检索结果-内蒙古大学图书馆

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Singh, Jagjot Kumar, Sanu Johnson, Derick Mathew Savita Department of Computer Science Engineering Spl. Digital Forensics and Cyber Security India

Electromagnetic (EM) radiations are the waves of electromagnetic and magnetic field which are generated from electromagnetic devices. These radiations are dangerous to humans as well as animal health. Talking medically, in the human and animal body, these bioelectric signals cause different processes to occur. Exposing to larger EMradiations can cause disrupt in the natural work of the human body. These harmful radiations harm the body at the cellular level. According to the World Health Organization (WHO), the EMF radiations released by mobile phones and otherdevices are hazardous to humans. The objective of this review paper is to carry out a survey to know about the usage of mobile phones and laptops by people and will aware about the harmful impact of them. © Grenze Scientific Society, 2023.

关键词： Cellular telephones

来源：评论

学校读者我要写书评

暂无评论

Exploring the landscape of network security: a comparative analysis of attack detection strategies

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3211-3228页

作者： Rajesh Kanna, P. Santhi, P. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

引用

Iran Journal of computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. 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

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

来源：评论

学校读者我要写书评

暂无评论

Unleashing the power of pseudo-code for binary code similarity analysis

引用

cybersecurity 2023年第2期6卷 44-61页

作者： Weiwei Zhang Zhengzi Xu Yang Xiao Yinxing Xue School of Computer Science and Engineering University of Science and Technology of ChinaHefeiChina School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Code similarity analysis has become more popular due to its significant applicantions,including vulnerability detection,malware detection,and patch *** the source code of the software is difficult to obtain under most circumstances,binary-level code similarity analysis(BCSA)has been paid much attention *** recent years,many BCSA studies incorporating Al techniques focus on deriving semantic information from binary functions with code representations such as assembly code,intermediate representations,and control flow graphs to measure the ***,due to the impacts of different compilers,architectures,and obfuscations,binaries compiled from the same source code may vary considerably,which becomes the major obstacle for these works to obtain robust *** this paper,we propose a solution,named UPPC(Unleashing the Power of Pseudo-code),which leverages the pseudo-code of binary function as input,to address the binary code similarity analysis challenge,since pseudocode has higher abstraction and is platform-independent compared to binary *** selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the *** evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures(X86,ARM),different optimization options(O0-O3),different compilers(GCC,Clang),and four obfuscation *** experimental results show that the accuracy of UPPC in function search is 33.2%higher than that of existing methods.

关键词： Binary code similarity Machine learning Software security Pseudo-code

来源：评论

学校读者我要写书评

暂无评论

An Imperceptible Adversarial Attack Against 3D Object Detectors in Autonomous Driving

引用

IEEE Internet of Things Journal 2025年第12期12卷 21404-21414页

作者： Wang, Yizhou Wu, Libing Jin, Jiong Wang, Enshu Zhang, Zhuangzhuang Zhao, Yu Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

As LiDAR-based 3D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this paper, we investigate the robustness of 3D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model's discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors. © 2025 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

来源：评论

学校读者我要写书评

暂无评论

Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques 1

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Roshan, Rahul G. Salanke, Vishal S Nagasundari, S. Research Center for Information Security Forensics and Cyber Resilience PES University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

EEG-VARNet: An Advanced Deep Learning Model for AccuratePrediction and Classification of Major Depressive Disorder Using EEG Data 6

EEG-VARNet: An Advanced Deep Learning Model for AccuratePred...

引用

6th International Conference on cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mahender, Udutala Sathiyasuntharam, V. Annamalai University Department of Computer Science and Engineering Tamilnadu Chidambaram India CMR Engineering College Department of Cyber Security Hyderabad India

ISBN: (纸本)9798331505790

The correct and early identification of Major Depressive Disorder (MDD) is challenging because of its complicated causes and symptoms that are similar to those of other mental diseases. This study introduces a new method for forecasting and categorizing MDD by utilizing EEG data, with a specific emphasis on Valence and Arousal measurements. Our approach combines sophisticated methods for extracting and selecting features with deeplearning models. Firstly, EEG signals are subjected to preprocessing in order to eliminate artifacts and standardize the quality of the data. The Boruta algorithm is utilized to extract features, which are then selected using the improved Cuckoo Search Algorithm. The suggested model, referred to as EEG-VARNet, utilizes two CNN models (Xception V4) and an SVM classifier to capture both spatial and temporal dependencies present in the EEG data. The model undergoes hyperparameter adjustment to maximize performance. The results of our study show substantial enhancements in the prediction of MDD with an accuracy rate of 9 8. 2 %, a precision rate of 96.43 %, and a recall rate of 9 7. 8 9 %. The measurements demonstrate the model's exceptional ability to diagnose MDD, emphasizing its promise as a dependable tool for clinical use. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Malicious PE File Detection Using Machine Learning: An Analysis of Header Features

Malicious PE File Detection Using Machine Learning: An Analy...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shyam Kumar, S. Shetty, Jyothi Department of Cyber Security Nitte574 110 India Department of Computer Science and Engineering Nitte574 110 India

ISBN: (纸本)9798331517892

Portable Executable (PE) files serve as the foundation of the Windows operating system, containing executable code, object code, and Dynamic Link Libraries (DLLs). Their importance lies in their capacity to execute tasks and run programs within the Windows environment, rendering them essential to software execution. Comprehension of PE files is crucial in cybersecurity due to their prevalence and potential for harboring malicious software. Analysis of PE files enables researchers and analysts to uncover insights into their structure, behavior, and dependencies, which is essential for detecting and categorizing potential threats. PE file analysis provides critical information about a program's characteristics, facilitating the identification of indicators of compromise and potential security risks. Through examination of PE headers, sections, and attributes, analysts can gain insights into the file's functionality, dependencies, and potential security vulnerabilities. This knowledge equips cybersecurity professionals to develop robust detection and mitigation strategies, safeguarding systems and networks against cyber threats. In this paper, we examine the intricate details of PE files, utilizing tools such as PE-bear, PEinfo, CFF Explorer, and PEStudio to conduct comprehensive analysis. Our study employs machine learning techniques, specifically the Random Forest algorithm, to enhance malware detection capabilities. By preprocessing and augmenting the dataset with new executable files, we aim to enhance the efficacy of our analysis and provide valuable insights for cybersecurity research and defense strategies. © 2024 IEEE.

关键词： cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Real-Time Detection of AI-Generated Deepfake Audio: A Novel Approach

Real-Time Detection of AI-Generated Deepfake Audio: A Novel ...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Prathamesh Chiddarwar Computer Science Engineering (Specialization in Cyber Security and Forensics) MIT World Peace University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The rise of AI-generated deepfake audio presents a significant challenge in audio forensics, as traditional detection methods are inadequate against sophisticated manipulations. This study proposes a novel approach combining deep learning algorithms with signal processing techniques to detect deepfake audio in real-time. By leveraging voice pattern recognition and anomaly detection, the method achieves high accuracy and low latency. Experimental results show a significant improvement in detection performance, with the integrated system outperforming traditional methods. The proposed solution demonstrates potential for enhancing security and digital forensics by enabling the swift identification of deepfake audio.

关键词： Deep learning Deepfakes Accuracy Government Signal processing algorithms Signal processing Real-time systems Pattern recognition security Low latency communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：