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Federated Learning in Malware Detection 28

Federated Learning in Malware Detection

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Serpanos, Dimitrios Xenos, Georgios University of Patras Computer Technology Institute and Press DIOPHANTUS Dept. of Electrical and Computer Engineering Patras Greece University of Patras Campus Computer Technology Institute and Press DIOPHANTUS Patras Greece

ISBN: (纸本)9798350339918

Malware detection constitutes a fundamental step in safe and secure computational systems, including industrial systems and the Internet of Things (IoT). Modern malware detection is based on machine learning methods that classify software samples as malware or benign, based on features that are extracted from the samples through static and/or dynamic analysis. State-of-the-art malware detection systems employ Deep Neural networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, organizations also have significant privacy constraints and concerns which limit the data that they share with centralized security providers or other organizations, despite the malware detection accuracy improvements that can be achieved with the aggregated data. In this paper we investigate the effectiveness of federated learning (FL) methods for developing and distributing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their security Operations Centers (SOCs) and train their own local DNN model on their own private data. Exploiting cross-silo FL, we combine these DNNs into a global one which is then distributed to all organizations, achieving the distribution of combined malware detection models using data from multiple sources without sample or feature sharing. We evaluate the approach using the EMBER benchmark dataset and demonstrate that our approach effectively reaches the same accuracy as the non-federated centralized DNN model, which is above 93%. © 2023 IEEE.

关键词： Deep neural networks

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Performance Analysis of DDoS Mitigation in Heterogeneous Environments 2

Performance Analysis of DDoS Mitigation in Heterogeneous Env...

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2nd international conference on Interdisciplinary Cyber Physical systems, ICPS 2022

作者： Verma, Amandeep Saha, Rahul Lovely Professional University School of Computer Science and Engineering India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

computer and Vehicular networks, both are prone to multiple information security breaches because of many reasons like lack of standard protocols for secure communication and authentication. distributed Denial of Service (DDoS) is a threat that disrupts the communication in networks. Detection and prevention of DDoS attacks with accuracy is a necessity to make networks *** this paper, we have experimented two machine learning-based techniques one each for attack detection and attack prevention. These detection & prevention techniques are implemented in different environments including vehicular network environments and computer network environments. Three different datasets connected to heterogeneous environments are adopted for experimentation. The first dataset is the NSL-KDD dataset based on the traffic of the computer network. The second dataset is based on a simulation-based vehicular environment, and the third CIC-DDoS 2019 dataset is a computer network-based dataset. These datasets contain different number of attributes and instances of network traffic. For the purpose of attack detection AdaBoostM1 classification algorithm is used in WEKA and for attack prevention Logit Model is used in STATA. Results show that an accuracy of more than 99.9% is obtained from the simulation-based vehicular dataset. This is the highest accuracy rate among the three datasets and it is obtained within a very short period of time i.e., 0.5 seconds. In the same way, we use a Logit regression-based model to classify packets. This model shows an accuracy of 100%. © 2022 IEEE.

关键词： Machine learning

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Intelligent transportation systems trusted user's security and privacy 2

Intelligent transportation systems trusted user's security a...

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2nd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kaushik, Priyanka Rathore, Saurabh Pratap Singh Sachdeva, Lakshay Poonia, Minakshi Singh, Dipti Bir, Lovish CSE-APEX Chandigarh India Icapsr Management & Research New Delhi India Madhav Institute of Technology & Science Dept. of Eng. Math's & Computing Gwalior India Madhav University Dept. of Management Abu Road India

ISBN: (纸本)9798350360523

Standard encryption cannot be utilized in practical communications due to time and storage limits. Dedicated lane keeping (DRL) is a technique that helps self-driving cars navigate congested roads by keeping them in a specific lane (CAVs). Researchers have developed separate networks for distinct types of mixed traffic to cut down on the time and effort typically spent on instruction and coordination. A deep reinforcement learning technique boosts the efficiency of each part and the entire fleet. There is a common misconception that the blockchain is a secure database for private information. A distributed database system in which nodes are directly connected to consensus mechanisms. To ensure data integrity, block blocks on a blockchain network use cryptography and other computer safeguards (such as smart contracts and time stamps). Because of its decentralized design, data storage facilitates collaboration. Digitally signed data records can also be checked to ensure they are accurate. Using hashes to connect individual blocks protects data against tampering by hackers. There is no need for a centralized authority or third party to verify the ledger's accuracy because everyone can access it anytime. The blockchain allows for a transparent, trustworthy, and auditable system sharing information between entities. Like many other industries, transportation may benefit from the broader implementation of blockchain technology. Based on our findings, a state-run blockchain tailored to the transportation industry was developed and made available to the public. Because of blockchain technology, the car-sharing business model may need to be revised. Blockchain technology utilizes a distributed ledger to record transactions in a way that makes it impossible to alter the underlying data while still allowing for fast access for verification and auditing. © 2024 IEEE.

关键词： Blockchain

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Intrusion Detection System Using Ensemble Machine Learning in Cloud Environment 2nd

Intrusion Detection System Using Ensemble Machine Learning i...

