Article
Chemistry, Analytical
Hui-Yin Chang, Sean M. Colby, Xiuxia Du, Javier D. Gomez, Maximilian J. Helf, Katerina Kechris, Christine R. Kirkpatrick, Shuzhao Li, Gary J. Patti, Ryan S. Renslow, Shankar Subramaniam, Mukesh Verma, Jianguo Xia, Jamey D. Young
Summary: This article introduces important considerations for metabolomics software users and developers, and provides recommendations for establishing guidelines and best practices for developing metabolomics tools, divided into three stages: preparation, tool development, and distribution and maintenance.
ANALYTICAL CHEMISTRY
(2021)
Review
Computer Science, Theory & Methods
Pascal Maniriho, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury
Summary: There has been a growing trend of malware release, which has raised concerns among security professionals worldwide. Understanding different types of malware and their detection techniques is challenging but crucial for researchers and the security community. Malware analysis, including static analysis, code analysis, dynamic analysis, memory analysis, and hybrid analysis techniques, is a crucial step towards detecting malware. Machine learning and deep learning methods have gained attention for their ability to develop sophisticated malware detection models that can handle known and unknown malicious activities. This survey provides a comprehensive study and analysis of current malware and detection techniques using the snowball approach, covering topics such as malware analysis testbeds, dynamic malware analysis, memory analysis, malware behavior analysis tools, datasets repositories, feature selection, machine learning, and deep learning techniques. The study also includes comparisons of behavior-based malware detection techniques grouped by categories of machine learning and deep learning techniques, as well as discussion on performance evaluation metrics, current research challenges, and future directions.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Vasilios Koutsokostas, Nikolaos Lykousas, Theodoros Apostolopoulos, Gabriele Orazi, Amrita Ghosal, Fran Casino, Mauro Conti, Constantinos Patsakis
Summary: This study investigates the state and characteristics of malicious Microsoft Office documents and proposes an effective classification method that outperforms current detection algorithms.
COMPUTERS & SECURITY
(2022)
Article
Computer Science, Hardware & Architecture
Sanjeev Kumar, B. Janet
Summary: This paper introduces a novel malware threat intelligence system (MTIS) to detect modern and real-world malware samples with better classification accuracy without the need for code reversing and domain expertise. By combining grayscale images and texture feature extraction methods, the proposed architecture is resilient to packed and encrypted malware samples.
Article
Computer Science, Information Systems
Mar Gimenez-Aguilar, Jose Maria de Fuentes, Lorena Gonzalez-Manzano
Summary: This paper analyzes the use of blockchains for malicious purposes and highlights the lack of research on covert communications in malware. To encourage defense-oriented research, a new mechanism called Smart-Zephyrus is developed using Solidity smart contracts. Experimental results demonstrate the ability to hide 4 Kb of secret data within 41 s. Despite the high cost (around USD 1.82 per bit), the provided stealthiness may be valuable for attackers.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY
(2023)
Article
Computer Science, Interdisciplinary Applications
S. Korkin, A. M. Sayer, A. Ibrahim, A. Lyapustin
Summary: Drawing on decades of experience in radiative transfer (RT) code development for Earth science, the paper aims to bridge the gap between theory and code development. By presenting small pieces of theory alongside corresponding RT code, following the format of Numerical Recipes, the authors simplify the process of understanding and modifying existing RT code. The focus is on simulating unpolarized monochromatic solar radiation in a uniform plane-parallel atmosphere over a reflective surface using the Gauss-Seidel iterations method.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Nayanamana Samarasinghe, Mohammad Mannan
Summary: Malicious websites often mimic top brands to host malware and launch social engineering attacks, using cloaking techniques to hide malicious content. Existing blacklists are ineffective against cloaked malicious sites, but our proposed techniques can serve as a starting point for more effective and scalable early detection of cloaked malicious sites.
