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共 309 条记录，以下是41-50 订阅

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Cryptographic Ledger for Cyber security in Smart Grid

Cryptographic Ledger for Cyber Security in Smart Grid

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international conference on Edge Computing and Applications (ICECAA)

作者： Elanchezhiyan E K. Kalaiselvi D. Banumathy Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu

In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters.

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An Integrated Rule-Based and Machine Learning Technique for Efficient DoS Attack Detection in WSN

An Integrated Rule-Based and Machine Learning Technique for ...

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Disruptive Technologies (ICDT), international conference on

作者： Shalini Swami Pushpa Singh Sansar Singh Chauhan Department of Computer Science GL Bajaj Institute of Technology and Management Greater Noida UP

Wireless Sensor networks (WSNs) are highly distributed, lightweight nodes, deployed in large numbers to monitor systems. Because of the nature of wireless, there are various challenges for security and threats. An effective anomaly detection system is required to find out these attacks, especially Denial of Service (DoS) attacks. These attacks are the major threat to any system connected to the internet. To identify the DoS attacks in WSN, Machine Learning (ML) algorithms helps to increase the security measures and prevent attacks; however, makes the system computationally slower. An integrated rule-based and ML technique is proposed to quickly identify the DoS attacks. Rule-based technique is used to differentiate between normal flow and attack, particularly DoS attacks. If any flow is an ‘attack’ then ML models are used to detect types of DoS attack patterns such as TDMA, Blackhole, Grayhole, and Flooding. We have used a decision tree (DT) and support vector machine (SVM) classifier with WSN-DS dataset to train the proposed model. Analysis of the classifiers shows that DT outperforms with a high accuracy to detect the types of DoS attack. The proposed method is fast and efficient, since it avoids the use of ML model if flow is ‘normal’.

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Depth Analysis on Recognition and Prevention of DDoS Attacks via Machine Learning and Deep Learning Strategies in SDN

Depth Analysis on Recognition and Prevention of DDoS Attacks...

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Self Sustainable Artificial Intelligence systems (ICSSAS), international conference on

作者： Pranay Yadav Nishchol Mishra Sanjeev Sharma School of Information Technology (SoIT) Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal M.P INDIA

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Recent years have witnessed the exponential growth of global digitization. Similarly, the rise in distributed Denial of Service (DDoS) attacks continues unabated. DDoS is a significant threat to network security in the flexible environments of software-defined networks (SDN). This research discusses the study of the advanced characteristics of machine learning and deep learning technologies to detect and prevent DDoS attacks in SDNs. In this article, we closely examine the design of SDNs and the unique challenges they face during DDoS attacks. This research study analyzes various deep learning and machine learning models designed to respond to complex DDoS attack patterns and respond instantly to attacks. Different datasets related to attacks in SDN and IoT are discussed in the research article. Additionally, a comparative study of these methods are presented.

关键词： Deep learning Surveys Adaptation models Technological innovation Scalability Switches Denial-of-service attack Data models computer crime Software defined networking

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The Role of IoT in Enhancing Wireless Sensor Network Applications: A Comprehensive Analysis

The Role of IoT in Enhancing Wireless Sensor Network Applica...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), international conference on

作者： Sonia Singh Neha Gupta School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

WSNs are a system of spatially distributed sensors that sense some environmental phenomena and communicate the information without wires to a central system. Now, integration with the IoT further strengthens WSNs in aspects such as real-time data collection, analysis, and decision making across diverse network applications, fueling innovation in smart environments. Wireless Sensor networks (WSNs) are normally groups of sensors that gather information about their surroundings and transmit it to the main system without the use of wires. The role of the Internet of Things in WSN is to provide an avenue through which the data from the sensors can be shared in real-time, hence making smart systems efficient and responsive This paper present a qualitative research paradigm where systematic literature survey has been conducted on many articles in which IoT based WSN articles published in the last five years along with a qualitative analysis of performance indicators of 20 real-world IoT-enhanced WSNs. The findings reveal that IoT integration significantly improves WSN capabilities in three key areas: It has achieved (1) network longevity, which is a 40% increase in overall sensor lifespan; (2) data accuracy, with the observation that it has cut down on false positives by 25%; and (3) real-time responsiveness, which has proven to have truncated the delay in data transmission by 60%. Nevertheless, we also pinpoint the threats in the security and privacy domains, where 75% of investigated systems are vulnerable to cyber threats. The researchers establish that IoT greatly accelerates the WSN applications in smart city and environment monitoring, but this comes with the need to enforce better security and standardized procedures for optimal use of IoT in achieving the WSN application goals. This research therefore offers a systematic map for researchers and practitioners to foster the integration of IoT in WSN applications towards intelligent and reliable sensor networks in the

