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Detection of A Novel Dual Attack in Named Data networking

Detection of A Novel Dual Attack in Named Data Networking

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Liang Liu Silin Peng College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

distributed Denial of Service (DDoS) attacks in Named Data networking (NDN), such as Cache Pollution Attacks (CPA) and Improved Collusive Interest Flooding Attacks (I-CIFA), can significantly threaten the NDN network. However, most of the previous research has focused on the detection of a single attack by using Machine Learning algorithms and threshold-based methods, so the previous methods are not efficient enough for detecting dual attacks. This paper first proposes a dual attack by combining I-CIFA and CPA, and extracts the network traffic features, including the number of CacheMisses and the number of PIT entries. After analyzing the severe impact of the dual attack, a detection scheme BO-CatBoost is proposed based on Bayesian optimization and CatBoost. Finally, the experiment results show that the proposed detection scheme is robust in detecting the dual attack.

关键词： Pollution Machine learning algorithms distributed databases Telecommunication traffic Feature extraction Denial-of-service attack Bayes methods

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

引用

distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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Curse of Feature Selection: a Comparison Experiment of DDoS Detection Using Classification Techniques

Curse of Feature Selection: a Comparison Experiment of DDoS ...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Wenjia Wang Seyed Masoud Sadjadi Naphtali Rishe Knight Foundation School of Computing and Information Sciences Florida International University Miami USA

distributed denial-of-service (DDoS) attack is a malicious cybersecurity attack that has become a global threat. Machine learning (ML) as an advanced technology has been proven to be an effective way against DDoS attacks. Feature selection is a crucial step in ML, and researchers have put endless efforts to mitigate the “Curse of Dimensionality”. Feature selection is also causing problems to ML models, such as a decrease in prediction accuracy. Four supervised classification techniques, namely, Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF), are tested using mutual information score ranking to study the necessity of feature selection in DDoS detection.

关键词： Training Radio frequency Measurement Predictive models Feature extraction Denial-of-service attack Decision trees

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Pallas: Optimizing Userspace TCP Stack for Short-Lived Connections

Pallas: Optimizing Userspace TCP Stack for Short-Lived Conne...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Haiqiang Lin Wenxin Li Wenyu Qu Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University China

Short-lived TCP connections are important for modern Internet applications and call for efficient network stack support. Traditional Linux TCP stack is inefficient because of the inherent kernel overheads like context switching and memory copy. A promising alternative, called user-level TCP stack, is thus becoming attractive in this context. However, existing user-level stacks mainly focus on scalability issues when handling shortlived connections and fall short in optimizing request-response latency. To fill this gap, this paper presents Pallas, a built-in packet scheduler in the user-space mTCP stack, to prioritize the transmissions of short connections over long connections to reduce the request-response latency. Pallas schedules at packetlevel first to complete 1-packet short connections as quickly as possible and then smoothly switches to connection-level to defer the transmissions of long connections. We have implemented a Pallas prototype and evaluated it on a 4-core machine. The results show that Pallas-enhanced mTCP delivers significant performance. For example, it speeds up the overall request response time of mTCP by 1.49 times.

关键词： Schedules Scheduling algorithms Scalability Linux Prototypes Switches Scheduling

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Optimizing distributed Denial of Service (DDoS) Attack Detection Techniques on Software Defined Network (SDN) Using Feature Selection

Optimizing Distributed Denial of Service (DDoS) Attack Detec...

引用

Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Inamul Haq Salman A. Khan Nazeeruddin Mohammad Ali Zaman College of Computing and Info. Sciences Karachi Institute of Economics and Technology Karachi Pakistan Cyber security center Prince Mohammad bin Fahd University Al-Khobar Saudi Arabia Computer Science Department Shaheed Zulfiqar Ali Bhutto University Karachi Pakistan

ISBN: (数字)9798331504342

ISBN: (纸本)9798331504359

distributed denial of service (DDoS) attacks refer to a category of cyber attacks in which an attacker attempts to overwhelm a resource, such as a server, destination, or bandwidth through various means. This is usually done by either flooding the traffic or sending a heavy crunching job to exhaust the CPU or heavy amounts of data to pileup the storage. By the rise of virtualization and cloud technologies, networks have also evolved from hardware to a piece of software which virtualizes the operation of a network equipment. This virtualization is termed as a Software Defined Network (SDN). Due to the sheer impact of cyberattacks on the performance of networks, the study of DDoS attacks on SDN is vital in the present era. Contemporary approaches such as machine learning and deep learning have proven to be effective in attack detection of DDoS in SDNs. In this paper, a preliminary analysis is carried out to study the impact of feature selection to increase the throughput and response time of Machine learning techniques in detection of DDoS attacks on SDN. The results indicate that feature selection, when applied to machine learning algorithms, is an effective approach for DDoS attack detection in SDNs.

