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作者： Zoghi, Zeinab University of Toledo

学位级别：硕士

In this study, an Intrusion detection system (IDS) is designed based on Machine Learning classifiers and its performance is evaluated for the set of attacks entailed in the UNSW-NB15 dataset. This dataset is comprised of 2,540,226 realistic network data instances as well as 49 features. Most studies reported in the literature employ a representative subset of this dataset with predefined training and testing subsets, and containing a total of 257,673 records which this study also used. In light of relatively lower than expected performance of Machine Learning or Statistical classification algorithms tested on this dataset and as reported by others in the literature, this dataset was subjected to visual data analysis to explore potential reasons or issues which likely challenge Machine Learning classifiers. The consequent observations demonstrated the presence of class representation imbalance with respect to pattern counts and class overlap in feature space, which makes preprocessing strategies indispensable before this dataset can be meaningfully employed for data-driven model development for intrusion *** preprocessing, we implemented min-max scaling in the normalization phase followed by the application of Elastic Net and Sequential Feature Selection (SFS) algorithms. We employed ensemble methods using three base classifiers, namely Balanced Bagging, XGBoost, and RF-HDDT, augmented to address the imbalance issue. Parameters of Balanced Bagging and XGBoost are tuned for the imbalanced data, and Random Forest is supplemented by the Hellinger distance metric to address the limitations of default distance metric. Two new algorithms are proposed to address the class overlap issue in the dataset and applied during training. These two algorithms are leveraged to help improve the performance on the testing dataset by affecting the final classification decision made by three base classifiers as part of the ensemble classifier which employsa majority vote combine

关键词： Mathematics Computer Engineering Computer Science Engineering Statistics UNSW-NB15 Ensemble Learning Ensemble Classification XGBoost Random Forest Balanced Bagging Bagging Boosting Hellinger Distance Elastic Net Sequential Feature Selection anomaly detection system Machine Learning Cybersecurity Data Science

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Mutual Information Based Ensemble anomaly detection Strategy for Cyber-Physical systems 8th

Mutual Information Based Ensemble Anomaly Detection Strategy...

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Ahmad, Zeeshan Petrovski, Andrei Kotenko, Igor Shah, Syed Aziz Li, Yin School of Engineering Computing and Mathematical Sciences University of Wolverhampton Wolverhampton United Kingdom Samara State Technical University Samara Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom Harbin Institute of Technology Harbin China

ISBN: (纸本)9783031774102

Cyber-Physical systems (CPS) transformed industry 4.0 by integrating the cyber world with the physical processes for smooth industrial operations. However, this integration of interconnected industrial devices with digital technologies makes CPS more susceptible to cyber and physical anomalies. To secure CPS from all such anomalies, one possible solution is the deployment of anomaly detection systems, which detect physical and cyber irregularities in the data. To this end, this study proposes a Mutual Information-based Ensemble anomaly detection Strategy utilizing Gradient Boosting Machine (GBM) and Light GBM to detect cyber and physical anomalies to secure CPS. The proposed methodology is evaluated on the physical and network hardware-in-the-loop dataset obtained from a Water Distribution Testbed. The proposed solution exhibited 4.6% to 6.0% and 0.7% to 1.8% accuracy improvement and a reduction of 4.7% to 6.3% and 0.7% to 1.4% in false alarm rate on the network and physical datasets respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： anomaly detection system Cyber-Physical system Gradient Boosting Machine Machine Learning Mutual Information

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Detekce síťových anomálií

Detekce síťových anomálií

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作者： Pšorn, Daniel Brno University of Technology

Tato diplomová práce se zabývá metodami detekce anomálií v sítovém provozu. Nejdříve práce rozebírá základní pojmy z oblasti detekce anomáli&#... 详细信息

Tato diplomová práce se zabývá metodami detekce anomálií v sítovém provozu. Nejdříve práce rozebírá základní pojmy z oblasti detekce anomálií a již používané technologie. Dále jsou zde podrobněji popsány tři metody pro vyhledávání anomálií a některé typy anomálií. V druhé části této práce je popsána implementace všech tří metod a jsou prezentovány výsledky experimentování na reálných datech.

