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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Face Detection Based on Improved Multi-task Cascaded Convolutional Neural Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第2期51卷 67-74页

作者： Jia, Siyu Tian, Ying College of Computer and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China

With the development of deep learning and computer vision, face detection has achieved rapid progress owing. Face detection has several application domains, including identity authentication, security protection, media, and entertainment. Although multi-task cascaded convolutional neural networks (MTCNN) have high accuracy and robustness, the model has the disadvantages of large parameters and computational overhead in the real scene due to the complexity of the real scene and the constraints of hardware facilities. Therefore, the development of an improved network model is crucial. This paper improves the MTCNN model by reducing the number of parameters and the computational overhead and using better model parameters to locate the key points of the face. This model improves the accuracy and robustness of the face age estimation. The WiderFace and CelebA datasets are used for training. The final face detection accuracy reaches 98.7% while simultaneously reducing the number of model parameters to 70% under the same conditions. This model meets the application needs of modern society for face detection and demonstrates the efficiency and accuracy of the improved network model. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Face recognition

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Solidification microstructure of Ti-43Al alloy by twin-roll strip casting

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China Foundry 2023年第2期20卷 99-107页

作者： Yang Chen Guo-huai Liu Ye Wang Zhao-dong Wang State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China College of Material Science and Chemical Engineering Harbin University of Science and TechnologyHarbin 150080China

As a near-net-shape technology,the twin-roll strip casting(TRC)process can be considered to apply to the fabrication of TiAl alloy ***,the control of the grain distribution is very important in strip casting because the mechanical properties of strips are directly determined by the solidification microstructure.A three-dimensional(3D)cellular automation finite-element(CAFE)model based on ProCAST software was established to simulate the solidification microstructure of Ti-43Al ***,the influence of casting temperature and the maximum nucleation density(nmax)on the solidification microstructure was investigated in *** simulation results provide a good explanation and prediction for the solidification microstructure in the molten pool before leaving the kissing *** and simulated microstructure show the common texture<001>orientation in the columnar grains ***,the microstructure evolution of the Ti-43Al alloy was analyzed and the solidification phase transformation path during the TSC process was determined,i.e.,L→L+β→β→β+α→α+γ+β/B2 phase under a faster cooling rate and L→L+β→β→β+α→γ+lamellar(α_(2)+γ)+β/B2 phase under a slower cooling rate.

关键词： TiAl alloy simulation microstructure strip casting solidification

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Effects of feature selection and normalization on network intrusion detection

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Data science and Management 2025年第1期8卷 23-39页

作者： Mubarak Albarka Umar Zhanfang Chen Khaled Shuaib Yan Liu College of Information Technology United Arab Emirates UniversityAl Ain15551United Arab Emirates School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China Department of Computer Science Shantou UniversityShantouGuangdong515063China

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based *** researchers used data preprocessing techniques such as feature selection and normalization to overcome such *** most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider ***,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis ***,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,*** IDS models were implemented using the full and feature-selected copies of the datasets with and without *** models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art *** forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 *** RF models also achieved an excellent performance compared to recent *** results show that normalization and feature selection positively affect IDS ***,while feature sel

关键词： Cybersecurity Intrusion detection system Machine learning Deep learning Feature selection Normalization

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Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 160-167页

作者： Abdalrada, Ahmad Shaker Neamah, Ali Fahem Murad, Hayder Department of Software Faculty of Computer Science and Information Technology Wasit University Iraq Department of Computer Faculty of Computer Science and Information Technology Wasit University Iraq College of Medicine Wasit University Iraq

Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression by using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstrated high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of 79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The proposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions © 2024 college of Education, Al-Iraqia university. All rights reserved.

关键词： Forecasting

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A Hypernetwork Based Model for Emergency Response System

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Chinese Journal of Electronics 2022年第1期31卷 129-136页

作者： WANG Wei LIU Shufen LI Bing College of Computer Science and Technology Jilin University

Current emergency response systems are facing several challenges, including complex emergency network structure definition and inefficient emergency scheduling. For these problems, the paper analyzes the characteristics of emergency networks, and abstracts them into multiple interconnected, interdependent and interactive networks according to the characteristics of hierarchy, attribute and function, and then proposes a hypernetwork based model and its constraint conditions. Furthermore, the paper proposes an emergency scheduling method. This method fully considers the psychological factors of people and the rescue cost factors in disasters in order to balance the interests among different levels of network during rescue. The experiment results show that the model and the method proposed in this paper can not only better reveal the composition and structure of emergency response system, but also effectively balance the cost and the satisfaction in rescue.

关键词： multiple interconnected networks emergency response system interactive systems emergency management hypernetwork based model interdependent networks complex emergency network structure definition interactive networks psychological factors emergency scheduling method rescue scheduling psychology disasters

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Hyperparameter Optimization for Convolutional Neural Networks using the Salp Swarm Algorithm

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 133-144页

作者： Abdulsaed, Entesar H. Alabbas, Maytham Khudeyer, Raidah S. Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

Convolutional neural networks (CNNs) have exceptionally performed across various computer vision tasks. However, their effectiveness depends heavily on the careful selection of hyperparameters. Optimizing these hyperparameters can be challenging and time-consuming, especially when working with large datasets and complex network architectures. In response, we propose a novel approach for hyperparameter optimization in CNNs using the Salp Swarm Algorithm (SSA). Based on the natural behavior of mollusks, SSA mimics the collective intelligence that governs feeding and navigation. Taking advantage of SSA's unique properties, our research thoroughly explores the hyperparameter space. This exploration aims to identify the algorithm that maximizes CNNs performance. This paper presents the architecture of the SSA-based framework for hyperparameter optimization and compares it to other established optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). We also present experimental results using the MNIST and fashion MNIST datasets, achieving an impressive classification accuracy of 99.46% for MNIST and 94.53% for fashion-MNIST. This case study not only contributes to the fields of deep learning and hyperparameter optimization by demonstrating the effectiveness of SSA in optimizing CNNs, but it also provides benefits to researchers and practitioners who are looking for optimal hyperparameter configurations for CNNs in a variety of computer vision applications. We also evaluate the scalability and robustness of our proposed method in the context of different CNNs structures. The insights we gained highlight SSA's potential for addressing challenges related to hyperparameter optimization. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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A prediction model for improving virtual machine live migration performance in cloud computing using artificial intelligence techniques

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International Journal of computers and Applications 2024年第12期46卷 1069-1087页

作者： Alubaidan, Haya A. Aljameel, Sumayh S. Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be employed. Live migration refers to the method whereby an online virtual machine or an application is moved from one physical server to another without shutting down the client or the program. However, a migration’s qualities, which define service quality, have to be evaluated prior to a migration. In this paper, we propose a novel approach using predictive machine learning and deep learning models to forecast key metrics such as total migration time, downtime, and total data transferred for two migration types: pre-copy migration and post-copy migration. It builds on methods from regression machine learning and deep learning, and hyperparameter optimization, and feature engineering to push past reported solutions. The novelty of this work is the development of the hybrid model that includes the positive aspects from both of the machine learning and the deep learning with an emphasis on the increased accuracy of the model’s predictions. Concretely, the proposed deep learning framework based on GRU provided outstanding performance with 99.6% in total migration time where the transferred data GRU achieved 99.9% Moreover, in Down time target GRU reached 98.3%. This outperforms the other machine learning model and prior studies to become the best model for live migration prediction. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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