关键词： Technological innovation Machine learning algorithms Feature extraction Denial-of-service attack Throughput Software Time factors Servers Software defined networking Virtualization

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Approaching Solutions to Blockchain Security Trilemma and Consensus Mechanisms

Approaching Solutions to Blockchain Security Trilemma and Co...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Dipanshu Parashar Madhav Sharma Vishnu Sharma Prama Nand CSE Department GCET Greater Noida School of Engg. &Tech. Sharda University Greater Noida India

Blockchains have been referred to as revolutions that will enable the processing of data and transactions to move from a centralized party to a high-trust decentralized network of different verifying nodes. This paper represents multiple security factors involved in a successful blockchain and reviews blockchain scalability trilemma of decentralization, security, and speed in consideration with multiple consensus mechanisms with emphasis on performance and security. Further, the paper attempts to introduce Proof of Parity as a better way to run blockchain networks in multiple real-time scenarios. This also sheds light on security concerns in Hyperledger fabric or permissioned blockchains.

关键词： Privacy distributed ledger Scalability Smart contracts Proof of Work Fabrics Real-time systems

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Utilization of Augmented Reality Technology in Campus Promotion Using the Marker Based Tracking Method

Utilization of Augmented Reality Technology in Campus Promot...

引用

international conference on Informatics and computing (ICIC)

作者： Heny Pratiwi Muhammad Ibnu Sa’Ad Oktadius Giantoro STMIK Widya Cipta Dharma Information System Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study aims to develop an Augmented Reality (AR) application as a more attractive and interactive promotional media for the STMIK Widya Cipta Dharma campus. By utilizing AR technology, it is hoped that information about facilities, study programs, and campus activities can be presented in a more attractive and easily understood way by prospective students and the general public. In addition to using conventional promotional methods such as social media and print media (brochures, posters, and billboards), the application of AR aims to enrich and increase the effectiveness of campus promotions. The methods used in this study include the development of an Android-based AR application with Canva software for interface design, Unity 2022 as the main program, and SketchUp for 3D modeling. The application development process follows a multimedia development model consisting of six stages: Concept, Design, Material Collection, Assembly, Testing, and Distribution. System testing is carried out using the Black Box and Beta Testing methods to ensure the application functions as expected. The results of this study indicate that the Implementation of Augmented Reality as a Promotional Media for the STMIK Widya Cipta Dharma Campus can be successfully completed using Unity 2022. This application is then distributed to the public who need information about STMIK Widya Cipta Dharma.

关键词： System testing Three-dimensional displays Social networking (online) Operating systems Two-dimensional displays Media User interfaces Three-dimensional printing Augmented reality Assembly

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Implementation of Hyperparameter Tuning Random Forest Algorithm in Machine Learning for SDN Security: An Innovative Exploration of DDoS Attack Detection

Implementation of Hyperparameter Tuning Random Forest Algori...

引用

Artificial Intelligence, Blockchain, Cloud computing, and Data Analytics (ICoABCD), international conference on

作者： Hijrah Nisya Sofia Naning Hertiana Yudha Purwanto School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

Software Defined Network (SDN) represents a novel approach to network programming for the design, construction, and administration of computer networks. This approach entails the separation of the control plane from the data plane, with the objective of centralizing the network and consolidating all settings in the control plane. Network design frequently encounters challenges pertaining to network security and network attacks. SDN offers a number of advantages. One such advantage is that it allows the implementation of control functions at the controller level, thus positioning the controller as a key element of SDN. The centralized nature of SDN renders it susceptible to attacks, including distributed Denial of Service (DDo $S$ ) attack. Consequently, a security system is required to address the issue of attacks directed towards the SDN controller. DDoS attack detection system is created using a machine learning algorithm, namely Random Forest, whose algorithm performance is optimized using Hyperparameter Tuning to achieve high accuracy in the detection process. The InSDN dataset, comprising a total of 56 classes, was employed in this research, and the SelectFromModel feature selection method was utilized to identify crucial features within the InSDN dataset. The method was found to be effective in detecting DDoS attacks, with an accuracy value of 99.99%. The results demonstrate that Hyperparameter Tuning Random Forest is an effective method for detecting DDoS attacks on SDN.

关键词： Machine learning algorithms Accuracy Software algorithms Programming Network security Denial-of-service attack Software defined networking Computer crime Tuning Random forests

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Welcome Message from ICDCN 2022 conference Chairs

ACM International Conference Proceeding Series

引用

ACM international conference Proceeding Series 2022年 III页

作者： Singh, Pushpendra Wattenhofer, Roger Wu, Jie IIIT Delhi India ETH Zurich Switzerland Temple University United States

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From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks

From Federated Learning to Quantum Federated Learning for Sp...

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IEEE conference on Standards for Communications and networking (CSCN)

作者： Vu Khanh Quy Nguyen Minh Quy Tran Thi Hoai Shaba Shaon Md Raihan Uddin Tien Nguyen Dinh C. Nguyen Aryan Kaushik Periklis Chatzimisios Faculty of Information Technology Hung Yen University of Technology and Education Hungyen Vietnam College of Sciences and Engineering University of Tasmania Sandy Bay Hobart TAS Australia Department of Electrical and Computer Engineering University of Alabama in Huntsville USA Department of Electrical and Electronics Engineering Lac Hong University Vietnam Department of Computing & Mathematics Manchester Metropolitan University UK International Hellenic University Thessaloniki Greece University of New Mexico Albuquerque USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of $F L$ and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted.

关键词： 6G mobile communication Training Quantum computing Federated learning Wireless networks Computer architecture Space-air-ground integrated networks Real-time systems Artificial intelligence Standards

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