关键词： Wireless sensor networks Systematics Wires Smart systems Real-time systems Sensor systems Sensors security Internet of Things Intelligent sensors

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Analyzing the Attacks on Blockchain Technologies 2nd

Analyzing the Attacks on Blockchain Technologies

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2nd international conference on Machine Learning, Advances in Computing,Renewable Energy and communication, MARC 2020

作者： Vats, Vinay Kumar Katarya, Rahul Department of Computer Science & Engineering Delhi Technological University Shahbad Daulatpur Delhi110042 India

ISBN: (纸本)9789811623530

Blockchain technology has gained significant interest because of a wide variety of possible uses. It was first developed as a Bitcoin cryptocurrency but has since been used in many other business and non-business applications. There are various innovations available from Bitcoin to financial innovations, risk management, IoT, and public and social services. The blockchain infrastructure, instead of most current systems based on center-based architectures, combines peer-to-peer networks and distributed platforms that use blockchain registers to store transactions. But, like the Internet in 1990, the blockchain is regarded as still growing. In the future, it can alter so many aspects of technology. However, there are many setbacks, mainly due to the defense area, as a new undeveloped sector. So, it becomes important to analyze their success in multiple use cases and scenarios as more and more diverse blockchain technologies have appeared. We address the security concerns of blockchain technology in this survey. In this paper, with a focus on shared blockchains, we systematically analyze the attack surface of blockchain technologies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Intelligent network traffic analysis: leveraging machine learning for enhanced cybersecurity

Intelligent network traffic analysis: leveraging machine lea...

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2nd international conference on Pervasive Computing Advances and Applications (PerCAA 2024)

作者： Seema Kaloria Rakesh Kumar Saxena Deshraj Bairwa Faculty of Computer Science & Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9781837243006

Network traffic analysis is one of the necessary processes through which there can be monitoring, securing, and optimizing network operations. It is hard for this approach to notice some unknown or evolving kinds of security threats-mostly those involving zero-day attacks or distributed Denial-of-Service-because cyber threats are developing so fast that nowadays even the most state-of-the-art signature-based methods of traffic analysis are not considered sufficient. This paper presents the design and development of the NTA tool based on ML techniques that will solve the problem where conventional approaches fail to have any integration with unsupervised learning techniques toward the detection of anomalies and classification of network traffic. Utilizing K-means clustering, the tool classifies the network data in an unsupervised manner into normal and malicious traffic clusters. This had been trained on a data set with over 400,000 rows of network traffic data features through IP address and port numbers and timestamps. An extensive pipeline of data preprocessing had been set to clean and normalize the data thus providing quality input for the applied machine learning models. Real-time network anomalies and potential security breaches will be detected by the tool, which empowers network administrators to take necessary steps for improving security protocols. The results show the usability of the developed tool and effectiveness toward the detection of known threats and also unknown ones like DDoS attacks. The application of the K-means clustering algorithm in the constructed system showed efficient grouping of similar network patterns to acquire anomalies in high precision. Also, detection accuracy in this proposed tool is in comparison with traditional systems of NTA and is proved to be more reliable than the proposed methods and relatively low false-positive rates are associated. Scalability tests proved robust performance while dealing with large - scale networks

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1D CNN Based Model for Detection of DDoS Attack

1D CNN Based Model for Detection of DDoS Attack

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Padmavathy C Mahalakshmi A Vadivel R Rajat Kumawat Ritam Dutta Bhaskar Roy Nilanjan Dutta Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Computer Science and Engineering B.N.M Institute of Technology Bangalore India Computer Science and Engineering Poornima University Jaipur India Artificial Intelligence & Machine Learning Asansol Engineering College Asansol India Electronics & Comm. Engineering Asansol Engineering College Asansol India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

