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4th International Conference on Networking, Information Systems and Security, NISS 2021

作者： Fatima, Kiran Zahoor, Kanwal Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women

ISBN: (纸本)9781450388719

Traditional networks are complex and hard to manage due to many reasons such as manual configuration requirements of dedicated devices, lack of flexibility and a non-dynamic approach. To overcome these limitations and to meet the challenges of modern networks a new networking paradigm software Defined Networking (SDN) has been introduced. SDN presents a centralized and completely dynamic environment which provides flexibility and programmability in networks. It enables the network to be controlled centrally and intelligently using multiple software applications. SDN being in its infancy, brings along new challenges. Standardization of various interfaces, scalability, compatibility of contrasting or divergent networks, and vulnerability issues are few of them. This paper discusses various vulnerabilities and possible attacks on every layer of an SDN and focuses on control plane attacks. Further, it presents a comprehensive survey on numerous attacks on the brain of an SDN i.e. the control plane along with existing solutions. The study concludes that despite the challenges that are worth debating, SDN has many characteristics that make it an ideal candidate for future networks. © 2021 ACM.

关键词： Attacks on control plane SDN security software defined network (SDN) Vulnerabilities of SDN

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SACK: Enabling Environmental Situation-Aware Access Control for Vehicles in Linux Kernel

SACK: Enabling Environmental Situation-Aware Access Control ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Boyan Chen Qingni Shen Lei Xue Jiarui She Xiaolei Zhang Xiapu Luo Xin Zhang Wei Chen Zhonghai Wu School of Software and Microelectronic Peking University National Engineering Research Center for Software Engineering Peking University School of Cyber Science and Technology Sun Yat-sen University Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Connected and autonomous vehicles (CAVs) operate in open and evolving environments, which require timely and adaptive permission restriction to address dynamic risks that arise from changes in environmental situations (hereinafter referred to as situations), such as emergency situations due to vehicle crashes. Enforcing situation-aware access control is an effective approach to support adaptive permission restriction. Current works mainly implement situation-aware access control in the permission framework and API monitoring in user space. They are vulnerable to being bypassed and are coarse-grained. Autonomous systems have widely adopted mandatory access control (MAC) to configure and enforce system-wide and fine-grained access control policies. However, the MA $C$ supported by Linux security modules (LSM) relies on predefined security contexts (e.g., type) and relatively fixed permission transition conditions (e.g., exec syscall), which lacks consideration of environmental factors. To address these issues, we propose a Situation-aware Access Control framework in the Kernel (SACK), which enforces adaptive permission restriction based on environmental factors for CAVs. Incorporating environmental situations into the LSM framework is not straightforward. SACK introduces situation states as a new security context for abstracting environmental factors in the kernel. Subsequently, SACK utilizes a situation state machine to implement new adaptive permission transitions triggered by situation events. In addition, SACK provides a novel situation-aware policy language that links specific user space permissions to MAC rules while maintaining compatibility with other LSMs such as AppArmor. We develop two prototypes: an independent SACK with its own policies and a SACK-enhanced AppArmor that adaptively updates the corresponding policies of AppArmor. The experimental results demonstrate that SACK can efficiently enforce situation-adaptive permissions with negliaible runtime ove

关键词： Access control Connected vehicles Runtime Linux Prototypes Environmental factors Kernel Vehicle dynamics Autonomous vehicles Monitoring

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Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization

arXiv

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arXiv 2024年

作者： Wang, Zeyi Liu, Songbai Chen, Jianyong Tan, Kay Chen College of Computer and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularly in scenarios involving constraints. In this study, we employ a large language model (LLM) to enhance evolutionary search for solving constrained multiobjective optimization problems. Our aim is to speed up the convergence of the evolutionary population. To achieve this, we finetune the LLM through tailored prompt engineering, integrating information concerning both objective values and constraint violations of solutions. This process enables the LLM to grasp the relationship between well-performing and poorly performing solutions based on the provided input data. Solution’s quality is assessed based on their constraint violations and objective-based performance. By leveraging the refined LLM, it can be used as a search operator to generate superior-quality solutions. Experimental evaluations across various test benchmarks illustrate that LLM-aided evolutionary search can significantly accelerate the population’s convergence speed and stands out competitively against cutting-edge evolutionary algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Cross Device Federated Intrusion Detector for Early Stage Botnet Propagation in IoT

Cross Device Federated Intrusion Detector for Early Stage Bo...

