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Detecting and Classifying Darknet Traffic Using Deep Network Chains

computer systems Science & engineering 2023年第10期47卷 891-902页

作者： Amr Munshi Majid Alotaibi Saud Alotaibi Wesam Al-Sabban Nasser Allheeib Computer Engineering Department Umm Al-Qura UniversityMeccaSaudi Arabia Smart Lab Umm Al-Qura UniversityMeccaSaudi Arabia Information Systems Department Umm Al-Qura UniversityMeccaSaudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhSaudi Arabia

The anonymity of the darknet makes it attractive to secure communication lines from *** analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing ***,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks *** paper presents a two-stage deep network chain for detecting and classifying darknet *** the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is *** the second stage,traffic related to VPNs and Tor services is classified based on their respective *** methodology of this paper was verified on a benchmark dataset containing VPN and Tor *** achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,*** and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after *** the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time ***,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional ***,additional classifiers could be added to the chain to categorize specific activities of interest further.

关键词： Darknet darknet traffic deep network chains Internet traffic

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Graph-Based Local Planning with Spatiotemporal Risk Assessment for Risk-Bounded and Prediction-Aware Autonomous Driving

Graph-Based Local Planning with Spatiotemporal Risk Assessme...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Abdulrahman Ahmad Majid Khonji Ameena Al-Sumaiti Jorge Dias Khaled Elbassioni Department of Computer Science Khalifa University Abu Dhabi UAE Khalifa University Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE Department of Electrical Engineering Smart OR Lab Advanced Power Energy Center (APEC) Khalifa University Abu Dhabi UAE Department of Computer and Information Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Risk-bounded motion planning for autonomous driving in dynamic environments presents significant research challenges. Ensuring continuous navigation towards a destination while making real-time decisions is a nonconvex problem. This paper presents a graph-based local planning method constrained by user-specific driving preference, represented as a risk-bound criterion for motion planning. First, we propose a lattice graph construction method that adheres to the vehicle's curvature constraints. Then, we formulate the trajectory planning problem as an integer-linear programming task, addressed by our novel risk-bounded and prediction-aware constrained shortest path. Our solution accounts for both static and dynamic obstacles in urban settings, adhering to traffic regulations. At the core of our approach is a conservative spatiotemporal risk assessment mechanism, which evaluates collisions considering the uncertain delay from speed control of the ego vehicle and predicted trajectories of dynamic obstacles. We implemented our solution using the CARLA simulator and the ROS2 platform, within a comprehensive framework encompassing global planning, local planning, and vehicle control. The effectiveness of our approach is demonstrated through notable collision avoidance, improved path-tracking, and enhanced risk-bounded planning capabilities.

关键词： Trajectory planning Dynamics Velocity control Planning Spatiotemporal phenomena Trajectory Risk management Vehicle dynamics Collision avoidance Autonomous vehicles

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Semantic-Aware Pseudo-labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期PP卷 PP页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic- like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. Firstly, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic- like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. The source code will be made publicly available. ©1979-2012 IEEE.

关键词： Latent semantic analysis

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HealthEdge: A Machine Learning-Based smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jing Bi Ziqi Wang Haitao Yuan Jinhong Yang Jia Zhang College of Computer Beijing University of Technology Beijing China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

关键词： Accuracy Processor scheduling Computational modeling Education Autoencoders Collaboration Search problems Space exploration Particle swarm optimization Optimization

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Routing Heterogeneous Traffic in Delay-Tolerant Satellite Networks

arXiv

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arXiv 2023年

作者： Madoery, Pablo G. Kurt, Güneş Karabulut Yanikomeroglu, Halim Hu, Peng Ahmed, Khaled Martel, Stéphane Lamontagne, Guillaume Lab Department of Systems and Computer Engineering Carleton University Ottawa Canada The Poly-Grames Research Center Department of Electrical Engineering Polytechnique Montréal Montréal Canada The Digital Technologies Research Center National Research Council Canada Canada Satellite Systems MDA Canada

Delay-tolerant networking (DTN) offers a novel architecture that can be used to enhance store-carry-forward routing in satellite networks. Since these networks can take advantage of scheduled contact plans, distributed algorithms like the Contact Graph Routing (CGR) can be utilized to optimize data delivery performance. However, despite the numerous improvements made to CGR, there is a lack of proposals to prioritize traffic with distinct quality of service (QoS) requirements. This study presents adaptations to CGR to improve QoS-compliant delivery ratio when transmitting traffic with different latency constraints, along with an integer linear programming optimization model that serves as a performance upper bound. The extensive results obtained by simulating different scenarios show that the proposed algorithms can effectively improve the delivery ratio and energy efficiency while meeting latency constraints. © 2023, CC BY.

