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Machine Learning Algorithms to Detect Illicit Accounts on Ethereum Blockchain 23

Machine Learning Algorithms to Detect Illicit Accounts on Et...

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Chibuzo Obi-Okoli Olamide Jogunola Bamidele Adebisi Mohammad Hammoudeh Department of Computing and Mathematics Manchester Metropolitan University UK Department of Engineering Manchester Metropolitan University UK Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9798400709036

The rapid growth and psudonomity inherent in blockchain technology such as in Bitcoin and Ethereum has marred its original intent to reduce dependant on centralised system, but created an avenue for illicit activities, including fraud, phishing, scams, etc. This undermines the reputation of blockchain network, giving rise to the need to identify these illicit activities within the blockchain network. This current work tackles this crucial problem by investigating and implementing six machine learning algorithms with a particular emphasis on striking a balance between accuracy, precision and recall. The novelty of the work lies in the utilising of the synthetic minority over-sampling technique to handle data imbalance. Thus, increasing the accuracy of the light gradient boosting machine classifier to 98.4%. The outcome of this work holds great potential for enhancing the security and credibility of blockchain ecosystems paving the way for a more secure and dependable digital future in the age of decentralised and trustless systems.

关键词： Ethereum blockchain

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Federated Learning in Malware Detection

Federated Learning in Malware Detection

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international conference on Emerging Technologies and Factory Automation (ETFA)

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

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%.

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Machine Learning for Cloud-Based DDoS Attack Detection: A Comprehensive Algorithmic Evaluation

Machine Learning for Cloud-Based DDoS Attack Detection: A Co...

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international conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Anukriti Naithani Shailendra Narayan Singh Krishna Kant Singh Sushil Kumar Computer Science and Engineering ASET Amity University Noida India Computer Science and Engineering NIT Kurukshetra India

distributed denial of service attacks, in the recent times, have become a critical threat to not only private server connections but also meta scale cloud computing environments. This research paper furnishes a detailed analysis of five machine learning algorithms in context with network intrusion detection that are; Isolation Forest, Random Forest, Support Vector Machine (SVM) and Artificial Neural networks (ANN). The dataset used in this study, to differentiate between attack and normal cases, is the KDD cup 1999, wherein the dataset has been converted into binary classes to differentiate between the attack and normal cases. Following are the part of the research; Preprocessing the data, Scaling the features, Training the model and evaluating the performance using crucial metrics like accuracy, precision and F1 score and recall. Graphical representation and confusion matrices for data visualization have been made use of in addition to the aforementioned. The results show a penetrative information about the relative effectiveness and success of different ML algorithms in identifying network intrusions and hence can be used as insightful references for academics and practitioners in the field of network security. A well- explained algorithm selection, based on performance indicators enables an advanced approach to the study, thus providing a framework for further developments in systems of intrusion detection.

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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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A Comparative Analysis of IoT Protocols for Resource Constraint Devices and networks 22

A Comparative Analysis of IoT Protocols for Resource Constra...

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Proceedings of the 6th international conference on Future networks & distributed systems

作者： Sara Holm Mohammad Hammoudeh School of Computing and Mathematics Manchester Metropolitan University United Kingdom Information Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9781450399050

Communication standards and protocols are detrimental to the success of any Internet of Things (IoT) system or application. Selecting a communication standard and a suitable middleware or messaging protocol for IoT connectivity is challenging due to the heterogeneous resource-constrained IoT devices and their messaging requirements. Recently, several messaging/middleware protocols in the IoT field were developed and adopted in the industry. However, to date, there is no specific messaging protocol that can support all messaging use cases and fulfil the overall requirements of IoT systems. Therefore, it is critical to understand the application layer messaging and communication protocols of IoT systems to identify the most appropriate protocol that could fit and be applied in various contexts. This paper provides a comparative analysis of the MQTT, CoAP, and AMQP messaging protocols including their security.

关键词： Blockchain

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Demo Abstract: distributed Virtual CPS Environment for K12 20

Demo Abstract: Distributed Virtual CPS Environment for K12

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20th ACM/IEEE international conference on Information Processing in Sensor networks (IPSN)

作者： Stein, Gordon Jean, Devin Ledeczi, Akos Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781450380980

While educational robotics and makerspaces are useful to modern STEM education, they introduce both physical and economic barriers to entry. By creating a simulated, networked environment, we can facilitate instruction on cyber-physical systems and related topics while reducing cost and complexity. The virtual environment created is connected to a block-based programming language, NetsBlox, to allow students to engage with the curriculum regardless of programming experience. The networked simulation and collaborative programming environment combine to become especially effective for distance learning. This demonstration showcases example scenarios providing students with a simple interface to interact with a simplified sensor network in a small area of a smart city and solve robot challenges.

