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A Gradient-guided Fuzzing Approach to Recover More Complete control Flow Graph of Binary 21

A Gradient-guided Fuzzing Approach to Recover More Complete ...

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Qianjin Li, Xiangdong He, Yuchen Yue, Chong Dept. ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China

ISBN: (纸本)9798350329223

Recovering control flow graphs of binary is a code analysis task for generating program control structures, the results of which can be applied in malicious code analysis, vulnerability mining, and other cyberspace security fields. However, existing binary control flow graph recovery methods suffer from being incomplete and inefficient, while special code structures such as indirect jumps affect the application of recovery techniques. The fuzzing-based approach utilizes inputs of mutation to obtain test cases that can be executed up to an indirect jump. However, this method ignores the branch behavior of inputs in relation to coverage, generating inputs without considering the validity of those inputs, and failing to efficiently guide indirect jumps. To recover a more complete control flow graph, we propose a gradient-guided fuzzing to solve the indirect jump problem. First, the base CFG of the program is recovered using static analysis. Next, we optimize the test case generation utilizing a gradient-guided approach to discover indirect jump relationships by monitoring the execution of these test cases. Finally, indirect jump relations are added to the base CFG to obtain a more complete control flow graph. Experimental results show that our approach outperforms existing methods in solving indirect jumps. © 2023 IEEE.

关键词： Static analysis

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Real-Time Anomaly Detection in 5G Networks Through Edge Computing 3

Real-Time Anomaly Detection in 5G Networks Through Edge Comp...

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3rd IEEE international conference on intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Shaik, Riaz Raju, Dara Behera, Prakash Chandra Changala, Ravindra Mary, S. Suma Christal Balakumar, A. Kl University Department of Cse Andhra Pradesh Vaddeswaram India Vignana Bharathi Institute and Technology Dept. of Computer Science & Engineering Aushapur Ghatkesar India St. Claret College Department of Science Bangalore India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Ece samayapuram Trichy India

ISBN: (纸本)9798350361186

Strong anomaly detection techniques are becoming more and more necessary as 5G networks develop in order to maintain network performance, security, and dependability. This study leverages the capabilities of Mobile Edge Computing (MEC) to present a novel method for anomaly detection in 5G networks. By processing data closer to the network edge, the integration of MEC offers an efficient and decentralized architecture that lowers latency and improves real-time detection capabilities. The distributed module takes advantage of its close proximity to network devices by using sophisticated algorithms for anomaly detection, which are implemented at the mobile edge. The system can quickly detect abnormalities from typical network activity by utilizing capabilities including Flow Collection, Anomaly Symptom Detection, and Network Anomaly Detection. The distributed module provides anomalous information to the centralized decision-making module for thorough examination. It takes into account variables like resource use and network traffic and integrates this data with metrics gathered from monitoring modules. Because of its adaptive characteristics, the system may expand anomaly detection components, enhance detection functions, and modify virtualized resources in response to shifting network circumstances. The evaluation findings reveal that the suggested anomaly detection method performs well in 5G networks, with decreased false positives, increased responsiveness, and better flexibility to changing network conditions. Using MEC not only makes anomaly detection more effective, but it also fits in with the 5G design, which makes it a viable option for protecting the upcoming generation of communication networks. It obtains 95% accuracy in classification. The suggested approach has proven to be resilient in handling security issues by producing outcomes that are either comparable to or better than those attained by other techniques that have been previously presented in the s

关键词： 5G mobile communication systems

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Improving Traffic Sign Recognition with the YOLOv9 Algorithm 2

Improving Traffic Sign Recognition with the YOLOv9 Algorithm

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Anand, Ketan Dwivedi, Shyam Shankar Department of Computer Science and Engineering Rameshwaram Institute of Technology & Management AKTU Lucknow India