关键词： Delay tolerant networks

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Performance Evaluation of WLAN Access Points Selection Metrics for Fingerprinting based Localization

Performance Evaluation of WLAN Access Points Selection Metri...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Sohaib Bin Altaf Khattak Moustafa M. Nasralla Maged Abdullah Esmail Mohamed Marey Nikumani Choudhury Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Kingdom of Saudi Arabia Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Hyderabad India

abstract Reliability and cost are essential elements to consider in all engineering network designs. In RF-localization systems, reliability can be defined as seamless coverage with precise and accurate localization. Cost-efficiency aims to reduce the infrastructure while simultaneously maintaining high accuracy. Both the cost and reliability can be associated with Access Points (APs) deployment. Therefore, it is paramount to study how to optimize the APs placement in RF-localization systems. To select the optimal AP configuration, different selection metrics are proposed. This paper investigates different AP placement and optimization strategies for WLAN fingerprinting indoor localization systems. Performance of selection metrics are evaluated experimentally in a realistic indoor environment. A fingerprinting database is developed by a grid spacing of 2m using 7 WLAN APs, by collecting Received Signal Strength (RSS) values using an Android smartphone app. The experimental results show, the AP configuration obtained by the metric combining the fingerprint difference metric with the geometric dilution of precision, results in high localization accuracy.

关键词： Location awareness Performance evaluation Wireless LAN Costs Fingerprint recognition Reliability engineering Minimization

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Exploring Accelerometer Sensors for Optimized smart Shoe-Based Fall Detection

Exploring Accelerometer Sensors for Optimized Smart Shoe-Bas...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Md Shohanoor Rahman Ali Nikoukar Mesut Güneş Behnam Dezfouli Communication and Networked Systems (ComSys) - Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Department of Computer Engineering Internet of Things Research Lab Santa Clara University USA

The need of immediate fall detection, particularly within the geriatric population, is emphasized by its pivotal role in curtailing consequences post-incident. In this paper we introduce an algorithm that harnesses the subject’s acceleration, angular velocity, and orientation (pitch and roll) to ascertain fall incidents. The evaluation is based on systematic extraction and comparison of features delineated over varied temporal intervals. The presented algorithm uses data extracted from a singular sensor, amalgamating a triaxial accelerometer and a gyroscope, embedded in footwear possessing integrated computational modules. The resilience of the algorithm and efficacy are empirically validated through rigorous assessments, involving five healthy participants simulating 160 fall events and 650 activities indicative of routine locomotion.

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Effects of Air Exposure on the Interfacial Capacitance of Laser-Induced Graphene (LIG) with and without Silver (Ag) Nanoparticle Loading

Effects of Air Exposure on the Interfacial Capacitance of La...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Goh, Rou Xuan Sultan, Suhana Mohamed Pu, Suan Hui Leow, Pei Ling Lee, Hing Wah Fishlock, Sam J. Chong, Harold M. H. Ling, Ting Yang University of Southampton Malaysia Smart Manufacturing and Systems Research Group Johor Iskandar Puteri79100 Malaysia Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Skudai81310 Malaysia MIMOS Berhad Technology Park Malaysia Centre for Semiconductor & Thin Film Research Kuala Lumpur57000 Malaysia TEDI-London The Engineering & Design Institute LondonSE16 7LG United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9798350333312

research on laser-induced graphene (LIG) and LIG with silver nanoparticles loading (Ag-LIG) are increasing by the day as more of its potential are uncovered each day. With the LIG fabrication looking to increase further in the future, it will be useful to confirm and verify any changes in the quality of LIG when it is exposed to the ambient air. In normal laboratory conditions, researchers store samples in petri dishes or any other covered storage, but inevitably the air within the storage will still be in contact with the samples. In this study, we left LIG and Ag-LIG samples in a lidded petri dish after its fabrication and measured the interfacial capacitance of each sample using a standard three-electrode electrochemical setup with a potential range from 0 V to 0.4 V on different days. Results show that the interfacial capacitance of both LIG and Ag-LIG drops as it is left in the storage container longer after its fabrication, with Ag-LIG showing a greater decrease after 10 days compared to LIG. This indicates that the silver nanoparticles played an important part during the oxidation process, making Ag-LIG more sensitive to its environment during prolonged unuse. The findings from this study shows the potential usage of LIG and Ag-LIG as a low-cost single-use indicator for air exposure. © 2023 IEEE.

关键词： Graphene

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Estimating Complexity for Perception-based ADAS in Unstructured Road Environments

Estimating Complexity for Perception-based ADAS in Unstructu...

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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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