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cyber Science and technology Congress (cyberSciTech), IEEE

作者： Ruihao Chen Hui Lin Qingxin Lin Hui Ju College of Computer and Cyber Security Fujian Normal University Fuzhou Fujian China Department of Computer Engineering Fuzhou University Zhicheng College Fuzhou Fujian China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

With the advancement of anti-forensic techniques and network attack technologies aided by fileless methods, traditional forensics techniques based on non-volatile storage devices are increasingly encountering obstacles. Consequently, the forensic analysis of runtime data within memory has become increasingly important. In response to the challenges faced by devices running Linux systems, which are constrained by the multitude of Linux kernel versions and distributions, making memory forensics a non-trivial task, this research proposes an automated scheme that deploys a simulated environment consistent with the target system's Linux kernel based on containerization technology. The scheme extracts the memory data structure table from the simulated environment to assist analysts in analyzing Linux system memory images. Additionally, this proposal introduces an automated analysis of Linux memory images to enhance the efficiency of analysts' work, allowing them to focus their energies on the collation and analysis of more critical evidence information, thereby improving the overall efficiency of memory forensics analysis.

关键词： Image forensics Runtime Nonvolatile memory Linux Memory management Data structures Proposals Data mining Kernel

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Combating Terrorism: Detecting and Analyzing Extremist Content on X (Formerly Twitter)

Combating Terrorism: Detecting and Analyzing Extremist Conte...

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Delhi Section Conference (DELCON), IEEE

作者： Gideon Mwendwa Lokesh Chouhan Felix Etyang Ngaira Mandela Uwineza Gift School of Cyber Security and Digital Forensics National Forensic Sciences University Goa India School of Engineering Department of Computer Science Cochin University of Science and Technology Cochin India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Jinja Uganda

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This study investigates the efficacy of machine learning algorithms in detecting and evaluating extremist content on social media platforms, and we employ X, formerly known as Twitter. We therefore utilize Kaggle's dataset that contains 83,000 entries from both extremist and non-extremist posts to compare the performance of four machine learning models, namely Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Naive Bayes. Moreover, we examine a Stacking Classifier that leverages the advantages of all four models. The models were assessed using accuracy, precision, recall, F1 Score, ROC-AUC Score, training duration, and prediction time. The findings of our study revealed that the Support Vector Machine (SVM) algorithm was the most accurate and resilient, whereas Naive Bayes showcased remarkable computing efficiency, rendering it well-suited for real-time detection. The Random Forest and Gradient Boosting algorithms offered a well-balanced approach. Notably, the novel Stacking Classifier outperformed individual models, demonstrating the strength of a hybrid methodology. Hence our study presents a thorough structure for improving the identification of extremist material on social media, thereby providing essential knowledge for creating efficient, immediate detection systems to combat online extremism successfully.

关键词： Support vector machines Training Accuracy Social networking (online) Computational modeling Stacking Boosting Real-time systems Bayes methods Random forests

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GLLaMoR: Graph-based Logic Locking by Large Language Models for Enhanced Robustness

GLLaMoR: Graph-based Logic Locking by Large Language Models ...

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VLSI Test Symposium

作者： Akashdeep Saha Prithwish Basu Roy Johann Knechtel Ramesh Karri Ozgur Sinanoglu Lilas Alrahis Center for Cyber Security New York University Abu Dhabi UAE Department of Electrical and Computer Engineering New York University Tandon School of Engineering Department of Computer and Information Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331521448

ISBN: (纸本)9798331521455

Logic locking protects integrated circuits (ICs) from design piracy. The idea is to insert key-controlled components, a.k.a. key-gates, to lock the IC’s functionality, where the correct key is the designer’s secret. The robustness of logic locking can be enhanced by carefully identifying best locations to insert key-gates, e.g., by analyzing the IC’s topology and lock parts with high impact on functional behaviour. Traditionally, the challenge of identifying critical locations relies on computationally-intensive graph traversal and design methods like fault analysis. The rise of large language models (LLMs), which have recently demonstrated proficiency also on complex graph data, presents an interesting opportunity to revisit this challenge. Here, we present GLLaMoR, a first-of-its-kind framework using LLMs on graph-based IC representations to identify critical locking locations. Through LLM performance evaluation and end-to-end case studies, we demonstrate that GLLaMoR paves the way for more effective and scalable logic locking.

关键词： Performance evaluation Fault diagnosis Large language models Design methodology Very large scale integration Robustness Topology Logic Circuit faults

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IoT-based Posture Pro: Safeguarding the Human Spine from the Onset of Hunchback in the Initial Stages 8

IoT-based Posture Pro: Safeguarding the Human Spine from the...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Kumar, D Sathish Ramya, G. Ravi, S. Giri, Jayant Odeh, Mahmoud Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350367904

The Posture Pro, a smart body posture improver, is a specialized device aiding people in maintaining good posture while sitting or standing. It distinguishes itself from standard correctors by its ability to attach to and detach from a chair, providing customized support. Poor posture can lead to various health issues, such as headaches, stomach troubles, and back/neck discomfort. The Posture Pro gently realigns the shoulders and spine, reducing muscle and joint tension, improving circulation, and minimizing injury risk. It is especially beneficial for desk-bound individuals or those with chronic back pain, aiding in posture correction, pain relief, and enhanced mobility. It is critical to combine its use with regular exercise and stretching to maintain general wellness. There are other styles to choose from, such as materials for bracing that are apparent for support or covert ones that are worn under clothes. It is essential to fit and adjust properly to avoid pain or a worsening of existing conditions. It is advised to adopt the modified posture gradually to give the body time to adjust. The Posture Pro is a flexible way to correct posture both in and out of the chair because of its detachable and chair-fixable function. © 2024 IEEE.

