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DCEL:classifier fusion model for Android malware detection

Journal of Systems Engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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Provably Secure Symmetric-Key Privacy-Preserving authenticated key agreement protocol 13

Provably Secure Symmetric-Key Privacy-Preserving authenticat...

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13th International Conference on Communication and Network Security, ICCNS 2023

作者： Ni, Liang Zhou, Hengsheng Zhang, Yawei Liu, Xiaoyan Gu, Bingke School of Computer Science Henan Development and Innovation Laboratory of Industrial Internet Security Big Data Zhongyuan University of Technology Henan Zhengzhou China School of Computer Science Fujian Normal University Fujian China School of Computer Science Zhongyuan University of Technology Henan Zhengzhou China

ISBN: (纸本)9798400707964

In recent years, the rapid development of Quantum Computing technology has had a huge impact on the security of traditional Public Key Cryptography. However, the security of Hash functions and Symmetric Cryptographic algorithms is relatively less impacted by this security threaten and commonly have higher execution efficiency. This two primitives can offset the security impact by appropriately increasing the output scale of a Hash function and symmetric key respectively. Therefore, the Symmetric Key Authenticated Key Agreement(SAKA) protocol which constructed mainly by Hash functions and symmetric cryptographic algorithms has gradually attracted more attention. In this paper we proposed a provably secure SAKA scheme. We extend the security model used by Ferreira et al to formally prove the security of our protocol by introducing a criterion for indistinguishability of identities and Synchronization Robustness property. Our protocol not only captures basic security properties such as Perfect Forward Secrecy, Entity Authentication and Key Indistinguishability but also achieved Privacy Preservation and Full Synchronization Robustness. Furthermore, its implementation does not need to rely on a huge and complex Public key Infrastructure(PKI), so we can easily deploy it in a coucurrent environment. Comparison results indicate that the proposal consumes the least computation resource and reasonable communication cost among nine SAKA protocols while equiping 10 security properties. © 2023 ACM.

关键词： Hash functions

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DynamiSE: Dynamic Signed Network Embedding for Link Prediction 10

DynamiSE: Dynamic Signed Network Embedding for Link Predicti...

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10th IEEE International Conference on data science and Advanced Analytics, DSAA 2023

作者： Sun, Haiting Tian, Peng Xiong, Yun Zhang, Yao Xiang, Yali Jia, Xing Wang, Haofen Fudan University School of Computer Science Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Tongji University Shanghai China

ISBN: (纸本)9798350345032

In real-world scenarios, dynamic signed networks are ubiquitous where edges have positive and negative sign semantics and evolve over time. Encoding the dynamics and sign semantics of the network simultaneously is challenging. Moreover, over-smoothing is inevitably introduced by the learning of network dynamics. Targeting this gap, we propose Dynamic Signed Network Embedding (DynamiSE), which effectively integrates the balance theory and ordinary differential equation (ODE) into node representation learning to construct a deeper dynamic signed graph neural network and capture the complex sign semantics formed by the two types of edges. © 2023 IEEE.

关键词： Semantics

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Multi-Label Feature Selection Based on Improved Fisher Score with Label Correlation 13

Multi-Label Feature Selection Based on Improved Fisher Score...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Yu Cheng, Yuxuan Shi, Chunyu Lin, Yaojin School of Computer Science Minnan Normal University Zhangzhou China Minnan Normal University Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350319156

Feature selection for multi-label classification has received extensive attention in the fields of machine learning and data mining. However, some feature selection methods fail to incorporate label correlations, resulting in the degradation of learning performance. In this paper, we dedicate to fuse the Fisher score model and neighborhood mutual information model for multi-label learning. The neighborhood mutual information is initially constructed to identify the coefficients between labels, by measuring the granularity of each instance. After that, an improved Fisher score model based on label coefficients is formed to select features with multiple crucial labels. Extensive experiments demonstrate the effectiveness and stability of the proposed method. © 2023 IEEE.

关键词： Feature Selection

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings 38

FasMe: Fast and Sample-efficient Meta Estimator for Precisio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Xiao Wang, Yiqin Shen, Yangyang Shen, Dian Wang, Meng Duan, Peibo Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing China College of Design and Innovation Tongji University Shanghai China Department of Data Science & AI Monash University Melbourne Australia

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leading to unreliable estimates. Previous approaches either fail to perform well in small sample settings or suffer from inefficient estimation processes, even when incorporating meta-learning techniques. To this end, we propose a novel approach FasMe for Fast and Sample-efficient Meta Precision Matrix Learning, which first extracts meta-knowledge through a multi-task learning diagram. Then, meta-knowledge constraints are applied using a maximum determinant matrix completion algorithm for the novel task. As a result, we reduce the sample size requirements to O(log p/K) per meta-training task and O(log |G|) for the meta-testing task. Moreover, the hereby proposed model only needs O(p log ϵ−1) time and O(p) memory for converging to an ϵ-accurate solution. On multiple synthetic and biomedical datasets, FasMe is at least ten times faster than the four baselines while promoting prediction accuracy in small sample settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Leveraging Support Vector Machines for Detection of Network Traffic Attacks 4

Leveraging Support Vector Machines for Detection of Network ...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

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DepBoost-TransNet: Boosted Transformer Network for Depression Classification 2nd

DepBoost-TransNet: Boosted Transformer Network for Depressi...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Anil Rahood, Pratik Kumaresan, Prasanna Kumar Chakravarthi, Bharathi Raja School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9783031584947

Depression presents a significant global mental health challenge affecting countless individuals across the globe. Early detection is of paramount importance, yet conventional methods, such as self-reporting and professional evaluations, possess inherent limitations and subjectivity. With the proliferation of social media and the abundance of user-generated content, novel approaches employing transformer-based models and text-mining techniques hold the potential to reveal signs of depression within social media text. This study delves into the efficacy of transformer-based models and natural language processing techniques in recognizing depressive symptoms within social media text. Social media serves as a distinct window into mental well-being, and this investigation explores the utilization of advanced transformers to tackle this issue. Results indicate that transformers trained on imbalanced datasets outperform those employing data augmentation techniques, like SMOTE, due to their proficiency in discerning intricate patterns and sensitivity to synthetic data. Furthermore, the ensemble model, which amalgamates predictions from multiple transformer models, surpasses individual models, underscoring the potential of harnessing the diverse strengths of these models for enhanced depression classification. Despite certain limitations and privacy considerations, leveraging social media data for early depression detection shows promise in improving the well-being of affected individuals through timely intervention and treatment. Subsequent research endeavors should prioritize hyperparameter optimization and the acquisition of more balanced datasets to further enhance the accuracy and dependability of depression diagnosis via the analysis of social media text. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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To Train, or Not to Train: Exploring Foundation Models for TBAD Segmentation 26

To Train, or Not to Train: Exploring Foundation Models for T...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Abaid, Ayman Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9781837242672

Recent advancements in biomedical image analysis have been significantly influenced by vision foundation models (FMs) originally designed for general computer vision tasks. These models, such as the Segment Anything Model (SAM) have quickly transitioned into applications in medical image processing. This study evaluates three cutting-edge segmentation FMs using Type B Aortic Dissection (TBAD) CTA images. Our experiments critically assess their strengths and weaknesses compared to traditional methods, emphasizing their potential to enhance efficiency in medical image analysis. However, our findings also indicate that these FMs have not surpassed traditional segmentation models in terms of performance, suggesting limitations in their adaptability for medical image segmentation tasks straight out of the box. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Dissection

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