关键词： computer science education educational robotics distance learning

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The Impact of Quantum Computing on security in Emerging Technologies 5

The Impact of Quantum Computing on Security in Emerging Tech...

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5th international conference on Future networks and distributed systems: The Premier conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Abuarqoub, Abdelrahman Abuarqoub, Simak Alzu'bi, Ahmad Muthanna, Ammar Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom Faculty of Engineering and Technology Liverpool John Moores University Liverpool United Kingdom Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan Department of Telecommunication Networks and Data Transmission Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg193232 Russia

ISBN: (纸本)9781450387347

Quantum computing is rapidly shifting from high-tech lab to mainstream commercial use in the next few years, representing the next breakthrough in modern ICT. It is motivated by quantum computer's ability to perform computational tasks with efficiencies that are not possible with classical computers. Quantum computing technology has a lasting potential impact on society and broad implications on many aspects of our lives. Although quantum computing has several advantages, it threatens the security of current cryptographic techniques. It is expected that quantum computers will be able to solve the key distribution problem and break every single cryptography and authentication algorithm based on asymmetric cryptography. New emerging technologies such as the Internet of Things, blockchain, autonomous vehicles, 5G, artificial intelligence and robotics all rely on cryptography for their secure operation. Therefore, the realisation of quantum computing will pose significant challenges to the current progress and future development of these technologies. In this paper, the quantum technology preliminaries are discussed. The possible impact of quantum technologies on emerging technologies is investigated. Finally, some of the currently available quantum-safe cryptosystems devoted to protecting computer systems in the quantum era are discussed. © 2021 ACM.

关键词： 5G mobile communication systems

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TON-IoT: Detection of Attacks on Internet of Things in Vehicular networks 6

TON-IoT: Detection of Attacks on Internet of Things in Vehic...

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6th international conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sharma, Anshika Babbar, Himanshi Sharma, Avinash Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science and Engineering Mullana Haryana Ambala India

ISBN: (纸本)9781665482714

During the recent era, due to the exponential rise in the Internet of things (IoT) enabled devices around the globe, IoT and Machine learning (ML) has come out as usable and efficient approach to supply productive solutions in this environment. Vehicular networks (VN) are considered to be the most important application domain where ML-based techniques are generated to address common attack issues and present insightful discussions. It is the union of smart transport and internet systems which is responsible for the passenger's safety and security as the attack threats have been growing very rapidly in VN. The main objective is to overwhelm the targeted IoT devices with malicious data traffic in VN. To solve the above-mentioned issues, this paper covers numerous types of attacks (backdoor, injection, distributed Denial of service (DDoS), ransomware, password, scanning, Man in the middle (MITM) and cross-site scripting (XS S)) on VN deploying ML techniques using intrusion detection system (IDS) in the IoT based on the TON-IoT dataset. Due to the fact that it contains a variety of normal and malicious activities for various IoT services as well as heterogeneous data suppliers, TON-IoT provides a number of advantages that are completely missing from state-of-the-art datasets. The performance metrics are evaluated for the attack detection namely accuracy, precision, recall and F1-score using the three ML methods Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbour (KNN). Finally, the results conclude that amongst the three ML methods, KNN gives the highest accuracy rate i.e. 98.2%, while RF and NB give a 94% and 70% accuracy rate utilizing the ToN-IoT dataset. © 2022 IEEE.

关键词： Internet of things

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A Multistep Ahead Predictive Analysis of Crude Oil

A Multistep Ahead Predictive Analysis of Crude Oil

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Blockchain and distributed systems security (ICBDS), IEEE international conference on

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

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.

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Game Theoretical Model for Cybersecurity Risk Assessment of Industrial Control systems 11

Game Theoretical Model for Cybersecurity Risk Assessment of ...

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11th IFIP international conference on New Technologies, Mobility and security, NTMS 2021

作者： Nassar, Mohamed Khoury, Joseph Erradi, Abdelkarim Bou-Harb, Elias Computer Science Department Beirut Lebanon Qatar University College of Engineering Computer Science and Engineering Department Doha Qatar Cyber Center for Security and Analytics United States

ISBN: (纸本)9781665443999

Supervisory Control and Data Acquisition (SCADA) and distributed Control systems (DCS) use advanced computing, sensors, control systems, and communication networks to monitor and control industrial processes and distributed assets. The increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-Attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess the security risks of Industrial Control systems (ICS). In this paper, we propose a novel game theory model and Monte Carlo simulations to assess the cybersecurity risks of an exemplary industrial control system under realistic assumptions. We present five game enrollments where attacker and defender agents make different preferences and we analyze the final outcome of the game. Results show that a balanced defense with uniform budget spending is the best strategy against a look-Ahead attacker. © 2021 IEEE.

关键词： Cybersecurity

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