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Enhancing SDN Traffic Analysis Through Machine Learning on Preprocessed Controller Flow Statistics and Packet Analysis Data

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SN computer science 2025年第5期6卷 1-22页

作者： Vadivu, D. Sendil Santhosh, Ashwin Rajagopalan, Narendran Pavagada, Kaustub Valsaraj, Aswin Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

As an emerging technology, Software Defined Networks (SDN) has led to several vulnerabilities and risks, making it adoption challenging. Cyber threats in SDN include a wide range of malicious activities intended to exploit the architecture's centralized control and programming ability vulnerabilities. Adequate safety precautions for maintaining the stability of networks are required. Establishing an Intrusion Detection System (IDS) is vital in solving such issues, enabling real-time monitoring and classification of suspicious activity. Very few SDN-based datasets are available for attack detection. Hence, this study aims to generate two SDN-specific datasets, CSOR_SDN (Controller Statistics with Oftcl_Rest application for SDN) and WSCF_SDN (Wireshark Statistics with CicFlowmeter for SDN), from the network environment emulated using the Mininet and RYU controller by utilizing controller statistics and packet analysis data. The dataset generated with normal, DDoS (Distributed Denial of Service), DoS (Denial of Service), and probe attack flows is evaluated with multiple feature selection methods and Machine Learning (ML) classifiers to assess the complexity of the dataset. Correlation Coefficient-based feature selection (CFS) of filter methods is selected out of others with a Decision Tree (DT) classifier, capable of producing 100% results for the CSOR_SDN dataset with the least execution time in attack detection. For the proposed IDS, tenfold stratified cross-validation is performed with confidence interval assessment for the test dataset created on an enterprise network use case to highlight the scalability support of the model. A comparative analysis of both the dataset and its resource utilization is conducted to evaluate its feasibility. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber threat Dataset OpenFlow Ryu SDN Security Traffic analysis

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SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

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computer Systems science & engineering 2023年第3期44卷 2439-2456页

作者： C.P.Thamil Selvi R.PushpaLaksmi Department of Computer science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and Technology PSNA College of Engineering and TechnologyDindigulTamilnadu624622India

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

关键词： Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification

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ONLINE PRIMAL DUAL MEETS ONLINE MATCHING WITH STOCHASTIC REWARDS: CONFIGURATION LP TO THE RESCUE

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SIAM Journal on Computing 2024年第5期53卷 1217-1256页

作者： Huang, Zhiyi Zhang, Qiankun Department of Computer Science The University of Hong Kong Hong Kong School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds with the probability of the corresponding edge. It is one of the few online matching problems that have defied the randomized online primal dual framework by Devanur, Jain, and Kleinberg (SODA 2013, SIAM, Philadelphia, 2013, pp. 101-107) thus far. This paper unlocks the power of randomized online primal dual in online matching with stochastic rewards by employing the configuration linear program rather than the standard matching linear program used in previous works. Our main result is a 0.572 competitive algorithm for the case of vanishing and unequal probabilities, improving the best previous bound of 0.534 by Mehta, Waggoner, and Zadimoghaddam (SODA 2015, SIAM, Philadelphia, 2015, pp. 1388-1404) and, in fact, is even better than the best previous bound of 0.567 by Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) for the more restricted case of vanishing and equal probabilities. For vanishing and equal probabilities, we get a better competitive ratio of 0.576. Our results further generalize to the vertex-weighted case due to the intrinsic robustness of the randomized online primal dual analysis. © 2024 Society for Industrial and Applied Mathematics.

关键词： Consensus algorithm

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Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

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computers, Materials & Continua 2024年第3期78卷 3867-3889页

作者： Dahyeon Kim Namgi Kim Junho Ahn Computer Information Technology Korea National University of TransportationChungju27469Korea Department of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world *** existing analysis of software security vulnerabilities often focuses on specific features or *** partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the *** key novelty lies in overcoming the constraints of partial *** proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security *** guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each *** guidelines are not only practical but also applicable in real-world software,allowing for prioritized security *** proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related *** analysis resulted in the identification of a total of 121 *** successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

关键词： Framework automation vulnerability analysis security guidelines

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

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Materials Today: Proceedings 2023年 72卷 958-965页

作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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SLOG: smartphone-based semantic lifelogging framework for digital prosthetic memory development

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Personal and Ubiquitous Computing 2023年第6期27卷 2071-2102页

作者： Ali, Shaukat Khusro, Shah Khan, Akif Khan, Inayat Alam, Iftikhar Solehria, Salman Faiz Department of Computer Science University of Peshawar Peshawar25120 Pakistan Department of Computer Science Sarhad University of Information Technology Peshawar25000 Pakistan Department of Computer Science University of Engineering and Technology Mardan Mardan23200 Pakistan

The advancements in sensors, processing, storage and networking technologies have turned smartphone into de facto lifelogging device. Realizing the lifelogging potential of smartphone, researchers have postulated with smartphone-based lifelogging systems with varying capabilities of capturing and storing information about peoples’ daily life activities, behaviors, interactions, locations, environments, etc., into their lifelog archives. However, the systems are lacking with several capturing, storing, retrieving and design issues, importantly not organizing the lifelog information with the same semantics as they exist in the real world and encoded in human episodic memory. The semantic organization of lifelog information will develop a true digital prosthetic memory to solve problems in different real-world domains including memory augmentation. In this paper, we have presented the architecture and implementation of Semantic LifelOGging (SLOG) framework for smartphone-based semantic lifelogging using Semantic Web technologies. The SLOG framework incorporates smartphone sensors and processing capabilities, and Semantic Web technologies to develop a semantically enriched digital prosthetic memory on smartphone. The semantic lifelog data model is developed by extending SmartOntoSensor ontology with relevant concepts and properties to represent, store and relate lifelog and contextual information. The semantic lifelog data model will provide contextual semantics to organize, annotate and relate lifelog information like human episodic memory and provide enough contextual cues to recall lifelog information like associative recall in human memory. The proof-of-concept applications are developed to show practicality of the SLOG framework. The quantitative and empirical evaluations have shown the SLOG framework and system has improved features and is acceptable in usability and effectiveness. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part

