Article
Computer Science, Information Systems
Qingling Xu, Dawei Zhao, Shumian Yang, Lijuan Xu, Xin Li
Summary: This paper proposes an approach for Android malware detection based on Graph Convolutional Networks (GCNs), which focuses on learning the behavior-level features of Android applications. The experimental results demonstrate that this method has high precision and accuracy in detecting malicious code.
Article
Computer Science, Information Systems
Chenkai Guo, Dengrong Huang, Naipeng Dong, Jianwen Zhang, Jing Xu
Summary: Although many embedding approaches have been proposed for code representation of mobile applications, insufficient attention has been paid to the event-driven nature of their running. This paper introduces a callback-based hierarchical embedding method Callback2Vec, which captures the running behavior of callbacks through a fine-grained callback sequence generation algorithm.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Minghui Cai, Yuan Jiang, Cuiying Gao, Heng Li, Wei Yuan
Summary: This paper aims to learn behavior level features of Android apps from function calls, using enhanced function call graphs (E-FCGs) and a Graph Convolutional Network (GCN) based algorithm. Experimental results show that the method outperforms traditional static features in malware detection.
Article
Computer Science, Information Systems
Yang Yang, Xuehui Du, Zhi Yang, Xing Liu
Summary: The openness of the Android operating system brings convenience to users but also poses a threat of attack from malicious applications, making malware detection a key research focus in mobile security. The DGCNDroid method proposed in this paper effectively addresses the issues of feature selection and feature loss in graph structures in current malware detection methods, achieving higher detection accuracy through experimentation on a dataset of 11,120 Android apps.
Article
Computer Science, Information Systems
Han Gao, Shaoyin Cheng, Weiming Zhang
Summary: The paper introduces a novel approach for Android malware detection and familial classification based on Graph Convolutional Network (GCN). Through experiments, GDroid system shows promising results in detecting Android malware and classifying malware families, outperforming existing methods.
COMPUTERS & SECURITY
(2021)
Article
Computer Science, Artificial Intelligence
Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Xinxing Liu, Pengfei Gao
Summary: The article introduces a system for analyzing the familial of Android malware, named GSFDroid. This system utilizes graph features and Graph Convolutional Networks to embed features, improving the efficiency of downstream analytics tasks. By using a simple graph feature normalization method to standardize embedded APK features, the system effectively clusters new malware samples from unknown families.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Pengbin Feng, Li Yang, Di Lu, Ning Xi, Jianfeng Ma
Summary: This paper presents a novel behavior-based Java malware detection method called BejaGNN, which captures malware semantic information using graph neural network. Experimental results demonstrate that BejaGNN achieves high F1 98.8% and outperforms existing Java malware detection approaches, validating the promise of graph neural network in Java malware detection.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Thanh Toan Nguyen, Thanh Trung Huynh, Matthias Weidlich, Quan Thanh Tho, Hongzhi Yin, Karl Aberer, Quoc Viet Hung Nguyen
Summary: Proposed SCAMA, an algorithm that efficiently mines maximal subgraphs by adopting a divide-and-conquer strategy to address the limitations of traditional approaches. By partitioning the graph database into equivalence classes and extracting maximal backbones using a graph convolutional network, SCAMA constructs maximal frequent subgraphs and facilitates graph classification.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Da Huang, Fangyuan Lei
Summary: In this paper, a temporal group-aware graph diffusion network (TGGDN) is proposed to address the issues of mutual interactions being overlooked and the lack of interpretability in dynamic network link prediction. Experimental results demonstrate the superiority of TGGDN in the task.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Chemistry, Analytical
Haojie Wu, Nurbol Luktarhan, Gaoqi Tian, Yangyang Song
Summary: The smartphone has become a vital tool in our daily lives, with the Android operating system being widely used. However, Android smartphones are often targeted by malware. To tackle this issue, researchers have proposed various malware detection approaches, including the use of function call graphs (FCGs). Our work aims to enhance node feature differences in an FCG for Android malware detection. We introduce an API-based node feature to analyze the behavioral properties of different functions and determine whether they are benign or malicious. Our experimental results show that our approach improves the detection accuracy compared to models using other features, suggesting the potential for further research on the use of graph structures and graph neural networks (GNNs) in malware detection.
Article
Computer Science, Artificial Intelligence
Chaobo He, Junwei Cheng, Xiang Fei, Yu Weng, Yulong Zheng, Yong Tang
Summary: Link prediction in attributed networks is challenging due to the need to effectively utilize community structure and attribute information. In this paper, we propose a novel CPAGCN method that combines AGCN and MLP to tackle this task. CPAGCN outperforms several strong competitors in link prediction, as demonstrated by extensive experiments on six real-world attributed networks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Saeed Seraj, Siavash Khodambashi, Michalis Pavlidis, Nikolaos Polatidis
Summary: Android platforms are commonly targeted by attackers, threatening user privacy. Many Android anti-malware applications are fake, necessitating the need for detection. This article presents a dataset and a customized multilayer perceptron neural network for detecting anti-malware.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-Wang Wu, Yan Wang, Yong Fang, Peng Jia
Summary: The surge of malware poses a huge threat to cyberspace security. Existing malware analysis methods rely on feature engineering, increasing the complexity of analysis. This research proposes a new method based on function call graph and graph embedding network, which automatically extracts semantic features for efficient malware analysis.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chen Liu, Bo Li, Jun Zhao, Ziyang Zhen, Weiwei Feng, Xudong Liu
Summary: This paper proposes a temporal interaction-enhanced malware variants detection framework called TI-MVD, which utilizes temporal and structural embedding features to detect malware variants. It introduces a novel end-to-end interaction-enhanced embedding approach to learn the structural embedding and a strong-correlated clique method to handle temporal interactions in parallel, reducing the time cost of temporal embedding. Experimental results on four real-world datasets show that TI-MVD outperforms state-of-the-art methods significantly.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shaojie Chen, Bo Lang, Hongyu Liu, Yikai Chen, Yucai Song
Summary: This paper proposes a novel Android malware detection method that integrates multiple features, achieving higher detection accuracy than existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Health Care Sciences & Services
Yongtao Wang, Shengwei Tian, Long Yu, Weidong Wu, Dezhi Zhang, Junwen Wang, Junlong Cheng
Summary: To improve the feature expression ability and segmentation performance of U-Net, we proposed a feature supplement and optimization U-Net (FSOU-Net). The proposed method utilizes shallow feature supplement module and deep feature optimization module to enhance the representation ability of features. Experimental results demonstrate the superiority of the proposed model in medical image segmentation.
