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检索条件"机构=Cyber Security in Computer Science and Engineering"

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Data-Driven Unified Channel Modeling of WiFi and LiFi Using Conditional GAN 5

Data-Driven Unified Channel Modeling of WiFi and LiFi Using ...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Saif, Amal Ibrahim, Rahmeh Almajali, Sufyan Salameh, Haythem Bany Princess Sumaya University for Technology Computer Science Department Amman11941 Jordan Princess Sumaya University for Technology Cyber Security Department Amman11941 Jordan College of Engineering Al Ain University Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

Wireless communication systems, rooted in the propagation of electromagnetic waves, have traditionally operated within the framework of digital modules such as source coding, encryption, and modulation. However, the conventional 'modular' approach faces challenges in achieving 'global optimization,' relying on simulated environments that diverge from real-world channel conditions. This paper proposes a solution to integrate deep learning into wireless communication systems, envisioning intelligent communication systems that learn and adapt. Recent strides in wireless channel modeling have embraced Generative Adversarial Networks (GANs) to generate realistic communication channel models. GANs applied to optical and Radio Frequency (RF) channels bridge gaps where actual measurements are unavailable, providing synthetic models that mirror real-world statistics. This paper's contribution involves innovating channel modeling by replacing autoencoder latent spaces with a GAN model, generalizing the model for diverse channel types, implementing a data-driven approach using Conditional GANs for realistic channel effects, and constructing a unified model adept at simultaneously handling WiFi and LiFi channels. The envisioned outcome is a groundbreaking approach that transcends the limitations of traditional wireless communication, propelling intelligent communication networks into an era of adaptability and optimized performance. © 2024 IEEE.

关键词： Wi-Fi

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Contrastive Learning at the Relation and Event Level for Rumor Detection 48

Contrastive Learning at the Relation and Event Level for Rum...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yingrui Hu, Jingyuan Ge, Jingguo Wu, Yulei Li, Tong Li, Hui Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Exeter Faculty of Environment Science and Economy Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data. © 2023 IEEE.

关键词： Graph neural networks

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Detection of Network Attacks using Machine Learning and Deep Learning Models

Detection of Network Attacks using Machine Learning and Deep...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Dhanya, K.A. Vajipayajula, Sulakshan Srinivasan, Kartik Tibrewal, Anjali Kumar, T. Senthil Kumar, T. Gireesh TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India STSM Architect IBM Security IBM India Sr. Architect IBM Security IBM India Pvt Ltd India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Anomaly-based network intrusion detection systems are highly significant in detecting network attacks. Robust machine learning and deep learning models for identifying network intrusion and attack types are proposed in this paper. Proposed models have experimented with the UNSW-NB15 dataset of 49 features for nine different attack samples. Decision Tree classifier produced the best accuracy of 99.05% compared to ensemble models - Random forest(98.96%), Adaboost(97.87%), and XGBoost(98.08%). K-Nearest Neighbour classifier trained for various values of K and best performance obtained for K=7 with the accuracy of 95.58%. A Deep Learning model with two dense layers with ReLU activation and a third dense layer with a Sigmoid activation function is designed for binary classification and produced good accuracy of 98.44% with ADAM optimizer, 80:20 Train-Test Split Ratio. Network attack exploits are detected with an accuracy 95% by XGBoost, Fuzzers attack with 90% accuracy by Random Forest, Generic attacks with 99% accuracy by Random Forest, and Reconnaissance attacks with 79% by Decision Trees. All features are relevant and strong in network attack detection, which eliminates the requirement of feature selection. © 2022 Elsevier B.V.. All rights reserved.

关键词： Network security

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Optimizing Energy Management and Load Balancing Through AI-Driven Quantum Approximate Optimization 4

Optimizing Energy Management and Load Balancing Through AI-D...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Manasa, M. Anitha, B.S. Mahadevi Tlajiya, Falguni Chavan, Sangramjit Department of Cyber Security Karkala India Brindavan College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Department of Electrical Engineering Gujarat India Pune India

ISBN: (纸本)9798350352931

The integration of Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. This study explores the application of these quantum computing techniques, coupled with Artificial Intelligence (AI), to optimize energy distribution across smart grids. The proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. The results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. The quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. These findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management. © 2024 IEEE.

