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anomaly detection system for ADS-B data: Attack vectors and machine learning models

INTERNET OF THINGS 2025年 29卷

作者： Cevik, Nursah Akleylek, Sedat HAVELSAN TR-06800 Ankara Turkiye Univ Tartu Inst Comp Sci EE-51009 Tartu Estonia Istinye Univ Dept Comp Engn TR-34010 Istanbul Turkiye

The topic of security challenges and solutions for Automatic Dependent Surveillance-Broadcast (ADS-B) systems is becoming more critical day-to-day because of the increasing air traffic volume. Since aircraft and ground stations receive broadcast ADS-B data cannot check the source and integrity of data, ADS-B systems can be spoofed easily by transmitting false data. In this paper, we develop an anomaly detection system for ADS-B data as a security solution. Various parameter sets were analyzed to identify critical ones. We created attack vectors for eight different attack scenarios, such as spoofing, message injection, and virtual trajectory change attacks, and created hybrid datasets by combining different attack vectors to increase the detection ability of different attack scenarios. These datasets have covered a wide range of attack scenarios to increase the robustness of anomaly detection assessments. We used attack datasets to evaluate the performance of different ML and DL models. The random forest classifier and the extra tree classifier are the standout performers, both achieving an impressive accuracy of 0.999. The decision tree classifier, with an accuracy of 0.992, also demonstrates strong performance, though slightly below that of the random forest and extra tree models. The results of the decision tree classifier have the lowest false negative and false positive rate, which are 0.004 and 0, respectively. Among the deep learning models, the multilayer perceptron model achieves notable success with an accuracy of 0.981744. Based on the results of our model, we increase the accuracy and reliability compared to existing methods. Additionally, we share our datasets to encourage further research and enable other researchers to expand our findings.

关键词： ADS-B anomaly detection system Intrusion detection system IDS Machine learning Avionics security Cyber security

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anomaly detection system in Cloud Environment Using Fuzzy Clustering Based ANN

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MOBILE NETWORKS & APPLICATIONS 2016年第3期21卷 494-505页

作者： Pandeeswari, N. Kumar, Ganesh PSNA Coll Engn & Technol Dept Informat Technol Dindigul India

Cloud computing affords lot of resources and computing facilities through Internet. Cloud systems attract many users with its desirable features. In spite of them, Cloud systems may experience severe security issues. Thus, it is essential to create an Intrusion detection system (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment. This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system. The proposed system is implemented and compared with Na < ve Bayes classifier and Classic ANN algorithm. The DARPA's KDD cup dataset 1999 is used for experiments. Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Na < ve Bayes classifier and Classic ANN.

关键词： anomaly detection system Artificial neural network Cloud computing False alarm rate Fuzzy clustering Naive Bayes classifier

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STRUCTURAL MATERIAL anomaly detection system USING WATER CHEMISTRY DATA .1. BASIC STRUCTURE OF anomaly detection system AND ITS APPLICATION FOR FUEL DEFECT detection

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 1986年第3期23卷 233-243页

作者： UCHIDA, S ASAKURA, Y KITAMURA, M OHSUMI, K HITACHI LTD HITACHI WORKS HITACHI 317 JAPAN

A system to diagnose abnormalities on structural materials in the primary cooling systems of BWR by analyzing water chemistry data is proposed. In the system, patterns composed of more than two kinds of water chemistry data are compared with several stages of reference patterns obtained by the models which relate the water quality changes to the structural material abnormalities. Small abnormalities on the materials canbe expected to be detected at very early stages and future abnormalities can be predicted by the diagnosis system. In this paper, the basic structures of the anomaly detection system using water chemistry data are explained.

关键词： water chemistry crud BWR type reactors anomaly detection system structural materials dose rate nuclear fuels defects diagnostic techniques

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SurveilNet: A Lightweight anomaly detection system for Cooperative IoT Surveillance Networks

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IEEE SENSORS JOURNAL 2021年第22期21卷 25293-25306页

作者： Osifeko, Martins O. Hancke, Gerhard P. Abu-Mahfouz, Adnan M. Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210049 Peoples R China CSIR ZA-0184 Pretoria South Africa

The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process. However, the isolated setup of existing surveillance systems makes ADM inefficient and susceptible to staleness due to the lack of resource sharing and continuous learning (CL). CL is the incremental development of models that adapts continuously to the external world. Thus, for efficient CL in surveillance systems, devices must share resources and cooperate with neighbor sites. Yet, solutions from the literature focus on the isolated environment thereby neglecting the need for resource sharing and CL. To address this gap, this paper proposes a cooperative surveillance system called SurveilNet that allows for resource sharing between surveillance sites under the control of a cooperator node. We further propose a lightweight subscription scheme that allows for a joint specialized model development process that continually adapts to the dynamics of the secured environment. Our proposed scheme offers the ability to learn from the neighboring site's data without compromising data privacy. The performance of our scheme is evaluated using a reclassified UCF-Crime dataset with the result showing the efficiency of our proposed scheme when compared to the state-of-the-art.