TECHNOLOGY AND HEALTH CARE
(2023)
Correction
Computer Science, Information Systems
Hongfeng You, Long Yu, Shengwei Tian, Xiang Ma, Yan Xing
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Correction
Computer Science, Information Systems
Xiangyu Wei, Long Yu, Shengwei Tian, Pengcheng Feng, Xin Ning
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
Bin Yu, Long Yu, Shengwei Tian, Weidong Wu, Zhang Dezhi, Xiaojing Kang
Summary: This research proposes a new multi-scale channel attention module (MS-CA), which is applied to an image segmentation model for accurate diagnosis and treatment planning of skin lesions. Experimental results show that the MS-CA model achieves better segmentation results compared to existing methods.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
(2023)
Article
Engineering, Biomedical
Zhen Peng, Shengwei Tian, Long Yu, Dezhi Zhang, Weidong Wu, Shaofeng Zhou
Summary: Semi-supervised learning is significant in medical imaging tasks, but pseudo-labeling-based methods face two problems in medical image datasets: bias towards the majority class and loss of useful information. To address these issues, we propose FullMatch, an SSL framework that utilizes all unlabeled data. Our method includes adaptive threshold pseudo-labeling (ATPL) that generates pseudo-labels based on the model's learning status and does not discard unlabeled data below the thresholds. We also introduce unreliable sample contrastive loss (USCL) to leverage useful information from low-confidence unlabeled data. Experimental results demonstrate the superiority of our method over state-of-the-art SSL methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Biochemical Research Methods
Jinmiao Song, Shengwei Tian, Long Yu, Qimeng Yang, Yuanxu Wang, Qiguo Dai, Xiaodong Duan
Summary: Studies have shown that IncRNA-miRNA interactions have important effects on gene expression and biological activities. In this research, a new prediction model called ISLMI was proposed, which used information injection and a second order graph convolution network (SOGCN) to enhance the performance of predicting lncRNA-miRNA interactions. The model achieved reliable performance in 5-fold cross-validation and significantly improved the prediction accuracy.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Zhi Wang, Feng Gao, Long Yu, Shengwei Tian
Summary: Accurate segmentation of polyps from colonoscopy images is crucial for early screening and diagnosis of colorectal cancer. This study proposes a novel network that combines uncertain area attention, cross-image context extraction, and adaptive fusion to improve polyp segmentation. The proposed method achieves state-of-the-art performance on multiple public datasets.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Zhen Peng, Dezhi Zhang, Shengwei Tian, Weidong Wu, Long Yu, Shaofeng Zhou, Shanhang Huang
Summary: In this study, a new algorithm called Multi-Curriculum Pseudo-Labeling (MCPL) is proposed to address the issue of data imbalance in medical image tasks. By evaluating the learning status of the model for each class and automatically adjusting the thresholds, adaptive pseudo-label generation for each class is achieved. Experimental results demonstrate that our method outperforms fully supervised baseline and other existing methods in medical image classification tasks.
Article
Computer Science, Information Systems
Xiaoheng Deng, Jincai Zhu, Xinjun Pei, Lan Zhang, Zhen Ling, Kaiping Xue
Summary: This paper proposes a Flow Topology based Graph Convolutional Network (FT-GCN) approach for label-limited IoT network intrusion detection. By leveraging flow traffic patterns and flow topological structure, FT-GCN is deployed at edge servers in IoT networks to detect intrusions. It constructs an interval-constrained traffic graph (ICTG) considering the time correlation of traffic flows, and enhances key statistical features of traffic flows using a Node-Level Spatial (NLS) attention mechanism. Intrusion identification in IoT networks is achieved by learning the combined representation of statistical flow features and flow topological structure with the cost-effective Topology Adaptive Graph Convolutional Networks (TAGCN).
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Engineering, Biomedical
Shaofeng Zhou, Shenwei Tian, Long Yu, Weidong Wu, Dezhi Zhang, Zhen Peng, Zhicheng Zhou, Junwen Wang
Summary: Recent research in semi-supervised learning focuses on consistency regularization using data augmentation, while the more general method of pseudolabelling is limited by noisy training. Medical datasets have a long-tail distribution, and combining these limitations, we propose FixMatch-LS and its variant FixMatch-LS-v2 for medical image classification. We introduce label smoothing to adjust the pseudolabel threshold and reduce the influence of noisy pseudolabels, and emphasize the importance of consistency for pseudolabelling to improve pseudolabel quality. The framework is validated on skin lesion diagnoses from the ISIC 2018 and ISIC 2019 challenges, achieving AUCs of 91.63%, 93.70%, 94.46%, and 95.44% on different proportions of labelled data from ISIC 2018.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
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)