ISBN: (纸本)9798331530389

Modern intelligent transportation systems and autonomous driving technologies are quite helpful in light of the population's increasing growth, and they work particularly well when used in conjunction with traffic sign monitoring. By giving drivers accurate and exact traffic signals, such as speed limits, warnings, and directional assistance, this makes transportation much safer. Autonomous vehicles must correctly understand these indicators in order to make wise driving judgments. Accurately identifying and classifying a wide variety of traffic signs in a range of environmental circumstances is a difficulty. A cutting-edge deep learning model created specifically for object recognition tasks is called YOLOv9 (You Only Look Once version 9).In addition to offering respectable speed and accuracy, YOLOv9 expands on the advantages of its predecessors by implementing cutting-edge strategies such as PGI and *** of these improvements, YOLOv9 is especially ideal for real-time applications like recognition of traffic signs, where more accuracy and quick and accurate object identification are essential. With an increased accuracy the results demonstrate the model's stability and dependability and show that it is appropriate for real-world uses in autonomous driving and traffic control. Subsequent investigations will concentrate on improving resilience in various situations, utilizing semi-supervised learning, creating more resource-efficient models, broadening the range of datasets, and combining traffic sign recognition with additional autonomous driving tasks. The development of dependable and effective traffic sign detection systems is furthered by this work. © 2025 IEEE.

关键词： Traffic signs

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Fusion Detection Algorithm of Maritime Radar and Electro-Optical Pod for Complex Sea Conditions 5

Fusion Detection Algorithm of Maritime Radar and Electro-Opt...

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5th international conference on intelligent control, Measurement and Signal Processing, ICMSP 2023

作者： Wang, Mingming Wang, Qingli Wang, Yihui Li, Zhenglin Shao, Shilin Zhou, Yang School of Computer Engineering and Science Shanghai University Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Future Technology Shanghai University Shanghai China

ISBN: (纸本)9798350336030

In order to improve the perceptual capability of Unmanned Surface Vessels (USVs) in complex sea conditions, a fusion detection algorithm has been developed that combines maritime radar and electro-optical (EO) pod. This algorithm integrates the positional state of the USV, class information of the maritime target obtained through a vision-based target detection algorithm, and processed maritime radar information. By using Gaussian and morphological filtering, the fusion of position state information from the USV, EO pod data, and maritime radar data is achieved. This effectively mitigates the negative impact of environmental factors, such as waves and water mist, on target detection. A cooperative sensing process between the maritime radar and EO pod is proposed to fuse the data obtained from both sources, resulting in accurate comprehensive target information. Experimental results show that the fusion algorithm achieves an average accuracy of 80.24% and a detection rate of 74.7%, enabling precise detection of maritime targets, including their distance, speed, and category. This enhanced perceptual capability is essential for USVs operating in complex maritime environments, enabling better decision-making and navigation performance. © 2023 IEEE.

关键词： Decision making

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Optimization of Transmission in Heterogeneous Integrated Networks for Active Distribution Substation 4

Optimization of Transmission in Heterogeneous Integrated Net...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Zhang, Pan Wu, Lei Pang, Chao He, Enchao State Grid Tianjin Electric Power Company Electric Power Research Institute Tianjing China Tianjing Sanyuan Electric Power Intelligent Technology Co. Ltd. Tianjing China

ISBN: (纸本)9798350373233

The distribution range of active distribution sub-stations is extensive, and their topology structures are diverse. The presence of numerous distributed power access points poses significant challenges to observability and operational control, necessitating the urgent strengthening of communication infrastructure. Traditional optical fiber dedicated networks require substantial economic and manpower investments, while relying solely on power line communication and low-power wireless communication methods is insufficient to meet various communication requirements. In response to this, this paper proposes a heterogeneous integrated network transmission optimization method that combines power line communication and wireless communication for active distribution substations. In the context of supporting multiple communication paths in heterogeneous CRANs, the unreliable communication caused by interference is avoided, and corresponding solving methods are proposed. Simulation results demonstrate that the proposed algorithm can identify the optimal transmission path from any source node to the central aggregation node that satisfies the performance requirements, thereby validating the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Optical fibers

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1st international Scientific and Practical conference on Information Technologies and intelligent Decision Making Systems, ITIDMS 2021

1st International Scientific and Practical Conference on Inf...