关键词： Systém detekce anomálií počítačové sítě bezpečnost metoda ASTUTE Kalmanův filtr vlnková transformace anomaly detection system computer networks security ASTUTE method Kalman filter wavelet transform Text

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Enhanced anomaly detection in Ethereum: Unveiling and Classifying Threats With Machine Learning

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IEEE ACCESS 2024年 12卷 176440-176456页

作者： Ehsan, Alvena Iqbal, Zahid Abuowaida, Suhaila Aljaidi, Mohammad Zia, Hafiz Usman Alshdaifat, Nawaf Alshammry, Nizal Khalf Univ Gujrat Fac Comp & IT Dept Informat Technol Gujrat 50700 Pakistan Univ Gujrat Fac Comp & IT Dept Comp Sci Gujrat 50700 Pakistan Al Al Bayt Univ Fac Prince Al Hussein Bin Abdallah II IT Dept Comp Sci Mafraq 25113 Jordan Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Appl Sci Private Univ Fac Informat Technol Amman 11937 Jordan Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Ar Ar 91431 Saudi Arabia

Blockchain has emerged as a groundbreaking security technology, playing a vital role in various industries such as banking, the Internet of Things (IoT), healthcare, education, and voting. However, the widespread adoption of this technology has introduced certain vulnerabilities, particularly in the form of exploitation by malicious entities. While existing research primarily focuses on identifying anomalous actor behavior, there has been limited exploration of precisely identifying hostile actors within the Ethereum network. This study aims to uncover malevolent actors operating on the Ethereum network and categorize attacks based on their actions. To achieve this research goal, a new dataset was constructed by consolidating data on malicious actors involved in illicit Ethereum activities. Key features were extracted from this dataset using advanced feature selection techniques, including Principal Component Analysis (PCA), Information Gain, and Ridge Regression. Machine learning classifiers such as LGBM, XGBoost, Random Forest, Extra Tree, Bagging, and K-Nearest Neighbors were applied to identify and classify malicious actors effectively. The results, achieving an impressive accuracy rate of 98%, underscore the effectiveness of Information Gain when coupled with LGBM and XGBoost. Notably, XGBoost demonstrates efficiency by completing the analysis in a mere 13.72 seconds. In addition to identifying fraudulent activities, this research classifies them into distinct categories, enhancing blockchain security and addressing trust concerns. This study's outcomes fortify the Ethereum network's resilience and contribute to the broader discourse on bolstering reliability in blockchain systems.

关键词： Peer-to-peer computing Bitcoin Accuracy Security Smart contracts Random forests Feature extraction anomaly detection Wireless sensor networks Principal component analysis Ethereum malicious actor anomaly detection system phishing PCA information gain

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IoTPredictor: A security framework for predicting IoT device behaviours and detecting malicious devices against cyber attacks

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COMPUTERS & SECURITY 2024年 146卷

作者： Kalaria, Rudri Kayes, A. S. M. Rahayu, Wenny Pardede, Eric Salehi, S. Ahmad La Trobe Univ Bundoora Vic 3086 Australia

Securing Internet of Things (IoT) devices is paramount to mitigate unauthorised access and potential cyber threats, safeguarding the integrity of transmitted and processed data within interconnected devices. Identifying malicious IoT devices necessitates vigilant monitoring of network traffic, behaviour analysis, and implementing security measures, including anomaly detection systems (ADSs), Intrusion detection systems (IDSs), and regular firmware updates. Traditional security approaches need to be revised for safeguarding IoT systems due to their inherent limitations in accommodating the resource-constrained nature of these devices. We introduce IoTPredictor, , an advanced security approach designed to predict and detect malicious activities in IoT devices. Leveraging Hidden Markov Models (HMMs), IoTPredictor integrates an ADS to proactively detect and thwart attacks within the complex IoT-fog computing landscape. Our conceptual approach begins with categorising IoT devices into genuine, compromised, and counterfeit. We propose an HMM-based state transition model that captures potential transitions between states, such as normal, compromised, or counterfeit operations. We introduce an algorithm for estimating probabilities associated with next-state predictions to facilitate predictive analysis. Furthermore, we present a formal approach for analysing communications between different states, enhancing the precision of the security framework. To validate the effectiveness of IoTPredictor, , we conduct a series of experiments and provide a comprehensive evaluation. The results demonstrate the robustness and efficiency of our proposed security framework in predicting and preventing malicious activities, thereby contributing to the overall security enhancement of IoT devices within the complex IoT-fog computing network.