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2nd international conference on Emerging Technologies and Intelligent systems, ICETIS 2022

作者： Manzoor, Saba Ahmad, Musheer Alhadawi, Hussam S. Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India Department of Computer Techniques Engineering Dijlah University College Baghdad10011 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq

ISBN: (纸本)9783031252730

Due to the expeditious evolution of advanced computing innovations, new organizational and operational strategies call for adoption. In the last few years, cloud computing has emerged and evolved as one such example to provide complex IT infrastructures, remote storage, a platform for developers and software according to user agreement. In order to make cloud computing possible which provides a distributed domain to its users, the internet has played a significant role. Irrespective of cloud’s versatility and efficiency, there are vulnerabilities present in the cloud environment which can arise both from within and outside the cloud. To protect clouds from any security risk, the traditional security methods fail to completely guard the networks and devices. As a consequence, building an effective intrusion detection system which can detect intrusions with higher accuracy in cloud environment needs to be prioritized. In this paper, we have suggested performing feature selection for reducing dimensions before applying ensemble method to build a model for detection of intrusions. A tree-based technique has been used to select the most prominent features. The intrusion detection system devised is anomaly-based that employs machine learning algorithms: Bagging algorithm and Random Forest algorithm on the UNSW-NB15 dataset which is more recent and comprehensive. An accuracy of 99.5% was achieved for both the bagging algorithm and Random Forest algorithm by reducing the dimension up to only 11 features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

引用

2nd international conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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A Multistep Ahead Predictive Analysis of Crude Oil 2

A Multistep Ahead Predictive Analysis of Crude Oil

引用

2nd IEEE international conference on Blockchain and distributed systems security, ICBDS 2023

作者： Mohapatra, Srikanta Kumar Singh, Kamakhya Narayan Sarangi, Prakash Kumar Sahu, Premananda Mohanty, Jayashree Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School Of Computer Application KIIT Deemed University Odisha India Hyderabad India School of Computer science and Engineering Lovely professional University Punjab India Chandigarh University Dept.of AIT-CSE Punjab Chandigarh Mohali India

ISBN: (纸本)9798350333763

The population is increasing in a rapid manner and the number of vehicles is increasing in the same ratio. Also, crude oils are very useful towards the energy needs in the industries. As a result, a day will come when we face the issue of feeling the vehicles with the petrol to run the vehicle and to run the industries. So, the solution is to find out the period up to which we can use the crude oil which is currently present in the world. To get a conclusion the most populated countries India and China is taken as reference. For simulation purpose time series based Artificial Neural Network application along with Facebook Prophet is used. Facebook Prophet is used to predict future situations in time series data related to oil production and consumption in two top countries by population, China, and India, over a period of 32 years. NeuralProphet is used for modelling time-series based on neural networks forecasting the near-term oil crisis. The dataset used in this study contains three characteristics, namely population, oil production, and consumption, and is obtained from different government sources. The best part of this model is that it can predict multi step ahead of given current data as required. The method based on NeuralProphet is best suit for the time series prediction for short period or long period. It can be used in multistep ahead prediction methods. It is predicted that by 2050 the scarcity of the petroleum is most. So, the solution is either to reduce the uses of vehicles, control the population or simply go for an alternate fuel like hydrogen could be considered a suitable substitute for oil. This prediction highlights the need for sustainable energy sources to meet the energy demand in the future. In conclusion, this paper presents NeuralProphet approach for forecasting future oil consumption and production trends combining the Facebook Prophet and NeuralProphet method. © 2023 IEEE.

关键词： Time series

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ASBlock:An Agricultural based Supply Chain Management using Blockchain Technology 2

ASBlock:An Agricultural based Supply Chain Management using ...

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2nd international conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Panigrahi, Amrutanshu Pati, Abhilash Dash, Bibhu Sahoo, Ghanashyam Singh, Debabrata Dash, Manoranjan University Odisha India School of Computer and Information Science University of the Cumberlands Williamsburg United States Department of CSE GITA Autonomous College Odisha India University Odisha India University Odisha India

Blockchain is a distributed digital ledger technology that allows for the immutable and auditable logging of transactions without needing a central authority or centralized database. First introduced in 2008 as the technology behind the digital currency Bitcoin, blockchain has now found use in a wide range of sectors. Modern agriculture supply networks have emerged to accommodate intricate interactions between regional actors and the agricultural environment. Interactions between various participants in the supply chain, including those involved in manufacturing, processing, and delivery. One of the main reasons fraud happens so often in agriculture is the lack of transparency in the supply chain. Businesses that aren't transparent run the risk of losing money, alienating their customers, and seeing their brands decline in value. To encourage the growth of an efficient and trustworthy trading environment, several fundamental adjustments to the current supply chain architecture are necessary. The potential for blockchain technology to enhance food and agricultural supply chain transparency is well acknowledged. Companies utilize IoT and blockchain to create more equitable, sustainable, and customer-friendly food manufacturing systems. In this current work, a blockchain-based decentralized application (dApp), 'ASBlock', is developed for agricultural-based supply chain management. The primary focus of this developed application is to bring transparency and security to all participating users of the application;the users of this application include farmers or producers, suppliers or retailers, hotels, and end users. The performance analysis shows that the average gas utilization of the proposed ASBlock dApp is ~2.8 * 104. The average smart contract execution time for all use cases is ~112sec. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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Ordering Services Modelling in Blockchain Platform for Food Supply Chain Management 2nd

Ordering Services Modelling in Blockchain Platform for Food ...