关键词： Medical problems

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Comparative Assessment of Brain Tumor Detection Techniques: A Comparative Study

Comparative Assessment of Brain Tumor Detection Techniques: ...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Pooja Dahiya Anshu Sharma Manisha Sharma Vijay Bharti Department of Computer Science and Engineering(Cyber Security) Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Science and Engineering Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Application Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Science and Engineering Apiit SD India Panipat Panipat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Medical picture segmentation refers to the process of categorizing the different parts of an image into anatomical components or defining the changes in structure and intensity in relation to the underlying functional process. The primary objective of this research is to analyze atherosclerotic lesions in living organisms using Magnetic Resonance Imaging (MRI). This research primarily aims to utilize computer technology to aid in the diagnosis and assessment of illness, with a particular emphasis on finding vulnerable plaques. MRI is a commonly used technique that helps to detect and forecast brain cancers in different neurological conditions and circumstances. Standard MRI sequences are frequently employed to distinguish various forms of brain tumors through visual examination of their characteristics and analysis of the contrast texture of the soft tissue. The brain tumor detection and classification system are developed using Discrete Wavelet Transform (DWT) and Support Vector Machines (SVMs). An efficient automated method for identifying a potential Region of Interest (ROI) where the existence of a tumor is certain. This study assesses the efficacy of the brain tumor extraction technique using symmetry analysis by calculating the mean and median values of the picture, specifically focusing on precision and sensitivity. Hence, the suggested technique is appropriate for rapid initial tumor extraction rather than precise segmentation.

关键词： Training Support vector machines Image segmentation Visualization Magnetic resonance imaging Brain cancer Transforms Discrete wavelet transforms Standards Tumors

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Advancing Alzheimer's Diagnosis Through Transfer Learning with Deep MRI Analysis

Advancing Alzheimer's Diagnosis Through Transfer Learning wi...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Adityasai B Nirmal Varghese Babu Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Alzheimer's disease (AD) poses a significant health-care challenge, necessitating early and accurate diagnosis for effective management. This study presents a novel approach aimed at enhancing AD diagnosis through the integration of transfer learning, deep learning techniques, and meticulous data preprocessing methodologies. Leveraging a rich dataset from the Open Access Series of Imaging Studies (OASIS), comprising MRI scans from both healthy individuals and those with AD, the proposed method seeks to identify subtle abnormalities indicative of the disease. The methodology encompasses several key steps. Initially, MRI data undergoes rigorous cleaning and standardization using histogram equalization to ensure compatibility and remove irrelevant information. Subsequently, a two-step feature extraction process involving an autoencoder neural network is employed to identify significant AD-related features. These features are then combined with other relevant data to provide a comprehensive understanding of the disease. To optimize model efficiency, three algorithms iteratively refine the feature set, focusing on the most informative features associated with AD. Finally, a pretrained deep learning model, VGG19, is fine-tuned using the AD-specific dataset. Results demonstrate a significant improvement in diagnostic accuracy post-hyperparameter tuning, with accuracy increasing from 76.5% to 89.5%. The enhanced model performance across various metrics underscores the potential of the proposed approach to revolutionize AD diagnosis, offering promise for earlier detection and more accurate prognostication. This research contributes to advancing the field of neuroimaging-based diagnostic techniques for neurodegenerative diseases like Alzheimer's.

关键词： Deep learning Measurement Accuracy Magnetic resonance imaging Transfer learning Neural networks Standardization

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Invulnerability Optimization of Communication Network Based on Analog Attack Strategy 11th

Invulnerability Optimization of Communication Network Based ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Xia, Lingling Ni, Xueli Jing, Zhengjun Liu, Jiayin Zhang, Yan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China College of Computer Engineering Jiangsu University of Technology Changzhou213001 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Invulnerability of communication networks directly related to security and reliability of networks has received increasing attention in recent years. In order to enhance the destruction resisting capacity of communication network, we propose a new invulnerability optimization method based on analog attack strategy and present an improved invulnerability measure with consideration of network connectivity and construction cost. The experimental simulations are conducted to construct four different optimized communication networks using analog attack strategy with different indicators of node centrality on the initial network respectively. Results demonstrate that the improved metric used to evaluate the economic benefits of network invulnerability is reasonable, and the proposed topology optimization method can improve the invulnerability of communication network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Structural optimization

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative analysis of different scene for face mask detection based on YOLOv4 3

Comparative analysis of different scene for face mask detect...

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3rd International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Cheng, Zuozheng Zhang, Chaoran Zhu, Yudong Department of Computer Science and Technology Zhengzhou University Zhengzhou450001 China Department of Information Security China University of Mining and Technology Xuzhou221116 China Department of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510661301

The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement. © 2023 SPIE.

关键词： Deep learning

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Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

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computer Systems Science & engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

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