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1st international Scientific and Practical conference on Information Technologies and intelligent Decision Making Systems, ITIDMS 2021

ISBN: (纸本)9783031213397

The proceedings contain 13 papers. The special focus in this conference is on Information Technologies and intelligent Decision Making Systems. The topics include: Tone Image Processing with Discrete Data Structure;the Concept of Client-Server Architecture for Remote control of a distributed Knowledge Base;experimental Study of Russian-Language News Text Categorization Performance Using Machine Learning Algorithms;experimental Evaluation of the Efficiency of Compression of Files by Fractal-Spectral Codec;an intelligent Decision Support System for Solving Complex Territorial Planning Problems;fault Tolerance of distributed Worker Processes in Corporate Information Systems and Technologies;functioning of the Thermal Memory Cell;using Data Mining Technology in monitoring and Modeling the Epidemiological Situation of the Human Immunodeficiency Virus in Kazakhstan;information Technology and Artificial Intelligence in Improving of Center Pivot Irrigation control;KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method;prioritization of distributed Worker Processes Based on Etcd Locks.

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Implementation of Internet of Things in Wireless Sensor Networks for environmental monitoring 2

Implementation of Internet of Things in Wireless Sensor Netw...

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2nd IEEE international conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Vineela, M. Krishna, V. Rama Rani, K. Sandhya Nadh Singh, Bhaludra R. Sri Varsha, Kasarla Vaishnavi, Kura Venkata Sai Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad India Department of Computer Science and Engineering Annamacharya institute of Technology and Sciences Telangana Hyderabad India Department of Computer Science and Engineering MallaReddy College of Engineering &Technology Telangana Hyderabad India Bhoj Reddy Engineering College for Women Telangana Hyderabad59 India

ISBN: (纸本)9798350315905

Data is collected by the end tags, which in turn send it to the router, which forwards it to the coordinator and provides services to multiple clients, one of which is the display of collected data. Finally, the collected data is sent to the cloud via the base station, which can be accessed remotely via Ethernet. These include sensors for heat, cold, vibration, pressure, humidity, light, and air and water pollution. Recently, attention has been focused on wireless technologies such Wi-Fi [8, 9], Bluetooth [7, 10], and Zig Bee [6, 7].Ultimate goal in writing this article is to provide a framework that can be utilised on a global scale to control variables in any ecosystem. All environmental data in real time is gathered by a Raspberry Pi and associated sensors, and then retrieved by a web server for display. Since this information is available online, the user is not restricted to their physical location. The Raspberry Pi serves as a hub that establishes a zigzag link between a network of dispersed sensor nodes. In several applications, including habitat monitoring, "Wireless Sensor Networks" (WSN) have been utilized to gather data on physical phenomena. The term "Internet of Things" (IoTs) refers to the interconnection and intelligent linking of items such as smartphones, Internet televisions, sensors, and actuators, which creates new channels of interaction between people and their belongings. © 2023 IEEE.

关键词： Internet of things

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Oil Spill Detection on Sea Surface Based on SAR-Images by Using Global Context and Edge Perception 3

Oil Spill Detection on Sea Surface Based on SAR-Images by Us...

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3rd international conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Rong, Haitao Chen, Xianqiao Xie, Kai Wang, Qirui School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359961

Synthetic Aperture Radar (SAR) has been widely used in marine environmental monitoring. However, there are some challenges in the detection of oil spills based on SAR images. Existing oil spill detection methods inadequately consider the characteristics of SAR images, such as high noise, complex texture, and blurry boundaries, resulting in poor edge accuracy and false positives and negatives in oil spill region detection. In response to these issues, this paper proposes a sea surface oil spill detection model based on global context and edge awareness. Firstly, the model enhances the extraction and utilization of edge information by designing an edge-Aware detection branch network. Secondly, improvements are made to the feature extraction method of the encoder. The backbone network is enhanced for capturing low-level feature information of oil spills by introducing the CA (Channel Attention) module. The use of a dense-connected spatial pyramid module expands the receptive field to enhance global context awareness, thereby improving the extraction of high-level semantic features from oil spill features. Finally, through comparative experiments, the effectiveness of the proposed detection model in this chapter is validated. © 2023 IEEE.

