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IEEE CS International Conference on Software Science, Technology and Engineering (SWSTE)

作者： Leshem, Guy David, Esther Chalamish, Michal Shapira, Dana Ashkelon Acad Coll Dept Comp Sci Ashqelon Israel

ISBN: (纸本)9781479944323

Today, sensors and/or anomaly detection algorithms (ADAs) are used to collect data in a wide variety of applications(e.g. Cyber security systems, sensor networks, etc.). Today, every sensor or ADA in its applied system participates in the collection of data throughout the entire system. The data collected from all of the sensors or ADAs are then integrated into one significant conclusion or decision, a process known as data fusion. However, the reliability, or reputation, of a single sensor or ADA may change over time, or may not be known at all. Since this reputation is taken into account when determining the final conclusion post data classification, one must be able to predict their reputations. We propose a new machine learning prediction technique (MLPT) to predict the reputation of each sensor or ADA. This technique is based on the existing 'Decision Tree Certainty Level' technique, or DTCL, which is the creation of many random decision trees (forests) with high certainty levels [Dolev et al. (2009)]. In particular, it was shown that the DTCL enhances the classification capabilities of CARTs (Classification and Regression Trees) [Briman et al. (1984)]. After applying the DTCL technique to the reputation data, we then apply a new evolutionary process on those decision trees to reduce the overall number of trees by merging only the most accurate trees and then using only these new trees to generate the reputation values. Thus, we combine DTCL and evolution techniques to enable the determination of sensor or ADA reputations by using only the most accurate trees. Finally, we demonstrate how to improve the data fusion process by identifying the most reliable portions of the collected data to reach more accurate conclusions.

关键词： anomaly detection algorithms Decision tree Prediction Reputation

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Implementation of anomaly detection algorithms for Detecting Transmission Control Protocol Synchronized Flooding Attacks 12

Implementation of Anomaly Detection Algorithms for Detecting...

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Mkuzangwe, Nenekazi N. P. McDonald, Andre Nelwamondo, Fulufhelo V. CSIR Modelling & Digital Sci Pretoria South Africa Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa

ISBN: (纸本)9781467376822

This work implements two anomaly detection algorithms for detecting Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The two algorithms are an adaptive threshold algorithm and a cumulative sum (CUSUM) based algorithm. Furthermore, we fused the outcomes of the two algorithms using the logic OR operator at different thresholds of the two algorithms to obtain improved detection accuracy. Indeed, the results indicated that the OR operator performs better than the two algorithms in detecting SYN flooding attack and detection delay.

关键词： TCP SYN Flooding attack anomaly detection algorithms fusion detection probability false alarm rate detection delay

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Implementation of anomaly detection algorithms for Detecting Transmission Control Protocol Synchronized Flooding Attacks

Implementation of Anomaly Detection Algorithms for Detecting...

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International Conference on Fuzzy Systems and Knowledge Discovery

作者： Nenekazi N. P. Mkuzangwe Andre McDonald Fulufhelo V. Nelwamondo Modelling and Digital Science Council for Scientific and Industrial Research Pretoria South Africa

ISBN: (纸本)9781467376839

关键词： component TCP SYN Flooding attack anomaly detection algorithms fusion detection probability false alarm rate detection delay

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Pose-driven human activity anomaly detection in a CCTV-like environment

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IET IMAGE PROCESSING 2023年第3期17卷 674-686页

作者： Yang, Yuxing Angelini, Federico Naqvi, Syed Mohsen Newcastle Univ Intelligent Sensing & Commun Res Grp Newcastle Upon Tyne Tyne & Wear England Thales UK Ltd Reading RG2 6GF Berks England

Human activity anomaly detection plays a crucial role in the next generation of surveillance and assisted living systems. Most anomaly detection algorithms are generative models and learn features from raw images. This work shows that popular state-of-the-art autoencoder-based anomaly detection systems are not capable of effectively detecting human-posture and object-positions related anomalies. Therefore, a human pose-driven and object-detector-based deep learning architecture is proposed, which simultaneously leverages human poses and raw RGB data to perform human activity anomaly detection. It is demonstrated that pose-driven learning overcomes the raw RGB based counterpart limitations in different human activities classification. Extensive validation is provided by using popular datasets. Then, it is demonstrated that with the aid of object detection, the human activities classification can be effectively used in human activity anomaly detection. Moreover, novel challenging datasets, that is, BMbD, M-BMbD and JBMOPbD, are proposed for single and multi-target human posture anomaly detection and joint human posture and object position anomaly detection evaluations.

