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Two-Stage Orthogonal Network Incident Detection for the Adaptive Coordination with SMTP Proxy
[chapter]
2003
Lecture Notes in Computer Science
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In this paper we present the adaptive detection and coordination system, two-stage detection, which consists of anomaly and misuse detection combined by lightweight neural networks to synchronize with ...
Another feature of our model is to set delay line in the protection system for IDS to synchronize with proxy server for more effective data control. ...
They use artificial neural networks for anomaly detection in order to detect unseen behavior and for misuse detection in order to detect variations of known attacks. ...
doi:10.1007/978-3-540-45215-7_37
fatcat:dk3iihpoyfemrf35zlraxhicoq
A Two Stage Intrusion Detection System for Industrial Control Networks Based on Ethernet/IP
2019
Electronics
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Furthermore, the anomaly detection model, using a one class support vector machine (OCSVM), is able to detect malicious control instructions by analyzing the key field in Ethernet/IP packets. ...
An improved intrusion detection system (IDS), which is strongly correlated to specific industrial scenarios, is necessary for modern ICS. ...
Acknowledgments: The authors would like to thank and appreciate the support of all the scholars for helping us with this piece of work and the problems encountered. ...
doi:10.3390/electronics8121545
fatcat:2x2lw7ddgnbadod3yp6ze2c2yy
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2008.03343v1 fatcat:3nfhytgo3fa7xjvxlkdo2l3kxm
With this goal in mind, a promising research field emerged focusing on creating and managing fingerprints that model the behavior of both the device actions and its components. ...
In the current network-based computing world, where the number of interconnected devices grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at the same rate. ...
ACKNOWLEDGMENT This work has been partially supported by the Swiss Federal Office for Defence Procurement (armasuisse) (project code Aramis R-3210/047-31), and the Irish Research Council, under the government ...
arXiv:2008.03343v2
fatcat:ujj7ka2txvfj7fbawyhloloac4
Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security
2024
Journal of Electrical Systems
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The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. ...
Key findings indicate that while the model exhibits potential in anomaly detection, as reflected by the progressive increase in triggered alerts and policy changes, its performance metrics, such as precision ...
On the other hand, frequent changes can also lead to instability and unpredictability in the network's behavior, disrupting users and systems relying on consistent network policies. ...
doi:10.52783/jes.639
fatcat:vx2miuuvdrbevjspukymce3th4
Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods
2018
International Journal of Engineering & Technology
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IDS plays a very important role for analyzing the network passage, also it assumes a key part to analyze the system activity log and each log is portrayed by huge arrangement of highlights and it requires ...
Since it isn't in fact possible to assemble a framework without any vulnerability, Intrusion Detection System (IDS), which can successfully distinguish Intrusion, gets to have pulled in consideration. ...
Abraham [14] develops an Intrusion detection system for misuses and anomaly detection using characteristic of meta rules and assosiciation rules, It also involves data mining to create report about network ...
doi:10.14419/ijet.v7i2.7.10597
fatcat:gewfywpxa5aqxaymjppjljri54
Network Intrusion Detection Model based on Fuzzy Support Vector Machine
2013
Journal of Networks
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In this paper, we concentrate on how to automatically detect the network intrusion behavior utilizing fuzzy support vector machine. ...
Network intrusion detection is of great importance in the research field of information security in computer networks. ...
The efficiency of Intrusion detection system relies on chosen system characteristics to monitor. Then, each intrusion detection system can find the intrusion behavior in time. ...
doi:10.4304/jnw.8.6.1387-1394
fatcat:ya6qdsxu4nfhfp2zuxggc4rcja
An Ecological Approach to Anomaly Detection: The EIA Model
[chapter]
2012
Lecture Notes in Computer Science
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The presented work proposes a new approach for anomaly detection. This approach is based on changes in a population of evolving agents under stress. ...
To verify this assertion, experiments based on Network Intrussion Detection Systems are presented. ...
Anomaly detection is a solution to the problem of classification that consists of segregating objects in a set of different classes. ...
doi:10.1007/978-3-642-33757-4_18
fatcat:souzurex35hmhnopskqr2za2hy
Enabling intrusion detection systems with dueling double deep Q-learning
2022
Digital Transformation and Society
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To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM ...
supervised learning, deep learning and RL.Originality/valueThe research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from ...
