people using mobile devices, mobile devices have become prime targets for cybercriminals. This paper provides a detailed background on mobile botnets and discusses various techniques for detecting them.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2023)
Article
Computer Science, Hardware & Architecture
Tong Anh Tuan, Nguyen Viet Anh, Tran Thi Luong, Hoang Viet Long
Summary: This study introduces a new dataset on DGA botnets named UTL_DGA22, which addresses the detection and classification problems in cybersecurity. The dataset includes only domain records and proposed a valuable set of attributes for classification algorithms, leading to good results in experiments. The UTL_DGA22 dataset serves as a database for researchers to develop algorithms and evaluate solutions objectively.
Article
Computer Science, Information Systems
Wadi' Hijawi, Ja'far Alqatawna, Ala' M. Al-Zoubi, Mohammad A. Hassonah, Hossam Faris
Summary: This study investigates Android botnets using static analysis to extract features from the applications' source code. Machine learning models are developed to detect malicious applications, with a focus on a set of features related to accessing resources on the target mobile. The Random Forest classifier performs the best in detecting Android botnets based on all sets of features.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Geography, Physical
Shubhangi Chaturvedi, Pritee Khanna, Aparajita Ojha
Summary: Early stage smoke detection using image and video analysis in the outdoor environment is crucial due to its wide applications in fire prevention and environmental safety. Various techniques, including conventional image processing, machine learning, and deep learning, have been proposed for real-time smoke detection. Key characteristics such as smoke pattern, motion analysis, color, and texture are utilized to identify smoke in the outdoor environment.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Cinare Oguz, Mete Yaganoglu
Summary: This study aims to reduce the duration and amount of COVID-19 transmission by shortening the diagnosis time of patients using Computed Tomography (CT). Deep learning models and classification methods were employed to develop a decision support system for radiologists. By extracting deep features and evaluating their performance, the study found that the combination of ResNet-50 and SVM achieved the best accuracy, F1-score, and AUC value. The high performance of this system suggests its potential as an auxiliary tool for diagnosing COVID-19.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Review
Cell Biology
Kogilavani Shanmugavadivel, V. E. Sathishkumar, Jaehyuk Cho, Malliga Subramanian
Summary: This article reviews methods and techniques for early detection of Alzheimer's Disease and provides a comprehensive analysis of AD diagnosis datasets. The research findings are important for improving the accuracy of Alzheimer's Disease detection.
AGEING RESEARCH REVIEWS
(2023)
Article
Computer Science, Information Systems
Mutasem K. Alsmadi, Ibrahim Almarashdeh
Summary: Fish classification is a widely studied problem in the fields of image segmentation, pattern recognition, and information retrieval. This study compares and evaluates various preprocessing methods, feature extraction techniques, and classifiers, and reviews the use of relevant databases. By collecting recent research works, it provides guidance for future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
I. Michael Revina, W. R. Sam Emmanuel
Summary: Facial expression recognition is a powerful tool for social communication, involving preprocessing, feature extraction, and classification stages, with performance of different FER techniques compared based on the number of expressions recognized and algorithm complexity.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Muhammad Attique Khan, Mamta Mittal, Lalit Mohan Goyal, Sudipta Roy
Summary: This paper discusses the importance of human detection and activity recognition in various fields and the application of statistical, supervised learning, and deep learning methods. It summarizes the different steps and technical difficulties in human classification systems, emphasizing the use of convolutional neural networks. The review aims to identify research topics, challenges, and solutions in this dynamic and challenging field.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Review
Agriculture, Multidisciplinary
A. S. M. Mahmudul Hasan, Ferdous Sohel, Dean Diepeveen, Hamid Laga, Michael G. K. Jones
Summary: The rapid development of deep learning techniques has enabled efficient detection and classification of objects from images or videos, with applications in agriculture especially for weed management. Automated weed detection plays a key role in improving crop yields and fine-tuning pre-trained models on plant datasets has proven to achieve high accuracy.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Software Engineering
Amit Kumar Mondal, Kevin A. Schneider, Banani Roy, Chanchal K. Roy
Summary: Software architecture is the foundation of a system, and its documentation and review are crucial for decision making, implementation, and future maintenance. The focus on architectural consistency and change detection has become increasingly important in modern software development. However, there are challenges in developing lightweight and automated techniques for processing large amounts of change revisions, as well as the need for capturing design decision associativity and reliable post analysis of architectural change.