关键词： Internet of Things Fog computing Device security Intrusion detection system anomaly detection system Hidden Markov model

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Automated One-Sided Learning Fault detection system for Reaction Wheel Bearing Friction Anomalies

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JOURNAL OF AEROSPACE INFORMATION systemS 2024年第5期21卷 380-391页

作者： Huang, Yujia Ferguson, Philip Univ Manitoba Dept Mech Engn E1-451 75A Chancellors Circle Winnipeg MB R3T 2N2 Canada Univ Manitoba NSERC Magellan Aerosp Ind Res Chair Satell Engn Dept Mech Engn E1-546 76A Chancellors Circle Winnipeg MB R3T 2N2 Canada

Monitoring a satellite's health during a mission has become critical in space engineering. In-flight anomaly detection is difficult for ground operators, placing space missions at risk of failure. Machine learning algorithms are data-driven methods that could autonomously detect faults in situ. In this paper, a new application of machine learning algorithms in space engineering is introduced for detecting reaction wheel bearing anomalies that only relies on nominal data (no failure data) for training and with no prior knowledge of the system dynamics. Using a one-sided regression method, an automated fault detection system was designed to monitor the attitude dynamics control system for a small satellite. The proposed detection algorithm was first trained using a simulated attitude dynamics control system for the small satellite. Next, the detection system was trained with only nominal behavioral data of the control system for a designated period of time. Then, different types of bearing friction failures were added to the simulated system to test the trained fault detection system. The empirical rule (68-95-99.7 rule) was used as a failure detection criterion to differentiate failure data from nominal. Similar physical tests were conducted using a combination of a brushless motor and drone propellers. Both simulation and experimental results demonstrated the robustness of detection accuracy, were model-free, and verified the feasibility of an easy-to-use, accurate, and autonomous anomaly detection system for reaction wheels that could be extended to other space systems.

关键词： Fault detection and Isolation Spacecraft Attitude Control Propellers Control systems Support Vector Machine anomaly detection system Brushless Motor Unmanned Aerial Vehicle Reaction Wheel Bearing Friction Anomalies

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Key Process Protection of High Dimensional Process Data in Complex Production

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CMC-COMPUTERS MATERIALS & CONTINUA 2019年第2期60卷 645-658页

作者： Shi, He Shang, Wenli Chen, Chunyu Zhao, Jianming Yin, Long Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China

In order to solve the problem of locating and protecting key processes and detecting outliers efficiently in complex industrial processes. An anomaly detection system which is based on the two-layer model fusion frame is designed in this paper. The key process is located by using the random forest model firstly, then the process data feature selection, dimension reduction and noise reduction are processed. Finally, the validity of the model is verified by simulation experiments. It is shown that this method can effectively reduce the prediction accuracy variance and improve the generalization ability of the traditional anomaly detection model from the experimental results.

关键词： Industrial control system outlier detection anomaly detection system rule tree model

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Methods of Testing of the Corporate Network's Traffic Anomalies

Methods of Testing of the Corporate Network's Traffic Anomal...

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2018 3rd International Conference on Communications, Information Management and Network Security(CIMNS2018)

作者： Nikita Kulyаsov Sergey Isaev Institute of Computational Modelling of the Siberian Branch of the Russian Academy of Sciences

This article gives an overview of the existing methods and software products designed to analyze anomalies that may indicate the occurrence of *** their basis,we propose our own original software tool that allows automatic detection of anomalies and subsequent detailed analysis of network service logs according to the metrics chosen by the *** software tool is designed as a web application integrated into the existing infrastructure of the corporate network of a scientific *** of our web application showed the relevance and demand of the anomaly detection systems' development.

关键词： network anomalies cybersecurity anomaly detection system intrusion detection system

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