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Annual IEEE Systems Conference

作者： Angela Grace Famera Raj Mani Shukla Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio Computing and Information Science Anglia Ruskin University Cambridge

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

A botnet is an army of zombified computers infected with malware and controlled by malicious actors to carry out tasks such as Distributed Denial of Service (DDoS) attacks. Billions of Internet of Things (IoT) devices are primarily targeted to be infected as bots since they are configured with weak credentials or contain common vulnerabilities. Detecting botnet propagation by monitoring the network traffic is difficult as they easily blend in with regular network traffic. The traditional machine learning (ML) based Intrusion Detection System (IDS) requires the raw data to be captured and sent to the ML processor to detect intrusion. In this research, we examine the viability of a cross-device federated intrusion detection mechanism where each device runs the ML model on its data and updates the model weights to the central coordinator. This mechanism ensures the client’s data is not shared with any third party, terminating privacy leakage. The model examines each data packet separately and predicts anomalies. We evaluate our proposed mechanism on a real botnet propagation dataset called MedBIoT. Overall, the proposed method produces an average accuracy of 71%, precision 78%, recall 71%, and f1-score 68%. In addition, we also examined whether any device taking part in federated learning can employ a poisoning attack on the overall system.

关键词： Data privacy Accuracy Federated learning Botnet Intrusion detection Telecommunication traffic Data models

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th International Conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Research on the Co-Creation Construction Model of Digital Education Resources in Internet+ Era 23

Research on the Co-Creation Construction Model of Digital Ed...

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7th International Conference on Education and Multimedia Technology, ICEMT 2023

作者： Chen, Ruxian Li, Xufeng Bian, Buxing Wang, Jie Cai, Ruiqi Zhang, Jing Department of Computer Science Software Engineering Institute of Guangzhou China Wushen Banner High School Nei Monggol China Leshan Normal University Teaching Departmen China Institute of Computing Science and Technology Guangzhou University China

ISBN: (纸本)9798400709142

With the development of Internet+, an open and innovative educational ecosystem is forming. The combination of Internet and education can greatly promote the integration and utilization of high-quality education resources. Firstly, we analyze the main problems in the construction of digital education resources. Secondly, we explore the co-creation construction model of high-quality mathematical resources among government, enterprises and schools, including the main elements of the model, the connection between the elements and the related mechanism support. Finally, it takes NetPad as an example to carry out practical research on the co-creation of digital education resource in the field of basic education, improving the construction and service level of high-quality digital education resources. © 2023 ACM.

关键词： E-learning

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Ensuring IoT Controller Reliability with Colored Generalized Stochastic Petri Net

Ensuring IoT Controller Reliability with Colored Generalized...

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International Computer Science and engineering Conference (ICSEC)

作者： Kruntarat Samngamnoi Sutinun Pomsiri Apipath Kamput Chanon Dechsupa Department of Computer Science Intelligent Software Engineering Research Group College of Computing Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method’s efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification.

关键词： Codes Petri nets Stochastic processes C++ languages Transforms Control systems Internet of Things Reliability Washing machines Self-service

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引用

作者： Tingyu Shi Department of Computing and Software Faculty of EngineeringMcMaster University

Searching for influencers among a social network is important because marketers can then use this information to conduct word-of-mouth(WOM) advertisement,which is an important marketing *** Review provides detailed information about WOM *** are many ways to search influencers and often they are network centrality *** paper aims to investigate whether each centrality measurement could produce similar results across different social media platforms(***,Twitter,Instagram).The social network data used in this research is from Huawei *** research uses four centrality measurements and three set similarity methods to analysis the *** a result,this paper draws a conclusion about the binary question "Does it provide similar results or not?".Since various companies and applications may have different standards and definitions about being similar,please also check similarity data provided in this paper.

关键词： social network centrality measurements marketing strategy

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Attention-guided CenterNet deep learning approach for lung cancer detection

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Computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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