关键词： Surveillance anomaly detection Sensors Feeds Feature extraction Task analysis Data privacy anomaly detection system federated deep learning Internet of Things (IoT) surveillance systems

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STRUCTURAL MATERIAL anomaly detection system USING WATER CHEMISTRY DATA .2. PREDICTION MODEL OF DOSE-RATE AROUND RECIRCULATION PIPES IN IRON CRUD SUPPRESSED BWR

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 1987年第7期24卷 557-564页

作者： NAGASE, M ASAKURA, Y HUJIMORI, H SAKAGAMI, M UCHIDA, S Energy Research Laboratory Hitachi Ltd.

A calculation model was developed to predict the dose rate, caused by 58Co which is formed by the activation of 58Ni, around the recirculation pipes in boiling water reactors (BWRs). The model is characterized by considering direct deposition of Ni ion on the nuclear fuel cladding surface and the geometrical contact probability for the ferrite formation reaction between deposited Ni (Co) and Fe2O3 on fuel cladding surface. This model showed the important role of the amount of Fe crud on the surface to reduce 58Co ion concentration in the reactor water. And the necessary Fe concentration in the feedwater for reducing the dose rate in the primary system was estimated as a function of the operating time. This model also enables the quantitative predictions of the effect of prefilming treatment of the feedwater heater tubes or another methods to reduce dose rate in an Fe crud suppressed BWR.

关键词： anomaly detection system BWR type reactors dose rates fuel cladding surface iron crud iron oxides nickel oxides prediction model quantity ratio recirculation pipes water chemistry

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STRUCTURAL MATERIAL anomaly detection system USING WATER CHEMISTRY DATA

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 1992年第11期29卷 1120-1126页

作者： ASAKURA, Y NAGASE, M UCHIDA, S OHSUMI, K HITACHI LTD HITACHI WORKS HITACHI 317 JAPAN

The concept of an advanced water chemistry diagnosis system for detection of anomalies and preventive maintenance of system components is proposed and put into a concrete form. Using the analogy to a medical inspection system, analyses of water chemistry change will make it possible to detect symptoms of anomalies in system components. Then, correlations between water chemistry change and anomaly occurrence in the components of the BWR primary cooling system are analyzed theoretically. These fragmentary correlations are organized and reduced to an algorithm for the on-line diagnosis system using on-line monitoring data, pH and conductivity. By using actual plant data, the on-line diagnosis model system is verified to be applicable for early and automatic finding of the anomaly cause and for timely supply of much diagnostic information to plant operators.

关键词： DIAGNOSTIC TECHNIQUES ONLINE systemS WATER CHEMISTRY CRUD BWR TYPE REACTORS anomaly detection system STRUCTURAL MATERIALS PH VALUES ELECTRIC CONDUCTIVITY CORRELATIONS PREDICTION MODEL

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HEADS: Hybrid Ensemble anomaly detection system for Internet-of-Things Networks 25th

HEADS: Hybrid Ensemble Anomaly Detection System for Internet...

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25th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Ahmad, Zeeshan Petrovski, Andrei Arifeen, Murshedul Khan, Adnan Shahid Shah, Syed Aziz Robert Gordon Univ Natl Subsea Ctr Aberdeen Scotland Univ Malaysia Sarawak Fac CS & IT Kota Samarahan Sarawak Malaysia Coventry Univ Res Ctr Intelligent Healthcare Coventry W Midlands England

ISBN: (纸本)9783031624940;9783031624957

The rapid expansion of Internet-of-Things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due to the presence of sensitive nature of data exchanged within IoT networks. To address these concerns, network-based anomaly detection systems play a crucial role in ensuring the security of IoT networks through continuous network traffic monitoring. However, despite significant efforts from researchers, these detection systems still suffer from lower accuracy in detecting new anomalies and often generate high false alarms. To this end, this study proposes an efficient Hybrid Ensemble learning-based anomaly detection system (HEADS) to secure an IoT network from all types of anomalies. The proposed solution is based on a novel hybrid approach to improve the voting strategy for ensemble learning. The ensemble prediction is assisted by a Random Forest-based model obtained through the best F1 score for each label through dataset subset selection. The efficiency of HEADS is evaluated using the publicly available CICIoT2023 dataset. The evaluation results demonstrate an F1 score of 99.75% and a false alarm rate of 0.038%. These observations signify an average 4% improvement in the F1 score while a reduction of 0.7% in the false alarm rate comparing other anomaly detection-based strategies.

关键词： anomaly detection system Ensemble-based learning Gradient Boosting Machine Internet-of-Things Machine Learning

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Sequence-based Analysis of Static Probe Instrumentation Data For a VMM-based anomaly detection system 3

Sequence-based Analysis of Static Probe Instrumentation Data...

