Article
Computer Science, Information Systems
Tiep M. Hoang, Trung Q. Duong, Hoang Duong Tuan, Sangarapillai Lambotharan, Lajos Hanzo
Summary: This article presents a framework for converting wireless signals into structured datasets for detecting active eavesdropping attacks at the physical layer using machine learning algorithms.
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, Theory & Methods
Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, Yang Liu
Summary: The current approach for Android malware detection relies on server-side scanning, yet a final defense line on mobile devices is still necessary. This paper introduces an effective real-time detection system on mobile devices, evaluating the impact of different parameters on detection performance.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Information Systems
Mulhem Ibrahim, Bayan Issa, Muhammed Basheer Jasser
Summary: Android is dominating the global smartphone market, leading to a strong need for effective security measures. This research proposes a new method for detecting and classifying Android malware using deep learning models and static analysis, achieving high accuracy in malware detection and classification.
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
Junwei Tang, Ruixuan Li, Yu Jiang, Xiwu Gu, Yuhua Li
Summary: Android malware poses a serious security threat, and obfuscation technology can generate variants that bypass existing detection methods. The proposed MGOPDroid system combines opcode feature extraction, TFIDF algorithm, and deep learning detection model for efficient anti-obfuscation Android malware detection. Experimental results show that the detection accuracy for both unobfuscated and obfuscated malware samples is over 90% with MGOPDroid.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Review
Computer Science, Information Systems
Asma Razgallah, Raphael Khoury, Sylvain Halle, Kobra Khanmohammadi
Summary: This paper investigates the main mechanisms and approaches for malware detection in Android applications, identifying the advantages and limitations of each, and suggesting avenues for future research in this area.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Hardware & Architecture
Jiayun Xu, Yingjiu Li, Robert H. Deng, Ke Xu
Summary: SDAC is a novel slow-aging solution proposed to address the model aging problem in Android malware detection, achieving significantly higher accuracy and slower aging speed compared to state-of-the-art solutions. By evaluating the contributions of new APIs and evolving based on existing API contributions, SDAC effectively adapts to changes in Android specifications without the need for retraining on new labeled datasets.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Theory & Methods
Routa Moussaileb, Nora Cuppens, Jean-Louis Lanet, Helene Le Bouder
Summary: Ransomware is a concerning threat in the 21st century, with attackers shifting towards targeted attacks. Numerous detection mechanisms have been proposed, with the article providing a systematic review of countermeasures, defining four stages of the attack and proposing a roadmap for combating ransomware.
ACM COMPUTING SURVEYS
(2021)
Review
Computer Science, Theory & Methods
Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
Summary: Malicious applications, especially those targeting Android, pose a serious threat to developers and end-users. Existing defense approaches based on manual rules or traditional machine learning may not be effective due to the rapid growth of Android malware and the advancement of evasion technologies. Deep learning (DL) techniques have shown promising performance in various domains, so applying DL to Android malware defenses has gained significant research attention. This article presents a systematic literature review that identifies 132 studies from 2014 to 2021, revealing the prevalence of DL-based Android malware detection and other defense approaches based on DL.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Alejandro Guerra-Manzanares, Hayretdin Bahsi, Sven Nomm
Summary: This study discusses the evolution of Android malware datasets, the impact of time variables, the significance of data sources, and key factors in building more effective, robust, and long-lasting Android malware detection systems.
COMPUTERS & SECURITY
(2021)
Article
Computer Science, Theory & Methods
Chuanchang Liu, Jianyun Lu, Wendi Feng, Enbo Du, Luyang Di, Zhen Song
Summary: This paper presents MOBIPCR, an efficient mobile-oriented malware detection system that integrates a cloud-based architecture, machine learning model, and detection process to protect personal data.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ismail Atacak, Kazim Kilic, Ibrahim Alper Dogru
Summary: In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed method achieves high accuracy and F-scores in the detection of malicious applications.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Abdullah Talha Kabakus
Summary: This study proposes an Android malware detection framework called DroidMalwareDetector, which utilizes CNN for comprehensive analysis and achieves a high level of accuracy according to experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Ismail Atacak
Summary: In this study, a fuzzy logic-based dynamic ensemble (FL-BDE) model was proposed to detect malware in the Android operating system. The FL-BDE model combines the power of machine learning methods and the decision-making ability of a fuzzy inference system. Experimental results showed that the FL-BDE model outperformed other machine learning-based models and demonstrated excellent performance in detecting malicious applications.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Flavia Bonomo-Braberman, Nick Brettell, Andrea Munaro, Daniel Paulusma
Summary: This article discusses the convexity and mim-width of bipartite graphs, and it proves that for certain families of graphs 7-t, the 7-t-convex graphs can be solved in polynomial time for NP-complete problems. It also explores the bounded and unbounded mim-width of 7-t-convex graphs for different sets 7-t.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Keqin Li
Summary: In this paper, we propose a computation offloading strategy to satisfy all UEs served by an MEC and develop an efficient method to find such a strategy. By using Markov chains to characterize UE mobility and calculating the joint probability distribution of UE locations, we can obtain the average response time of UEs and predict the overall average response time of tasks. Additionally, we solve the power constrained MEC speed setting problem.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Correction
Computer Science, Hardware & Architecture
Peter L. Bartlett, Philip M. Long
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Philipp Czerner, Roland Guttenberg, Martin Helfrich, Javier Esparza
Summary: This paper presents a construction method that produces population protocols with a small number of states, while achieving near-optimal expected number of interactions, for deciding Presburger predicates.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Katharina T. Huber, Leo van Iersel, Remie Janssen, Mark Jones, Vincent Moulton, Yukihiro Murakami, Charles Semple
Summary: This paper investigates the relationship between undirected and directed phylogenetic networks, and provides corresponding algorithms. The study reveals that the directed phylogenetic network is unique under specific conditions. Additionally, an algorithm for directing undirected binary networks is described, applicable to certain classes of directed phylogenetic networks.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Francesco Betti Sorbelli, Alfredo Navarra, Lorenzo Palazzetti, Cristina M. Pinotti, Giuseppe Prencipe
Summary: This study discusses the deployment of IoT sensors in an area that needs to be monitored. Drones are used to collect data from the sensors, but they have energy and storage constraints. To maximize the overall reward from the collected data and ensure compliance with energy and storage limits, an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) is proposed and solved using an Integer Linear Programming algorithm.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Carla Binucci, Emilio Di Giacomo, William J. Lenhart, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nollenburg, Antonios Symvonis
Summary: In this study, we investigate the problem of representing a graph as a storyplan, which is a model for dynamic graph visualization. We prove the NP-completeness of this problem and propose two parameterized algorithms as solutions. We also demonstrate that partial 3-trees always admit a storyplan and can be computed in linear time. Additionally, we show that even if the vertex appearance order is given, the problem of choosing how to draw the frames remains NP-complete.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Leszek Gasieniec, Tomasz Jurdzinski, Ralf Klasing, Christos Levcopoulos, Andrzej Lingas, Jie Min, Tomasz Radzik
Summary: This passage describes the Bamboo Garden Trimming Problem and presents approximation algorithms for both Discrete BGT and Continuous BGT.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)