COMPUTERS & SECURITY
(2021)
Article
Computer Science, Information Systems
Singam Sai Bala Subrahmanyam, P. Goutham, Vasanth Kumar Reddy Ambati, C. V. Bijitha, Hiran Nath
Summary: The number of breached IoT devices has nearly doubled from 639 million in 2020 to 1.51 billion in the first half of 2021. The limited computing capacity of IoT devices makes them vulnerable to malware attacks, highlighting the need for improved security measures. Current research on IoT malware detection mostly focuses on static analysis methods, neglecting packers and obfuscation techniques. Our proposed hybrid detection model, trained on a dataset of 3145 samples, achieves a high accuracy of 99.18% and MCC score of 0.98, even when static or dynamic analysis fail.
COMPUTERS & SECURITY
(2023)
Article
Nutrition & Dietetics
Hanqi Luo, Jiaxi Geng, Madeleine Zeiler, Emily Nieckula, Fanny Sandalinas, Anne Williams, Melissa F. Young, Parminder S. Suchdev
Summary: The BRINDA research group was formed to improve the interpretation of micronutrient biomarkers in settings with inflammation. Their inflammation adjustment methods have provided important insights and this paper serves as a practical guidebook for users to streamline their analyses using the BRINDA R package and SAS macro.
JOURNAL OF NUTRITION
(2023)
Article
Computer Science, Information Systems
Cengiz Acarturk, Melih Sirlanci, Pinar Gurkan Balikcioglu, Deniz Demirci, Nazenin Sahin, Ozge Acar Kucuk
Summary: This study proposed a methodological framework for detecting malicious code by analyzing run trace outputs using Long Short-Term Memory (LSTM). Models were developed for run traces of malicious and benign Portable Executable (PE) files, achieving high accuracy rates in detecting malware.
Article
Computer Science, Hardware & Architecture
Roopak Surendran, Tony Thomas, Sabu Emmanuel
Summary: This article proposes a malware detection mechanism based on the presence of malicious system call codes in the system call sequence of an application, achieving consistent accuracy and precision in various datasets.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Artificial Intelligence
Recep Sinan Arslan
Summary: This study proposed a model using AndroAnalyzer for static analysis and deep learning, achieving an accuracy of 98.16% with recall, precision, and F-measure values of 98.78, 99.24, and 98.90 respectively, outperforming traditional machine learning techniques.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Antonella Santone
Summary: The paper proposes a method for Android malware detection and family identification using a deep learning model trained on application image representations, improving detection accuracy. Experimental results show an average accuracy ranging from 0.96 to 0.97, with interpretability provided about the model's predictions.
COMPUTERS & SECURITY
(2021)
Review
Chemistry, Multidisciplinary
Nick A. Shepelin, Zahra P. Tehrani, Natacha Ohannessian, Christof W. Schneider, Daniele Pergolesi, Thomas Lippert
Summary: Nanoscale thin films have a wide range of applications in various fields, and their functional tunability based on chemical composition has driven human progress. Pulsed laser deposition is an important method for fabricating thin films, using laser energy to form a plasma and deposit material onto a substrate. This technique allows for the production of crystalline films with a wide range of atmospheric conditions and chemical complexity. However, achieving high quality films with desired composition requires rigorous optimization of growth parameters. This tutorial review provides an overview of pulsed laser deposition, discusses the effects of growth parameters on film properties, and explores in situ monitoring techniques.
CHEMICAL SOCIETY REVIEWS
(2023)
Article
Biochemistry & Molecular Biology
Dmitrii Smirnov, Pavel Mazin, Maria Osetrova, Elena Stekolshchikova, Ekaterina Khrameeva
Summary: Lipidomics is a rapidly growing discipline that involves the identification and quantification of thousands of lipids. This paper provides guidelines for analyzing lipidome data obtained using untargeted LC-MS methods, focusing on practical approaches for data analysis. The paper also outlines potential applications of untargeted lipidomics for biological studies and includes a detailed R notebook for data analysis based on xcms software.
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)
