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Contrastive Learning for Robust Android Malware Familial Classification

IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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An Accurate Stock Prediction Using Ensemble Deep Learning Model

An Accurate Stock Prediction Using Ensemble Deep Learning Mo...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Baswaraj, D. Rekha, Ravula Shashi Divya, V. Natchadalingam, R. Sahni, Nancy Reddy, Pundru Chandra Shaker Computer Science and Engineering Vasavi College of Engineering Telanagana Hyderabad India Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India CSE-Data Science R.L.Jalappa Institute of Technology Karnataka Bengaluru India Mittal School of Business Management Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305708

Financial textual, statistical, and graphical data have all seen increased interest in machine learning and deep learning as analytical tools in recent years. Anticipating the trend of a stock price in the future is challenging applications of deep-learning(DL) in economics. Several factors influence the amplitde &incidence of ascend and descend of stocks at the alike time, making it difficult to predict their future movement. In addition, these are merely company-level considerations;industry-performance, investor-mood, and economic-factors also have a role in determining the direction of equities in the future. In this research, we offer a unique deep learning method for forecasting stock price behavior in the future. The model combines two recurrent-neural-networks(RNN) with a fully connected NN using a mixing ensemble learning technique. The Standard & Poor's 500 Index serves as a case study for our investigation. Our blending ensemble-DL strategy significantly outperforms the best existing anticipation desgn on the same dataset by decreasing the mean-squared-error(MSE) by 58%, growing the precision by 41%, the recall by 52%, the F1-score by 45%, and the movement-direction-accuracy by 35%. The goal study is to detail our intend ethos and demonstrate how EDL may more correctly anticipate upcoming stock price patterns and help investors make sound financial decisions than more conventional approaches. © 2023 IEEE.

关键词： Deep-learning Ensemble-learning Statistical-finance Stock-prediction

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Multi-head Hashing with Orthogonal Decomposition for Cross-modal Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Liu, Wei Li, Jun Wu, Zhijian Xu, Jianhua Yang, Bo School of Computer and Electronic Information Nanjing Normal University Nanjing210023 China School of Data Science and Engineering East China Normal University Shanghai200062 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Recently, cross-modal hashing has become a promising line of research in cross-modal retrieval. It not only takes advantage of complementary multiple heterogeneous data modalities for improved retrieval accuracy, but also enjoys reduced memory footprint and fast query speed due to efficient binary feature embedding. With the boom of deep learning, convolutional neural network (CNN) has become the de facto method for advanced cross-model hashing algorithm. Recent research demonstrates that dominant role of CNN is challenged by increasingly effective Transformer architectures due to their advantages of long-range modeling by relaxing local inductive bias. However, the absence of inductive bias shatters the inherent geometric structure, which inevitably leads to compromised neighborhood correlation. To alleviate this problem, in this paper, we propose a novel cross-modal hashing method termed Multi-head Hashing with Orthogonal Decomposition (MHOD) for cross-modal retrieval. More specifically, with the multi-modal Transformers used as the backbones, MHOD leverages orthogonal decomposition for decoupling local cues and global features, and further captures their intrinsic correlations through our designed multi-head hash layer. In this way, the global and local representations are simultaneously embedded into the resulting binary code, leading to a comprehensive and robust representation. Extensive experiments on popular cross-modal retrieval benchmarking datasets demonstrate the proposed MHOD method achieves advantageous performance against the other state-of-the-art cross-modal hashing approaches. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Memetic Firefly Algorithm and Higher-Order Functional Link Neural Network Approach for Asian Stock Price Prediction 6th

Memetic Firefly Algorithm and Higher-Order Functional Link N...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Pemmada, Suresh Kumar Das, Saumendra Nayak, Sarat Chandra Vimal, S. GITAM School of Technology Visakhapatnam530045 India School of Management Studies GIET University Gunupur765022 India Department of Computer Science and Engineering Gitam University Hyderabad India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9789819780921

Novel Covid-19 (SARS-COV-2) has surfaced as one of the extremely harmful pandemics throughout the world over the era. The pandemic has affected many government and private industrial sectors across the globe. Prediction of stock market fluctuation and its behavior (especially in a pandemic) has been a challenging task. Like other sectors, the effect of Covid-19 has a huge effect on the stock market price across the world. The role of machine learning (ML) approaches (such as ANN) has explored many efficient solutions in this domain for the last two decades. This chapter has evolved an intelligent hybrid approach of memetic firefly algorithm and functional link neural network (FLNNN) to predict the stock market of major Asian countries. A thorough analysis has been conducted on the Covid-19 impact on the stock exchange price by considering the lockdown period and post-lockdown period of these countries. Simulated results show that the proposed framework is one of the resourceful and suitable models for predicting the future stock market among other competitive models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Financial markets

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Token2vec: A Joint Self-Supervised Pre-Training Framework Using Unpaired Speech and Text 48

Token2vec: A Joint Self-Supervised Pre-Training Framework Us...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yue, Xianghu Ao, Junyi Gao, Xiaoxue Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

Self-supervised pre-Training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-Text joint pre-Training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-Training one step further and propose token2vec, a novel joint pre-Training framework for unpaired speech and text based on discrete representations of speech. Specifically, we introduce two modality-specific tokenizers for speech and text. Based on these tokenizers, we convert speech/text sequences into discrete speech/text token sequences consisting of similar language units, thus mitigating the domain mismatch problem and length mismatch problem, which are caused by the distinct characteristics between speech and text. Finally, we feed the discrete speech and text tokens into a modality-Agnostic Transformer encoder and pre-Train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-Training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability. © 2023 IEEE.

关键词： Modeling languages

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Blockchain-Enabled Private and Secure Task Allocation Framework

Blockchain-Enabled Private and Secure Task Allocation Framew...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Ayda Valinezhad Orang Ali Dorri Serge Gaspers Sushmita Ruj School of Computer Science and Engineering UNSW Sydney and Data61 CSIRO School of Computer Science Queensland University of Technology School of Computer Science and Engineering UNSW Sydney

In the realm of wireless communications and task allocations, where agents need to travel to perform tasks, a new challenge has emerged. This challenge involves efficiently distributing tasks among agents while considering their preferences and priorities. Unfortunately, many existing solutions rely on centralized systems, making them susceptible to various vulnerabilities, including privacy concerns and security threats. To address these issues, this paper introduces a novel solution named SEPP-TAP-a secure and privacy-preserving decentralized framework for multi-agent task allocation problems, harnessing the power of blockchain technology. SEPP-TAP not only protects agents' privacy and location information but also strengthens network security by mitigating threats like the Sybil attack and network flooding. Our approach employs innovative anonymization techniques to ensure that agents' identities remain concealed throughout successive task requests, breaking any associations between tasks performed by a particular agent.

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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computer Modeling in engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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