关键词： attention mechanism edge perception oil spill detection SAR images

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Performance Analysis of Moving Average Filtering Used in TDLAS carbon dioxide Sensing System 1

Performance Analysis of Moving Average Filtering Used in TDL...

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1st international conference on Spatial Atmospheric Marine environmental Optics, SAME 2023

作者： Yang, Chen Gui, Lin Lin, Hao Dai, Pan Liu, Zijun School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China School of Resource and Environmental Engineering Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Optical Sensing and Regulation Technology Ministry of Education Nanjing University Nanjing210023 China

ISBN: (纸本)9781510666337

Reducing carbon emissions is a global priority due to human impact on atmospheric pollution and the greenhouse effect. Achieving carbon peak and neutrality requires real-time monitoring of CO2 concentrations. However, developing high-sensitivity, portable, and anti-jamming gas detection solutions is challenging. Among spectroscopic techniques, Tunable Diode Laser Absorption Spectroscopy (TDLAS) is highly sensitive for detecting CO2 concentrations. This paper elaborates on the principles of TDLAS for detecting CO2 concentrations and proposes a noise reduction algorithmto meet diverse environmental requirements. Simulations were performed using software to simulate CO2 absorption spectra at approximately 1.57866535μm under high-intensity noise (0.1mW - 1mW). Based on this simulation, we applied the Wavelength Modulation Spectroscopy (WMS) technique to calculate the ratio of the output differential signal's second harmonic intensity to the first harmonic S2f/1f(T) and output power to reduce light intensity influence and improve concentration inversion linearity. The weighted convolutional moving average filtering was utilized to optimize WMS denoising, utilizing weight transfer to make the process more precise and reliable. After analyzing various window functions, it was concluded that a window length of 9 would be the most optimal. The algorithm improved the signal-to-noise ratio (SNR) by 22.435% under these conditions. When the noise level increased fourfold from the original signal, the algorithm enhanced the SNR by 59.514%, enabling reliable CO2 monitoring even under challenging conditions. © 2023 SPIE.

关键词： Carbon dioxide

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A Transfer Learning Approach to Bird Species Recognition using MobileNetV2 7

A Transfer Learning Approach to Bird Species Recognition usi...

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7th international conference on intelligent Computing and control Systems, ICICCS 2023

作者： Kondaveeti, Hari Kishan Guturu, S.V.N. Sai Vignesh Jayan Praveen, K.S. Kumar, Samparthi V S VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350397253

The growing interest in biodiversity and conservation has heightened the need for accurate and efficient bird species recognition. This is because bird populations, their distribution, migration patterns, and environmental impact assessments can benefit from the valuable data these recognition methods provide. To address the complex task of manual bird species classification, this research paper presents a transfer learning approach based on MobileNetV2. The proposed solution uses MobileNetV2 to categorize bird species in photographs, achieving 96.79% accuracy on a defined dataset. This work highlights the potential of transfer learning in solving complex recognition tasks and the efficiency of MobileNetV2 in achieving high accuracy despite its lightweight design. The method has real-world applications, including real-time bird identification systems and bird population monitoring, and will become more valuable with advancements in hardware and deep learning. To improve the overall system's accuracy even further, using a larger dataset, scheduling regular retraining sessions and further fine-tuning the model is suggested. In this study, the model is trained separately on two different datasets. The first dataset contains around 1800 images with 200 types of bird species. The second dataset contains around 180 images with 20 types of bird species, specifically South Indian bird species. The attained accuracy with the first dataset is 90.91% and the attained accuracy with the second dataset is 96.79%. © 2023 IEEE.

关键词： environmental impact assessments

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