关键词： Quantum computers

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Honeypot Comparison of Attack Detection and Mitigation of SSH Attack 3

Honeypot Comparison of Attack Detection and Mitigation of SS...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ali, Wasif Sajid, Ahthasham Ghodke, Tanvi Ajit Malik, Rida Malik, Neeru Kaushik, Keshav Riphah Institute of System Engineering Riphah International University Department of Cyber Security Islamabad Pakistan Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra India School of Engineering & Technology Pimpri Chinchwad University Maharashtra Pune India Amity School of Engineering and Technology Amity University Mohali India

ISBN: (纸本)9798331518592

This research study addresses the importance of honeypots at multiple levels, which includes but not limited to placement in network or group of networks working alongside in an organization. This paper talks about the usage of multiple honeypots working alongside to log useful data for the mitigation of ongoing bot or actual attack this approach helps the organization better understand the threat they are dealing and the techniques which they can use to overcome the gap of the unseen security risks. The comparison will shed better light to the working and attack patterns of the ongoing attacks, gaining more confidence on using the honeypot for better protection. This allows strategic, optimized and critical insight to the evolving attack patterns. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning Based Traffic Detection in Cloud Computing Environment

Deep Learning Based Traffic Detection in Cloud Computing Env...

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International Conference on computer and Applications (ICCA)

作者： Sheily Verma Panwar Aboubakr Aqle (Computer Science/ Cyber Security) CUQ-Ulster University Doha Qatar (Computer Engineering) CUQ-Ulster University Doha Qatar

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This work uses the characteristics of both architectures to present a novel hybrid model combining LSTM and GRU units to enhance anomaly detection in cloud environments. Separately integrating outputs from LSTM and GRU cells, a new fusion layer simultaneously captures long-term and short-term dependencies. Using attention mechanisms improves emphasis on pertinent sequence data, hence increasing detectability and interpretability. While a novel feature extraction technique emphasises important anomalies, adaptive learning rates customised to different model sections maximise learning efficiency. Custom loss functions improve sensitivity to small variations by punishing false negatives. The model distinguishes itself by being dynamically retrained on streaming data sets, therefore providing a self-adj usting mechanism for changing traffic patterns. Transparency in decision-making made possible by explain ability tools such as SHAP and LIME deepens anomaly detection. Genetic algorithms and reinforcement learning among other optimisation techniques help to fine-tune parameters, thereby improving accuracy and efficiency. Emphasising strong performance benchmarks, the model shows scalability and low-latency deployment in large-scale cloud systems. Novel methods of data augmentation replicate different traffic patterns, hence improving model adaptability and resilience. Combining creativity with pragmatic scalability for real-world use, this method offers a convincing, modern solution for difficult anomaly detection in cloud computing.

关键词： Deep learning Cloud computing Adaptation models Accuracy Computational modeling Scalability Decision making Traffic control Long short term memory Anomaly detection

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6GAI: Active IPv6 Address Generation via Adversarial Training with Leaked Information 27

6GAI: Active IPv6 Address Generation via Adversarial Trainin...

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27th International Conference on computer Supported Cooperative Work in Design, CSCWD 2024

作者： Jiao, Liang Zhu, Yujia Zhang, Wenxiu Zhao, Lei Zhou, Yi Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Shandong Branchs Beijing China School of Cyber Security University of Chinese Academy of Sciences National Computer Network Beijing China School of Information Science And Engineering University of Jinan Jinan China National Computer Network Emergency Response Technical Team Coordination Center of China Guangdong Branch Guangdong China

ISBN: (纸本)9798350349184

Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. In this paper, we introduce 6GAI to implement more efficient target address generation. 6GAI is built with Generative Adversarial Net (GAN) integrated with Convolutional Bottleneck Attention Module (CBAM). 6GAI allows the discriminative net to leak generated address's high-level features extracted by CBAM to the generative net, while the generative net incorporates such informative signals into all generation steps through an additional Manger module, which takes the extracted features of current generated address nybbles and outputs a latent vector to guide the Worker module for active IPv6 address generation. This work outperformed the state-of-the-art target generation algorithms on two datasets including one public dataset and one independently collected dataset. © 2024 IEEE.

关键词： Deep learning

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Transforming cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： cybersecurity

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A Novel Method with Transformers for Fine-grained Encrypted Traffic Classification 25

A Novel Method with Transformers for Fine-grained Encrypted ...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Kong, He Li, Tong Ge, Jingguo Li, Hui Yang, Liqun Lu, Zhuang Xu, Zhibin Xie, Chuanxi Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350330014

With the rise of cyber threats and data breaches, encrypted traffic has become increasingly important in ensuring online privacy and security. However, it also brings significant security challenges. Existing studies have shown that encrypted traffic from applications or web services can be identified using various methods. Nevertheless, there is limited generalization ability across multiple scenarios and difficulties in distinguishing similar traffic traces between web pages of the same application. To overcome the limitations of existing methods, we propose ANT-ET, a novel learning method tailored for fine-grained webpage analysis. ANT-ET extracts the contextual semantics from raw traffic and represents it as a semantic vector. These vector representations are subsequently utilized for classification through a classification layer and discriminator. The experimental results show that ANT-ET can effectively understand the semantics of encrypted traffic across various scenarios, achieving F1-score greater than 95% in four public encrypted traffic tasks, outperforming existing methods. In addition, we collected a real-world traffic dataset to verify the effectiveness of ANT-ET for fine-grained webpage detection. © 2023 IEEE.

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