关键词： pose estimation data handling different human activities classification human poses assisted living object detection deep learning (artificial intelligence) Image recognition Neural nets object position anomaly detection evaluations popular state-of-the-art autoencoder-based anomaly detection systems Optical, image and video signal processing anomaly detection algorithms joint human posture human activity anomaly detection Computer vision and image processing techniques object-positions related anomalies

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Hyperspectral anomaly detection:a performance comparison of existing techniques

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International Journal of Digital Earth 2022年第1期15卷 2078-2125页

作者： Noman Raza Shah Abdur Rahman M.Maud Farrukh Aziz Bhatti Muhammad Khizer Ali Khurram Khurshid Moazam Maqsood Muhammad Amin iVISION Lab Electrical Engineering DepartmentInstitute of Space TechnologyIslamabadPakistan AI Enabling Technologies Lab Center of Excellence in Artificial IntelligenceBahria UniversityIslamabadPakistan Department of Electrical and Computer Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripurPakistan Avionics Engineering Department Institute of Space TechnologyIslamabadPakistan

anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several *** anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is *** this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different *** addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available *** indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)***,this paper studies the effect of various dimensionality reduction techniques on anomaly *** indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

关键词： anomaly detection algorithms hyperspectral imagery deep learning dimensionality reduction

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Online anomaly detection Leveraging Stream-Based Clustering and Real-Time Telemetry

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第1期18卷 839-854页

作者： Putina, Andrian Rossi, Dario Telecom Paris Infres Dept F-75013 Paris France Huawei Technol France DataCom Dept F-92100 Boulogne Billancourt France

Recent technology evolution allows network equipment to continuously stream a wealth of "telemetry" information, which pertains to multiple protocols and layers of the stack, at a very fine spatial-grain and high-frequency. This deluge of telemetry data clearly offers new opportunities for network control and troubleshooting, but also poses a serious challenge for what concerns its real-time processing. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed up to 3Terabit/s worth of real application traffic. We contrast DenStream with offline algorithms such as DBScan and Local Outlier Factor (LOF), as well as online algorithms such as the windowed version of DBScan, ExactSTORM, Continuous Outlier detection (COD) and Robust Random Cut Forest (RRCF). Our experimental campaign compares these seven algorithms under both accuracy and computational complexity viewpoints: results testify that DenStream (i) achieves detection results on par with RRCF, the best performing algorithm and (ii) is significantly faster than other approaches, notably over two orders of magnitude faster than RRCF. In spirit with the recent trend toward reproducibility of results, we make our code available as open source to the scientific community.

关键词： anomaly detection Telemetry Feature extraction Support vector machines Real-time systems Protocols Principal component analysis anomaly detection algorithms stream learning machine learning model driven telemetry network monitoring and measurements

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anomaly detection and Categorization for a Data Quality Management Framework in Financial Regulatory Reporting

Anomaly Detection and Categorization for a Data Quality Mana...

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Enterprise Design, Operations, and Computing (EDOC) Forum / IDAMS Workshop / SoEA4EE Workshop / IRESEARCH Workshop / MIDas4CS Workshop

作者： Tafech, Aya Univ New South Wales Sydney NSW 2052 Australia

ISBN: (纸本)9783031547119;9783031547126

Financial institutions are subject to stringent regulatory reporting requirements tomanage operational risk in international financialmarkets. Producing accurate and timely reports has raised challenges in current data processes of big data heterogeneity, system interoperability and enterprise-wide management. Data quality management is a key concern, with current approaches being timeconsuming, expensive, and risky. This research proposes to design, develop, and evaluate a Financial Reporting Data Quality Framework that allows non-IT data consumers to contextualize data observations. The framework will use anomaly algorithms to detect and categorize observations as genuine business activities or data quality issues. To ensure sustainability and ongoing relevance, the framework will also embed an update mechanism.

