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Delay-CJ:A novel cryptojacking covert attack method based on delayed strategy and its detection

Digital Communications and Networks 2023年第5期9卷 1169-1179页

作者： Guangquan Xu Wenyu Dong Jun Xing Wenqing Lei Jian Liu Lixiao Gong Meiqi Feng Xi Zheng Shaoying Liu Big Data School Qingdao Huanghai UniversityQingdao266427China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computing Macquarie UniversityNSW2109Australia Graduate School of Advanced Science and Engineering Hiroshima UniversityJapan

Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain *** has been common since 2017,and in fact,it once became the greatest threat to network *** better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for *** on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype *** results show that the existing detection methods used for testing may become invalid as result of this *** view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process ***,it identifies malicious mining by monitoring CPU usage and analyzing the function call *** system not only effectively detects the attack in our example but also has universal *** recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real *** found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.

关键词： Cryptojacking Evasion techniques Delayed strategy Cryptocurrency mining Behavior-based detection

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai China Ant Group Shanghai China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Fudan University School of Computer Science Shanghai China Tongji University Department of Control Science and Engineering Shanghai China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the- art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Leveraging the Effect of CNN to Identify Severity of Lumpy Skin Disease in Cattle

Leveraging the Effect of CNN to Identify Severity of Lumpy S...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Kumar, Vinesh Maurya, Jay Prakash Shrivastava, Abhishek School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science Engineering VIT Bhopal University Sehore India School of Mechanical Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

There have been research articles in the past that identified the presence of Lumpy Skin Disease (LSD) in Cattle through machine-learning methods. This paper is focused not only on confirming the presence of the virus but also on predicting the stage of the LSD virus in a Cattle skin image sample. In this paper, several machine-learning classification algorithms including Support Vector Machines, Naive Bayes, Random Forest, AdaBoost, K-nearest neighbors, and finally, Convolutional Neural Networks, were analyzed to classify the image samples of Cattle skin into 3 categories: Normal (LSD-negative), Mild LSD, and Severe LSD classes. A custom-made dataset consisting of 3454 image samples from the three classes was built to train the models. There is no cure for LSD, and the infected cattle's immune response is the only way out;however, as a supporting aid, primary anti-viral treatment can be given for a faster recovery. This should be administered to the cattle with a higher probability of getting cured and staying alive. Hence, the detection of the stage of the disease is significant in terms of the investment of time and treatment to needy cattle as healthcare facilities and government resources are limited. After analyzing the results from the models trained on the algorithms mentioned earlier, the best accuracy was obtained through Convolutional Neural Networks with pre-trained base model VGG-16 with 92.93% on the training dataset and 88.93% on the validation dataset. This model eliminates possibilities of human bias to empower the government in granting fair compensation in case of death due to LSD and to maximize resource utilization in the best possible way if integrated into a user-friendly application. © 2024 IEEE.

关键词： Convolutional neural networks

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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Computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

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Intelligent Automation & Soft computing 2023年第7期37卷 1145-1161页

作者： H.Shalma P.Selvaraj Department of Computing Technologies Faculty of Engineering and TechnologySchool of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

关键词： Depth maps occlusion detection reconstruction refined disparity map

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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

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Computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

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Feature Extraction in Image Processing using Medical Scans: Detailed Survey 2

Feature Extraction in Image Processing using Medical Scans: ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Tanu Singh, Manu Sugandh, Urvashi Panwar, Arvind UPES School of Computer Science Uttarakhand Dehardun India Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India Galgotias University School of Computing Science and Engineering Uttarakhand Greater Noida India

ISBN: (纸本)9798350368109

An important area of research in computer vision is feature extraction algorithms for image processing, which is the focal point of this paper's analysis. In the field of visual management, feature extraction and its representation serve an important role. It can focus on desirable details that may represent the image's inherent meaning. The examined image has undergone a variety of image pre-processing techniques, such as Data Cleaning, Histogram Equalization, Skull Striping, Binarization, Feature Scaling, etc. before acquiring features about the particular medical scans. After that, feature extraction techniques are applied to obtain specific features that will be useful for the analysis of image scans. Thus, the approaches utilized for shape, texture, color and appearance-based feature extraction are the main subjects of our investigation in this paper. © 2024 IEEE.

关键词： Medical image processing

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Autonomous Navigation of Humanoid Robots Using Kinect Sensor: A Vision-Based Approach for Real-Time Obstacle Detection and Path Planning 7

Autonomous Navigation of Humanoid Robots Using Kinect Sensor...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Ghosh, Mukesh Kumar Behera, Chandan Kumar Vit Bhopal University School of Computing Science Engineering and Ai Madhya Pradesh Sehore India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India

ISBN: (纸本)9798331527822

This research presents a novel perspective for an autonomously navigated humanoid robot system which unifies a Kinect Xbox 360 sensor, and L298N motor drivers with Arduino Uno for harnessing real-time depth sensing and vision-based obstacle detection. The robot can conclusively navigate, avoid obstacles and change the direction of its path if an obstacle is found. The Kinect sensor provides enumerated outer environmental data, allowing for decisive path planning and navigation. Arduino Uno processes the sensor data and sends directive commands to the motors via motor controllers through sequential communication. This paper proposes, and analyzes the navigation system's performance, and proves its advantages compared to existing models. © 2024 IEEE.

关键词： Anthropomorphic robots

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

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Digital Communications and Networks 2023年第4期9卷 827-835页

作者： Jianlong Zhang Guangzu Fang Bin Wang Xiaobo Zhou Qingqi Pei Chen Chen School of Electronic Engineering Xidian UniversityXi'an710071China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin300000China School of Telecommunications Engineering Xidian UniversityXi'an710071China

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle *** 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous ***,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection ***,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object *** first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the ***,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point ***,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as *** the experimental results,our method achieves satisfactory results in monocular 3D object detection.

关键词： Monocular 3D object detection Pseudo-LiDAR Confidence sampling Hierarchical geometric feature extraction

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