关键词： Smartphones

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Enhancing Malware Detection: A Comparative Analysis of Ensemble Learning Approaches 1

Enhancing Malware Detection: A Comparative Analysis of Ensem...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Ajvad Haneef, K. Shashank, N.S. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392111

Malware represents the greatest threat to cybersecurity and serves as the weapon of choice for carrying out nefarious activities in the digital realm. The proliferation of malware poses significant risks to individuals and organizations. Therefore, malware detection is essential for companies to strengthen their cyberspace, protect sensitive data and minimize financial losses from cyberattacks. Ensemble learning methods have emerged as powerful tools in this domain, leveraging the strengths of multiple models to enhance detection accuracy and reliability. We present a comparative analysis of ensemble learning approaches for malware detection using the EMBER dataset. We evaluate six ensemble models, including Random Forest, AdaBoost, Extra Trees, XGBoost, LightGBM, and Bagging, based on key performance metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that all ensemble models demonstrate strong performance in malware detection, with Bagging ensemble achieving the highest accuracy and overall performance. The results highlight the effectiveness of ensemble learning techniques in enhancing malware detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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Demonstration of high-performance STO-based WORM devices controlled by oxygen-vacancies and metal filament growth

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Journal of Materials Chemistry C 2024年第23期12卷 8418-8425页

作者： Hsu, Chih-Chieh Wen, Xiu-Ming Xiao, Kai-Zer Jhang, Wun-Ciang Joodaki, Mojtaba Department of Electronic Engineering National Yunlin University of Science and Technology Douliu64002 Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan School of Computer Science and Engineering Constructor University Bremen28759 Germany

Herein, we propose high-performance Ti/STO/n+-Si and Ag/STO/n+-Si write-once-read-many-times devices, where the resistance transition mechanisms are controlled by oxygen vacancies in the STO layer and metal atoms from the electrochemically active Ag electrode, respectively. The as-deposited STO films have a low oxygen-vacancy content of 12.2%. When the Ti electrode is deposited, the formation of a thin TiOx interfacial layer at the Ti/STO interface is achieved, which leads to an increase of oxygen vacancy content in the STO layer to 28.6%. The relatively higher oxygen vacancy density means that the Ti/STO/n+-Si device can provide a voltage-polarity-independent memory function. In contrast, the resistance transition of the Ag/STO/n+-Si device can be limited in the positive voltage region because Ag cations can only be driven into the STO layer using a positive bias. Both devices show high ILRS/IHRS ratios, superior data retention capability, and fast transition speeds. © 2024 The Royal Society of Chemistry.

关键词： Oxygen vacancies

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A Novel Deep Reinforcement Learning Approach for Stability-Based Parallel Disassembly Sequence Planning Problem

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SN computer science 2025年第3期6卷 1-19页

作者： Kumar, M. R. Mahesh Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Parallel Disassembly Sequence Planning (DSP) deals with obtaining the order of disassembling the product with multiple parts disassembling simultaneously. Existing studies derive the product’s disassembly order based on the requirement for liaison data. However, the absence of stability data does not ensure that all the product parts are disassembled without damage. Since the order of retrieval of the product can also be obtained from stability data, this reduces the requirement of an initial precedence graph based on liaison data. The complexity of the problem increases if we adopt different representations for precedence and stability conditions of the product for constraint checking throughout the disassembly process. This paper constructs a stability-based precedence graph based on only the product’s stability data. The developed graph represents the precedence of the stable parts for disassembly, and the same is utilized to formulate the problem as a model-free deep Reinforcement Learning (RL) problem. A novel Exploration–Exploitation (EE) strategy-based Deep Q-Learning (DQL) algorithm is proposed to parallel DSP minimizing direction changes. Our effective reward mechanism accelerates the learning process for all the conditions and objectives of the problem considered. Based on past performance, the proposed algorithm dynamically decides the exploration and exploitation strategy to avoid local optima. We applied the proposed algorithm on five different-scale products and compared the results with uniform-sampling-based model-free deep RL algorithms. It showed significant improvements, particularly in cumulative rewards and disassembling all the parts for large-scale products with early convergence. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Q-learning Deep Q-Networks Disassembly sequence planning Parallel disassembly Precedence graph Stability graph

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Segmentation of Skin Lesion Image Using FCM with Color Transformation Saliency 1

Segmentation of Skin Lesion Image Using FCM with Color Trans...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Bavya, S. Saravana Kumar, V. Kavitha, M. Anantha SivaPrakasam, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science & Engineering Chennai India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India S A Engineering College Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science & Technology Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

In the majority of Western nations including America, Australia and Europe, skin cancer is badly-behaved. Skin concealing, inadequacy of Sun-lights, climate, age, and inherited are major risk factors. Early identification of skin cancer and proceed the treatment can avoid death. The abnormal skin region determines where the skin diseases had been found. Various sorts of skin ailments be existent currently. Melanoma is the sort of skin threat which has grows the greater part of mortality rate. The timely identification and intercession of melanoma include myriad changes of fix. Melanoma has been recognized by shape, size, concealing and outward of the skin injury. This paper demonstration about the traditional clustering technique in particular Fuzzy Clustering technique is acted in these skin lesion pictures. This strategy is segmenting this melanoma from these dermoscopic images effectively. The presentation of this strategy is assessed as far as pixel grouped and time complexity. © 2024 IEEE.

关键词： Feature extraction

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