JOURNAL OF SYSTEMS AND SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Soodeh Hosseini, Ali Emamali Nezhad, Hossein Seilani
Summary: Botnet is a network and internet risk that requires detection through analysis and monitoring. Traditional approaches are not effective due to the growth of Internet and network environments. A new detection model is proposed for evaluating different botnet attacks and accuracy.
Review
Biology
Mehwish Zafar, Muhammad Imran Sharif, Muhammad Irfan Sharif, Seifedine Kadry, Syed Ahmad Chan Bukhari, Hafiz Tayyab Rauf
Summary: The skin is the largest organ in the human body and skin cancer is one of the most dangerous types of cancer. Abnormal cell growth in human skin cells can be caused by various pathological variations and genetic disorders. Early diagnosis is crucial due to the slow development and high mortality rate of skin cancer. Numerous computer-aided diagnosis systems utilizing deep learning, machine learning, and computer vision approaches have been proposed for the recognition of skin cancer. This research provides a comprehensive review of the methodologies, techniques, and approaches used for examining skin lesions, with a focus on the challenges and obstacles in analyzing complex and rare features.
Article
Computer Science, Theory & Methods
Ahmed Alharbi, Hai Dong, Xun Yi, Zahir Tari, Ibrahim Khalil
Summary: Social media has rapidly grown and become an essential part of many people's lives, but it has also become a popular source for identity deception. This article analyzes various identity deception attacks and provides a detailed review of detection techniques, while also identifying the challenges and issues in existing technologies.
ACM COMPUTING SURVEYS
(2022)
Review
Computer Science, Information Systems
Rajasekhar Chaganti, Bharat Bhushan, Vinayakumar Ravi
Summary: With the advent of technologies like IoT and SDN, the DDoS attack vector has expanded, posing new threats to targeted victims. Blockchain technology, with its decentralized design and secure distributed storage, can enhance security in DDoS mitigation. This paper reviews and categorizes state-of-the-art DDoS mitigation solutions based on blockchain technology, considering deployment location and architectures like IoT and SDN.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Ying Xing, Hui Shu, Fei Kang, Hao Zhao
Summary: Botnet has become a serious threat to the internet ecosystem, and detecting and tracking these botnets is crucial. This article proposes a botnet detection framework, Peertrap, based on self-avoiding random walks (SAW) community detection. The framework can accurately detect P2P bots even with incomplete topological information.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Yeming Gu, Hui Shu, Pan Yang, Rongkuan Ma
Summary: This paper presents MinSIB, a toolkit for reducing static binary instrumentation overhead, which better focuses on functional areas and security-related basic blocks, and improves the efficiency of fuzzing.
COMPUTERS & SECURITY
(2022)
Article
Computer Science, Information Systems
Yang Li, Fei Kang, Hui Shu, Xiaobing Xiong, Zihan Sha, Zhonghang Sui
Summary: With the rapid development of reverse engineering technology, software security issues have become an urgent financial loss problem. The current development of code obfuscation mainly focuses on increasing the complexity of code structure, while neglecting the protection of program semantic information. This paper proposes a software protection method based on program semantic information called COOPS, which reconstructs the semantic relationship within the program by establishing a switch relationship between intrafunction control flow and interfunction calling. The evaluation results demonstrate that COOPS exhibits strong resistance to program similarity analysis techniques and significantly improves the level of software protection.
SECURITY AND COMMUNICATION NETWORKS
(2022)
Article
Computer Science, Information Systems
Yeming Gu, Hui Shu, Rongkuan Ma, Lin Yan
Summary: The security research on Windows has been neglected in the academic field, where most new methods are designed for Linux and cannot be easily applied to Windows. This paper introduces SpotInstr, a lightweight static instrumentation tool for Windows binaries, which can quickly and effectively instrument most Windows PE programs. It also proposes a selective instrumentation method based on a set of filters, and presents SpotFuzzer, a system utilizing SpotInstr for fuzzing Windows binaries, demonstrating their superior performance and stability.
SECURITY AND COMMUNICATION NETWORKS
(2022)
Article
Computer Science, Hardware & Architecture
Zihan Sha, Hui Shu, Xiaobing Xiong, Fei Kang
Summary: This paper presents an obfuscation strategy called execution trace obfuscation, which switches program execution trace between multiple threads to realize equivalent code transformation. Further cascade encryption of a function and key removal interferes with advanced reverse analysis tools effectively.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)