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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Pandey, Pragati Shrivastava, Ankit Chandra Singh, Ajay Shanker Sharma, Nisha Galgotias University School of Computing Science & Engineering India

ISBN: (纸本)9798350356816

The rapid proliferation of unwired techniques and mobile instruments had profound effect on daily basis. To leverage the potential of these technologies in the hospitality industry, there have been early attempts to merge them. This research project focuses on automating the pizza ordering process in restaurants with the goal of enhancing the overall eating occurrence for consumers. This paper delves into model and execution of an Automated Pizza Ordering System for restaurant *** system leverages unwired data connectivity for communication with servers. A dedicated Android application on the user's mobile device provides access to the eating place menu. Consumers can place their orders with their mobile devices, and these orders detail are transmitted wirelessly to a central database, which then routed to the cooking area and cash handler. This technology enables eating place owners to easily manage menu updates and *** use of wireless applications on mobile devices offers convenience, streamlines operations, reduces the potential for human mistakes, and provides on-time customer feedback. This system effectively addresses limitations of previous PDA-based pizza ordering system and proves to be a cost-effective and efficient alternative to traditional multi-touch eating place management systems. © 2024 IEEE.

关键词： Information management

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of Computer science & Technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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

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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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A new perspective exploration of machine learning algorithms for defending Side-Channel attacks

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Multimedia Tools and Applications 2024年 1-20页

作者： Ramakrishnan, Vedhavathy Thoguluva Seethapathy, Murugaanandam Sundar, Ramesh Dhandapani, Saveetha Munusamy, Sundarrajan Balasubrahmaniam, Lakshmi Dhevi Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Machine learning algorithms are used in various real-time applications, where security is one of the major problems. Security is applied in various aspects of the application in cloud computing. One of the security issues is a side-channel attack, which is not a common problem that can be solved by a point solution. The emerging computing industries need a better solution for side-channel attack detection since the amount of data used in communication and sharing on the Internet is enormous. Several old research works have concluded that machine learning algorithms are highly suitable for data analytics. Hence, this paper has aimed to propose a better machine-learning model for side-channel attack detection. This paper explains some machine learning algorithms from a technical point of view. The explanation identifies that the Convolution Neural Network algorithm is selected as a better algorithm for side-channel attack detection. The proposed CNN model has experimented with Python software to verify the results. Since CNN is more efficient in data analytics, the analysis result produces more accurate predictions than earlier algorithms. The performance of the proposed CNN is compared with the earlier algorithms for evaluating it. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data Analytics

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Research on traffic sign detection method based on FLB-YOLOv8 5

Research on traffic sign detection method based on FLB-YOLOv...

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5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Cai, Yichong Min, Rui Huang, Jinyu School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (纸本)9781510681842

In this paper, the proposed FLB-YOLOv8 model address issues in current traffic sign recognition, such as leakage, false detection, low accuracy, and excessive model parameters. Firstly, a small target detection layer is added to improve semantic information and feature expression for small targets. Secondly, the FasterNet Block structure replaces the Bottleneck structure in the C2f module of YOLOv8, reducing parameters and computation while maintaining accuracy. The introduction of the LSK module adjusts the spatial receptive field dynamically, improving feature extraction capability. Lastly, a weighted bidirectional feature pyramid network enables efficient bidirectional cross-scale connection and feature fusion, balancing feature information of different scales. Experimental results on the CCTSDB dataset show that the improved model achieves 78.3% mean average precision (mAP), a 4.7 percentage point improvement over the original model. It also reduces model parameters by 56% and computation by 26%. This algorithm effectively improves detection accuracy, reduces model complexity, and achieves better performance in traffic sign detection. © 2024 SPIE.

关键词： Traffic signs

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Method and Practice of Trusted Embedded computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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Hierarchical detection of unsafe conditions in banks based on YOLOv8 5

Hierarchical detection of unsafe conditions in banks based o...

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5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Jiang, Shuping Li, Yuansong School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (纸本)9781510681842

This study proposes an improved YOLOv8 algorithm based on the network model framework in response to issues in grading accuracy, slow speed, high false alarm rate, and the excessive workload of monitoring staff encountered in the tiered monitoring of unsafe banking conditions. These challenges lead to less effective monitoring results. The study utilized a dataset that includes PASCAL VOC2007 and a section of a self-built dataset, collectively comprising of 8,511 images. Initially, a Partial Convolutions module was introduced to the backbone section, enhancing the ability to recognize occluded objects in images and improving the resistance to interference. Following that, an enhancement was made to the Bottleneck structure, integrating Large Kernel Attention (LSKA) and enhancing its detection capabilities for small targets. For large target detection, Multi-Scale Dilated attention was employed to enhance the model's processing efficiency and detection accuracy. Lastly, an ASFF head was incorporated. Overall, the improvements resulted in a 3.0% increase in mAP@0.5, boosting detection accuracy and speeding up the detection process. © 2024 SPIE.

关键词： Image enhancement

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Lightweight YOLOv8 for grape leaf lesion detection 3

Lightweight YOLOv8 for grape leaf lesion detection

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2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Ren, Yuhao Huang, Jinyu School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In the field of smart agriculture, the rapid and accurate detection of grape leaf diseases is crucial, especially for early-stage small lesions. To enhance the efficiency of detecting grape leaf diseases in resource-limited environments, this study presents an optimized lightweight model based on the YOLOv8n framework, named YOLO-BMCA. By integrating the upgraded ADown module and incorporating CGBlock enhancements, the model not only improves the efficiency of capturing both local and global information but also significantly reduces the consumption of computational resources. The adoption of BiFPN strengthens the multi-level feature fusion, while the MLCA mechanism focuses on enhancing the capture of key features, thereby increasing accuracy. In target detection of grape leaf diseases, YOLO-BMCA achieved an accuracy rate of up to 94.7%, with improvements of 2%, 1.1%, and 3.3% in the recall rate, mAP50, and mAP50-90 respectively, compared to the original algorithm. Meanwhile, the model's parameter volume and size were also significantly reduced by 58.6% and 55%, respectively, demonstrating the strong applicability of the optimized model in resource-constrained environments. © 2024 SPIE.

关键词： Diseases and disorders Performance modeling Feature extraction Data modeling Detection and tracking algorithms Convolution Feature fusion Deep learning Agriculture Target detection

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PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home

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Journal of Reliable Intelligent Environments 2024年第2期10卷 123-136页

作者： Raeiszadeh, Mahsa Tahayori, Hooman Bahadori-jahromi, Ali Department of Computer Science and Engineering and IT Shiraz University Shiraz Iran School of Computing and Engineering University of West London London United Kingdom

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants' behavior patterns. Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories. Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Energy utilization

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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