关键词： Big Data Data Quality Financial Regulatory Reporting anomaly detection algorithms Machine Learning

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anomaly detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction

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WATER RESOURCES RESEARCH 2024年第6期60卷 e2023WR034748-e2023WR034748页

作者： Zhu, Ruijie Yang, Fengtian Zhou, Xiaocheng Tian, Jiao Zhang, Yongxian He, Miao Li, Jingchao Dong, Jinyuan Li, Ying Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun Peoples R China China Earthquake Adm Inst Earthquake Forecasting United Lab High Pressure Phys & Earthquake Sci Beijing Peoples R China

This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M >= 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre-earthquake anomalies from post-earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods. This study explores the potential of utilizing machine learning algorithms for earthquake prediction based on hydrothermal fluid chemical anomaly data. An earthquake prediction model integrating six machine learning algorithms was developed using continuous hot spring hydrochemical monitoring data. The mode

关键词： earthquake prediction hot spring hydrogeochemical anomalies machine learning anomaly detection algorithms

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A Novel Network user Behaviors and Profile Testing based on anomaly detection Techniques

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第6期10卷 305-324页

作者： Tahir, Muhammad Li, Mingchu Zheng, Xiao Carie, Anil Jin, Xing Azhar, Muhammad Ayoub, Naeem Wagan, Atif Aamir, Muhammad Jamali, Liaquat Ali Imran, Muhammad Asif Hulio, Zahid Hussain Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116020 Peoples R China Shenzhen Univ Coll Comp Sci Shenzhen 518060 Guangdong Peoples R China Univ Southern Denmark Dept Math & Comp Sci Cam Pusvej 55 DK-5230 Odense M Denmark Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Nankai Univ Coll Software Engn Tianjin 300350 Peoples R China Dalian Univ Technol Sch Chem Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

The proliferation of smart devices and computer networks has led to a huge rise in internet traffic and network attacks that necessitate efficient network traffic monitoring. There have been many attempts to address these issues;however, agile detecting solutions are needed. This research work deals with the problem of malware infections or detection is one of the most challenging tasks in modern computer security. In recent years, anomaly detection has been the first detection approach followed by results from other classifiers. anomaly detection methods are typically designed to new model normal user behaviors and then seek for deviations from this model. However, anomaly detection techniques may suffer from a variety of problems, including missing validations for verification and a large number of false positives. This work proposes and describes a new profile-based method for identifying anomalous changes in network user behaviors. Profiles describe user behaviors from different perspectives using different flags. Each profile is composed of information about what the user has done over a period of time. The symptoms extracted in the profile cover a wide range of user actions and try to analyze different actions. Compared to other symptom anomaly detectors, the profiles offer a higher level of user experience. It is assumed that it is possible to look for anomalies using high-level symptoms while producing less false positives while effectively finding real attacks. Also, the problem of obtaining truly tagged data for training anomaly detection algorithms has been addressed in this work. It has been designed and created datasets that contain real normal user actions while the user is infected with real malware. These datasets were used to train and evaluate anomaly detection algorithms. Among the investigated algorithms for example, local outlier factor (LOF) and one class support vector machine (SVM). The results show that the proposed anomaly-based and profile

关键词： Network user behaviors profile testing anomaly detection techniques datasets anomaly detection algorithms machine learning

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Smoothing Gamma Ray Spectra to Improve Outlier detection 7

Smoothing Gamma Ray Spectra to Improve Outlier Detection

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7th IEEE Symposium on Computational Intelligence for Security and Defense Applications (IEEE-CISDA)

作者： Barnabe-Lortie, Vincent Bellinger, Colin Japkowicz, Nathalie Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781479954315

Rapid detection of radioisotopes in gamma-ray data can, in some situations, be an important security concern. The task of designing an automated system for this purpose is complex due to, amongst other factors, the noisy nature of the data. The method described herein consists of preprocessing the data by applying a smoothing method tailored to gamma ray spectra, hoping that this should decrease their variance. Given that the number of counts at a given energy level in a spectrum should follow a Poisson distribution, smoothing may allow us to estimate the true photon arrival rate. Our experiments suggest that the added data preprocessing step can have large impact on the performance of anomaly detection algorithms on this particular domain.

关键词： Poisson distribution gamma-ray spectra national security radioisotopes smoothing methods anomaly detection algorithms data preprocessing step gamma ray spectra outlier detection radiation level monitoring radioisotope detection security concerns smoothing method Gamma-rays Noise measurement Smoothing methods Splines (mathematics) Support vector machines Testing Training gamma-ray spectra smoothing methods Poisson distribution Radioisotopes Gamma Rays Noise measurement National Security Support Vector Network detection algorithms Functional training

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