As the technology behind informatics systems has improved, the Internet of Things (IoT) has grown in significance and application. IoT applications are becoming more popular as a result of the expansion of IoT enablers including smart watches, smart phones, security cameras, and smart sensors. Unfortunately, other issues have raised due to the unsecured nature of IoT devices. distributed Denial of Service (DDoS) assaults are one of the deadliest cyber-attacks as an example. The untrustworthiness of IoT systems causes a number of security issues. Protecting IoT networks from dispersed denial of service attacks has been the focus of several recent research and development efforts. The primary challenge in this area of study is creating a model that is responsive to different types of DDoS assaults while also being able to distinguish between malicious and benign traffic to prevent false positives. In this study, a Convolutional Neural Network with only one dimension is used. The suggested system is employed in both statistics and cognitive science, and it is based on the central nervous system in particular. They are modelled as mathematical functions that are structured to depict complicated interactions between inputs and outputs (dependent variables), and they are exemplified by the connectivity of brain systems from a variety of input variables to the output (dependent variables) We foresee three types of assaults in our article. All models are tested with precision, recall and F-score analysis. SVM achieved nearly 86% of precision, 95.15% of recall and 90.43% of F-score, where the projected model achieved 92.53% of precision, 97.17% of recall and 91.59% of F-score.

关键词： Support vector machines Computational modeling Brain modeling Mathematical models Internet of Things security Convolutional neural networks

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A Comprehensive Study on Blockchain: Transforming the World

A Comprehensive Study on Blockchain: Transforming the World

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Innovation in Technology (INOCON), IEEE international conference for

作者： Sujata Negi Thakur Sudhanshu Maurya Bhupesh Rawat Department of Computer Science & Engineering Graphic Era Hill University Haldwani Campus Nainital India School of Computing Graphic Era Hill University Bhimtal Campus Nainital India

We are going towards digitization, and while discussing it, “money” is the most common word that comes to everyone’s mind. We have “Bitcoins” to support this. The Blockchain, a shared ledger system, allows for the trading of Bitcoin and other types of digital money. In essence, bitcoins are long sequences of code that have been transformed into currency. Digital money that is created and held electronically is called bitcoin. Bitcoin transfers employ a shared ledger system called a blockchain. It is also being used in a number of other fields, including the government, the healthcare industry, and e-voting systems. These days, people are incredibly concerned about transaction security. Due to its comprehensive security characteristics, blockchain networks are increasingly being used. Throughout the world, people are embracing the blockchain network since it has complete security features. In our work, we have also covered some of the unique features of blockchain besides security. Every invention, including blockchains, must overcome numerous difficulties, as we all very well know. Some of the obstacles that the adoption of blockchain technology faces have been briefly discussed. We have covered the fundamentals of current blockchain technology, as well as its applications, features, and difficulties.

关键词： Technological innovation distributed ledger Government Bitcoin Medical services Regulation Blockchains

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Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in Cloud Computing

Seagull-optimized Deep Convolutional Neural Network for Dete...

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Self Sustainable Artificial Intelligence systems (ICSSAS), international conference on

作者： Yogesh B. Sanap Pushpalata G. Aher Computer Science and Engineering Sandip University Nashik Sandip University Nashik Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model’s performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier’s parameters and improves the model’s performance in attack detection with fast convergence, which reduces the model’s time complexity. The experimental outcomes indicate that the developed $\mathrm{SgO}-\mathrm{SCNN}$ model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%.

关键词： Cloud computing Accuracy Computational modeling Denial-of-service attack Data models Convolutional neural networks computer crime Time complexity Optimization Overfitting

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DDoS Attack Intrusion Detection System with CNN and LSTM Hybridization

DDoS Attack Intrusion Detection System with CNN and LSTM Hyb...

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Sustainable Computing and Smart systems (ICSCSS), international conference on

作者： V Poornachander Konga Sathish Kumar Sripelli Jagadish Department of Computer science and Applications Government Degree College (Autonomous) Narsampet Warangal Telangana Department of Computer Science & Engineering Chaitanya (Deemed to be) University Hanamkonda Telangana Dept of CS & AI SR University Ananthasagar Telangana

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

distributed Denial of service (DDoS) threats are among the most harmful events to network security. DDoS attacks are considered among the most common types of network attacks. When servers get attacked by these attacks., users experience issues when they try to access those servers for assistance. This caused the need for an effective means of identifying DDoS attacks., which is essential. Research has established that deep learning and machine learning are valuable methods for detecting DDoS attacks. The model has been verified employing the NSLKDD dataset. The seven levels of this type of development enable enhanced performance compared to LSTM and standard CNN. Modules for recognizing and handling defects have been included in this system. Convolutional Neural Network (CNN) and Long Short-Term Memory (CNN-LSTM) techniques are implemented in the anomaly detection model to recognize irregular patterns in traffic effectively. The proposed model utilizes flow rule directions from the controller to detect irregular traffic. It employs IP tracing to connect back to the attacker.

关键词： Training Accuracy Denial-of-service attack Prediction algorithms User experience Convolutional neural networks Servers

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