When detected, the system sends a signal to the administrator for further investigation. (2) Host Intrusion Detection System (HIDS): HIDS runs on hosts or devices on the network and examines incoming and ...
doi:10.1108/dts-05-2022-0016
fatcat:z2ccnqiivravzh7iwdjzusuala
Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
2024
Applied Sciences
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In this research, the model serves a dual purpose: detecting anomalies in industrial processes and replicating the machine's operational behavior with high fidelity in terms of a simulated torque signal ...
When anomalous behaviors are detected, their patterns are utilized to generate anomalous events, contributing to the enhancement of deep neural network model training. ...
Various machine learning (ML) techniques are also used to detect anomalies in machine systems. ...
doi:10.3390/app14020800
fatcat:7z3edgqrmzd6ziq3ar37jywcay
An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles' CAN Bus Using Machine Learning
2019
IEEE Access
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This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. ...
Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. ...
[19] proposed an intrusion detection system based on a long short-term memory (LSTM) recurrent neural network for CAN bus traffic. ...
doi:10.1109/access.2019.2937576
fatcat:n6jdp7mqjvh4bbbsp5bno6iyzi
Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems
2022
Water
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The methodology proposed in the article includes combining methods of machine learning and visual data analysis to improve the detection of attacks and anomalies in water treatment systems. ...
In addition, Change_Measure metric parameters were selected to ensure the detection of attacks and anomalies by using visual data analysis. ...
The paper proposes a visualization-assisted approach to anomaly and attack detection in a water treatment system constructed on the basis of a wireless sensor network. ...
doi:10.3390/w14152342
fatcat:vwa7zmpfojarxisggbtpjnnfli
Anomaly Detection Model Over Blockchain Electronic Transactions
2019
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)
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In this work, we propose a new model for anomaly detection over bitcoin electronic transactions. ...
We used in our proposal two machine learning algorithms, namely the One Class Support Vector Machines (OCSVM) algorithm to detect outliers and the K-Means algorithm in order to group the similar outliers ...
One-Class SVM is used in step 1 for behavioral analysis to detect the outlier's values of bitcoin transactions. ...
doi:10.1109/iwcmc.2019.8766765
dblp:conf/iwcmc/SayadiRC19
fatcat:zmelhhqo7bbztcdgntdezdm6te
Abnormal Detection of Wireless Power Terminals in Untrusted environment Based on Double Hidden Markov Model1
2020
IEEE Access
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The experiment results indicate that the intrusion detection system using proposed double HMM can effectively detect the terminal's abnormal behavior and identify the network attack behavior for an extended ...
in intrusion detection systems using a single HMM. ...
Double HMM for abnormal behavior detection. ...
doi:10.1109/access.2020.3040856
fatcat:jofwclpoanctfbz6ijdoc7fzem
Guest Editorial: Data-Driven Management of Complex Systems Through Plant-Wide Performance Supervision
2021
IEEE Transactions on Industrial Informatics
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To improve the performance of the classic archetypal analysis methods for anomaly detection tasks on nonconvex data sets, in "Anon-convex archetypal analysis for one-class classification based anomaly ...
detection in cyber-physical systems [item 9) in the Appendix]," the authors proposed a nonconvex archetypal analysis approach to one-class classification, which uses the nonconvex hull of normal behavior ...
doi:10.1109/tii.2020.3023259
fatcat:2x44ydldbreqdcyejz5jui7q24
Development Machine Learning Techniques to Enhance Cyber Security Algorithms. (Dept. E)
2021
MEJ Mansoura Engineering Journal
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Intrusion detection has become a necessary component for building network security to detect abnormal use of the system by monitoring and analyzing network behavior to detect an attack. ...
[13] Proposed a DDoS detection system using a set of classification algorithms: Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), Random forest) controlled by a fuzzy logic system in Apache ...
The corresponding author is responsible for ensuring that the descriptions are accurate and agreed upon by all authors. . ...
doi:10.21608/bfemu.2021.206401
fatcat:arolet7mvzbenhu7oeil4ex2le
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