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3rd IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud) / 2nd IEEE International Conference of Scalable and Smart Cloud (IEEE SSC)

作者： Paundu, Ady Wahyudi Okuda, Takeshi Kadobayashi, Youki Yamaguchi, Suguru Nara Inst Sci & Technol Internet Engn Lab 8916-5 Takayama Ikoma Nara 6300192 Japan

ISBN: (纸本)9781509009466

In this work, we propose a framework for a Virtual Machine Monitor (VMM)-based anomaly detection system (ADS). This framework uses a sequence-based analysis Hidden Markov Model (HMM) on static probe instrumentation data collected within the VMM. Long observations are split into multiple, uniformed-length, small sequences. The list of likelihood score of sequences in the new observation is compared to a reference list of likelihood scores created from a normal scenario dataset. Statistical distance values from both lists are used to predict the new observation anomaly status. We evaluated the effectiveness of the approach over multiple statistical distance measures and multiple sequence lengths. We also compared our sequence-based analysis results with a frequency-based analysis results that used the One-Class Support Vector Machine (OCSVM). The results show that the HMM sequence-based analysis can distinguish normal datasets from anomalous datasets better than the OC-SVM frequency-based analysis.

关键词： anomaly detection system virtual machine monitor hidden markov model static probe instrumentation statistical distance measure

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Hybrid deep neural network anomaly detection system for SCADA networks: Réseaux neuronaux profonds hybrides de détection d'anomalies pour les systèmes SCADA

Hybrid deep neural network anomaly detection system for SCAD...

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作者： Kabore, Raogo Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire

学位级别：博士

Les systèmes SCADA sont de plus en plus ciblés par les cyberattaques en raison de nombreuses vulnérabilités dans le matériel, les logiciels, les protocoles et la pile de communication. Ces systèmes utilisent aujourd'hui du matériel, des logiciels, des systèmes d'exploitation et des protocoles standard. De plus, les systèmes SCADA qui étaient auparavant isolés sont désormais interconnectés aux réseaux d'entreprise et à Internet, élargissant ainsi la surface d'attaque. Dans cette thèse, nous utilisons une approche deep learning pour proposer un réseau de neurones profonds hybride efficace pour la détection d'anomalies dans les systèmes SCADA. Les principales caractéristiques des données SCADA sont apprises de manière automatique et non supervisée, puis transmises à un classificateur supervisé afin de déterminer si ces données sont normales ou anormales, c'est-à-dire s'il y a une cyber-attaque ou non. Par la suite, en réponse au défi dû au temps d’entraînement élevé des modèles deep learning, nous avons proposé une approche distribuée de notre système de détection d'anomalies afin de réduire le temps d’entraînement de notre modèle. SCADA systems are more and more targeted by cyber-attacks because of many vulnerabilities inhardware, software, protocols and the communication stack. Those systems nowadays use standard hardware, software, operating systems and protocols. Furthermore, SCADA systems which used to be air-gaped are now interconnected to corporate networks and to the Internet, widening the attack *** this thesis, we are using a deep learning approach to propose an efficient hybrid deep neural network for anomaly detection in SCADA systems. The salient features of SCADA data are automatically and unsupervisingly learnt, and then fed to a supervised classifier in order to dertermine if those data are normal or abnormal, i.e if there is a cyber-attack or not. Afterwards, as a response to the challenge caused by high training time of deep learning models, we

关键词： Cybersécurité SCADA Systèmes de contrôle industriels Deep learning Machine learning Système de détection d’anomalies Cybersecurity SCADA Industrial control systems Deep leaning Machine learning anomaly detection system 004

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An anomaly detection system based on chi-square method with dynamic BIN algorithm

An anomaly detection system based on chi-square method with ...

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6th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA-2011

作者： Oshima, Shunsuke Ichimura, Yusuke Nakashima, Takuo Sueyoshi, Toshinori ICT Center for Learning Support Kumamoto National College of Technology 2627 Hirayama-Shinmachi Yatsushiro Kumamoto Japan Production Systems Engineering Course Kumamoto National College of Technology 2627 Hirayama-Shinmachi Yatsushiro Kumamoto Japan Dep. of Electronics and Intelligent Systems Engineering Tokai University 9-1-1 Toroku Kumamoto Japan Graduate School of Science and Technology Kumamoto University 2-39-1 Kurokami Kumamoto Japan

ISBN: (纸本)9780769545325

The statistic researches have been proposed to detect anomaly attacks using chi-square. In these researches, features such as the IP address and the port number are used as the probabilistic variables. The method based on multiple variables has not been proposed to aim to improve the accuracy of anomaly detection. If the number of packets increase, these packets are classified into BINs before the calculation of chi-square method. The classification method depends on the calculation parameters such as the window width and the number of BIN, and the packet distribution of night and day time. In addition, the classification method should be changed based on these parameters. In this paper, we propose the dynamic BIN method to classify the incoming packets automatically. We also propose the CSDM (Chi-square-based Space Division Method) to detect anomaly attacks using the dynamic BIN methods with multiple probabilistic variables. As the results of experiments using the source IP address, the destination port number, and the interval time deviation of arriving packets as the probabilistic variables, the proposed dynamic BIN realized the equal classification, which does not depends on the features of packets and the number of BIN. In addition, the dynamic BIN mechanism and CSDM method using two probabilistic variables could improve F-measure compared to the conventional method. © 2011 IEEE.

关键词： BIN CSDM method Chi-square-based space division method Computer crime DoS/DDoS detection Equations F-measure Feature extraction Heuristic algorithms IP networks IP networks Mathematical model Probabilistic logic anomaly detection system chi-square value d

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