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Effective Intrusion Detection in Heterogeneous Internet-of-Things networks via Ensemble Knowledge Distillation-Based Federated Learning

Effective Intrusion Detection in Heterogeneous Internet-of-T...

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IEEE international conference on Communications (ICC)

作者： Jiyuan Shen Wenzhuo Yang Zhaowei Chu Jiani Fan Dusit Niyato Kwok-Yan Lam Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

With the rapid development of low-cost consumer electronics and cloud computing, Internet-of- Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT devices are often susceptible to cyber attacks due to their open deployment environment and limited computing capabilities for stringent security controls. Hence, Intrusion Detection systems (IDS) have emerged as one of the effective ways of securing IoT networks by monitoring and detecting abnormal activities. However, existing IDS approaches rely on centralized servers to generate behaviour profiles and detect anomalies, causing high response time and large operational costs due to communication overhead. Besides, sharing of behaviour data in an open and distributed IoT network environment may violate on-device privacy requirements. Additionally, various IoT devices tend to capture heterogeneous data, which complicates the training of behaviour models. In this paper, we introduce Federated Learning (FL) to collaboratively train a decentralized shared model of IDS, without exposing training data to others. Furthermore, we propose an effective method called Federated Learning Ensemble Knowledge Distillation (FLEKD) to mitigate the heterogeneity problems across various clients. FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results on the public dataset CICIDS2019 demonstrate that the proposed approach outperforms local training and traditional FL in terms of both speed and performance and significantly improves the system's ability to detect unknown attacks. Finally, we evaluate our proposed framework's performance in three potential real-world scenarios and show FLEKD has a clear advantage in experimental results.

关键词： Training Privacy Federated learning Smart cities Intrusion detection Training data Data models

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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international conference on Parallel and distributed systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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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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7th international conference on Mobile Internet security, MobiSec 2023

7th International Conference on Mobile Internet Security, Mo...

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7th international conference on Mobile Internet security, MobiSec 2023

ISBN: (纸本)9789819744640

The proceedings contain 21 papers. The special focus in this conference is on Mobile Internet security. The topics include: Computing Resource Allocation Based on Multi-base Station and Multi-user Scenario in Mobile Edge Computing;lattice-Based Multi-entity Identification Protocols;Hardware Implementation of SM4 Encryption Algorithm with Hybrid Stochastic Computing;Privacy-Preserving Fair Outsourcing Polynomial Computation Without FHE and FPR;SFPDML: Securer and Faster Privacy-Preserving distributed Machine Learning Based on MKTFHE;existence of Equivalent Keys in a Random Number Generator with Field Trace and a Dynamic Mapping;a Heterogeneous Computing Framework for Accelerating Fully Homomorphic Encryption;generic Construction of Forward-Secure Chameleon Hash Function;exploring Leakage Characteristics and Attacks Through Profiles of Screaming Channels;Revisiting an Extension of Kannan’s Embedding for Ring-LWE;revisiting the Algorithms for the Quaternion -Isogeny Path Problems;the Impact of Data Scaling Approaches on Deep Learning, Random Forest and Nearest Neighbour-Based Network Intrusion Detection systems for DoS Detection in IoT networks;integrating Machine Learning Models into the Linux Kernel: Opportunities and Challenges;early Weather Warning System with Real-Time Monitoring by IoT Sensors Considering Scalability and Reliability;privacy Preserving Multi Factor Authentication Protocol for Next Generation Grids Deployed in Smart Cities;research Directions in Formal Verification of Network Configurations Toward Verification of Mobile networks;Destructive Malwares on MITRE ATT&CK Tactics for Cyber Warfare: A Brief Survey and Analysis;intelligent Cooperative Routing for Services in Computing Integration Networking;A Biometric-Based Data Protection Scheme for RSDs.

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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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