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2nd international conference on Emerging Trends in Expert Applications and security, ICETEAS 2023

作者： Deshmukh, Pratibha Avinash, Sharma Gonsai, Atul M. Sonawane, Sayas S. Khan, Taukir Bharati Vidyapeeth’s Institute of Management and Information Technology Maharashtra Navi Mumbai India HR Mullana-Ambala India Department of Computer Science Saurashtra University GJ Rajkot India

ISBN: (纸本)9789819919086

Blockchain technology (BCT) is widely used in various applications where different stakeholders of their own interest are coming together where they cooperate and coordinate in trustless decentralized environment. The BCT is more suitable for supply chain management (SCM) applications. The food items supply chain is sensitive issue, and it is difficult to handle the problems food traceability, safety enable, authentication of stakeholders, security and consensus, transparency in transactions, food items shortfall, matching demand and supply chain wastage of material, etc. This research encourages for different stakeholders those are coming together on same platform and cooperate, coordinate each other to achieve common goal. The hyperledger fabric transaction flow is formed to record the world state by passing communication channels on peer to peer through farmers to distributors, distributors to food processing industry, food processing industry to distributors, distributors to retailers, and lastly retailers to customers. This proposed research uses core components of blockchain technology by designing specific architecture, writing smart contracts on Ethereum platform which provides facilitation for certain kind of food supply chain ordering services as a public permissioned platform. It uses blockchain platform along with properties of distributed and peer-to-peer supply chain architecture, and based on this, the analysis of SCM is shown through experimental results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Impacts of High Denisty Cloud Computing on Data Protection and security Management for 6G Networking

Impacts of High Denisty Cloud Computing on Data Protection a...

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Disruptive Technologies (ICDT), international conference on

作者： Vinay Mallikarjunaradhya Haritha Yennapusa Ranadeep Reddy Palle K. Suganyadevi Nidhi Gupta Legal Tech Platform Thomson Reuters Toronto Canada Department of Computer Science University of central Missouri Austin Texas USA Department of Computer Science University of Houston Austin Texas USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India

Data protection and security management in the 6G era is a key challenge due to the emergence of high density cloud computing. The availability of high density cloud computing technologies creates a platform for massive data storage, processing, and analytics capabilities at a scale that has never been seen before. High density cloud computing facilitates the deployment of complex distributed systems consisting of hundreds or thousands of nodes in a single deployment. This can have a significant impact on data protection and security management as it increases the potential threat to the system from malicious actors. To protect data in this environment, organizations must adopt more advanced security practices such as access control, encryption, and defensive layers to protect against malicious actors. The use of encryption can provide a layer of security by making it difficult for outsiders to access information without authorization. Access control can also prevent unauthorized users from accessing certain parts of the system, while defensive layers can help detect and block malicious attacks. The results of this study showed that high density cloud computing can potentially improve the efficiency and performance of data protection and security management in 6G networks.

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Comparing the Supersonic Cloud Computing Model to Enhance the Networking and security in Traditional Data Centers

Comparing the Supersonic Cloud Computing Model to Enhance th...

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Disruptive Technologies (ICDT), international conference on

作者： Ranadeep Reddy Palle Vinay Mallikarjunaradhya Haritha Yennapusa K. Suganyadevi Nidhi Gupta Department of Computer Science University of Houston Austin Texas USA Legal Tech Platform Thomson Reuters Toronto Canada Department of Computer Science University of central Missouri Austin Texas USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India

The Comparing the supersonic cloud computing model to enhance the networking and security in Traditional Data Centers study aimed to analyse the differences between traditional data centers and cloud computing models. The researchers' findings demonstrate that the supersonic cloud model consists of a number of services and functions that can improve network and security performance across all levels of the data center. Furthermore, the supersonic model offers more options for load balancing through routing protocols and more secure network access. In addition, the supersonic model allows for more flexibility in the resources used, such as virtual servers and storage systems. Lastly, the supersonic model allows for greater scalability through the use of distributed simultaneous networks. These benefits pave the way to the eventual goal of improving existing data center operation and security. The objective of this study was to assess the potential of supersonic cloud computing model (SCC) to improve the networking and security in traditional data centers. The findings indicated that SCC improved the network efficiency by 10.4%, reduced network latency by 11 %, and improved packet delivery rates by 8.6%. The security was also improved, with overall firewall rules increased by 12 %, and 5% fewer security violations. Overall, SCC showed promising performance in improving the networking and security of traditional data centers.

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