Academic literature on the topic 'Host intrusion detection system'

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Journal articles on the topic "Host intrusion detection system"

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Sagade, Omkar Dhananjay, Atharva Dnyaneshwar Ubhe, and Prof Prerna Siddharth Patil. "Host Based-Intrusion Detection System (Anomaly Detection System)." IOSR Journal of Computer Engineering 26, no. 6 (2024): 08–13. http://dx.doi.org/10.9790/0661-2606010813.

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The objective of our statistical anomaly detection project is to develop an IDS that notices behavior which is based on observed action, detects abnormalities or possible intrusions. The system utilizes statistical analysis techniques, specifically features to discover anomalous patterns of activity, and receives input data in the form of stats.txt and event.txt files. The project's goal is to offer a dependable and efficient IDS that can assist in identifying and averting cyberattacks on networks and computer systems
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Memon, Saifullah. "Host-based intrusion detection system using Support Vector Machine." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 20, no. 1 (2022): 44–54. http://dx.doi.org/10.52584/qrj.2001.07.

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In line with the communication industry’s use of recent advancements in network technology to link remote areas of the world, attackers or intruders have stepped up their attacks on networking infrastructure. System administrators might deploy intrusion detection tools and systems to thwart such efforts. In recent years, the use of machine learning (ML) techniques in intrusion detection systems (IDSs) has increased. One of the most popular machine learning (ML) techniques for intrusion detection is the Support Vector Machine (SVM) due to its excellent generalization and capacity to escape the
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AmanYadav, Srivastav Abhishek, Tiwari Abhinandan, and Vir Singh Krishna. "Host-based Intrusion Detection System (HIDS)." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1043–49. https://doi.org/10.35940/ijeat.E9903.069520.

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This paper presents the data analysis and feature extraction of KDD dataset of 1999. This is used to detect signature based and anomaly attacks on a system. The process is supported by data extraction as well as data cleaning of the above mentioned data set. The dataset consists of 42 parameters and 58 services. These parameters are further filtered to extract useful attributes. Every attack in the dataset is labeled either with “normal” or into four different attack types i.e. denial-of-service, network probe, remote-to-local or user-to-root. Using different machine learning algor
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Tao, Ran, Li Yang, Lu Peng, and Bin Li. "A Host-Based Intrusion Detection System Using Architectural Features to Improve Sophisticated Denial-of-Service Attack Detections." International Journal of Information Security and Privacy 4, no. 1 (2010): 18–31. http://dx.doi.org/10.4018/jisp.2010010102.

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Application features like port numbers are used by Network-based Intrusion Detection Systems (NIDSs) to detect attacks coming from networks. System calls and the operating system related information are used by Host-based Intrusion Detection Systems (HIDSs) to detect intrusions toward a host. However, the relationship between hardware architecture events and Denial-of-Service (DoS) attacks has not been well revealed. When increasingly sophisticated intrusions emerge, some attacks are able to bypass both the application and the operating system level feature monitors. Therefore, a more effectiv
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Liu, Ming, Zhi Xue, Xianghua Xu, Changmin Zhong, and Jinjun Chen. "Host-Based Intrusion Detection System with System Calls." ACM Computing Surveys 51, no. 5 (2019): 1–36. http://dx.doi.org/10.1145/3214304.

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Sharma, Gaurav, and Anil Kumar Kapil. "INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR." Acta Informatica Malaysia 5, no. 2 (2021): 58–61. http://dx.doi.org/10.26480/aim.02.2021.58.61.

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Security becomes the main concern when the resources are shared over a network for many purposes. For ease of use and time saving several services offered by banks and other financial companies are accessible over mobile apps and computers connected with the Internet. Intrusion detection (ID) is the act of detecting actions that attempt to compromise the confidentiality, integrity, or availability of a shared resource over a network. Intrusion detection does not include the prevention of intrusions. A different solution is required for intrusion prevention. The major intrusion detection techni
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Prof. M. V. Desai, Priya B. Bhosle. "Intrusion Detection System with Deceptive Virtual Host." International Journal of Innovative Research in Computer and Communication Engineering 03, no. 07 (2015): 6432–41. http://dx.doi.org/10.15680/ijircce.2015.0307011.

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Kukushkina, Nadezhda, and Aleksey Novokhrestov. "Development of the laboratory bench for studying intrusion detection systems." Digital technology security, no. 4 (December 27, 2021): 37–53. http://dx.doi.org/10.17212/2782-2230-2021-4-37-53.

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The research object of this article is network-based and host-based intrusion detection systems. The aim of the study is to obtain an overview of intrusion detection systems, as well as to build a constructive version of a virtual laboratory bench intended for teaching students (studying the test characteristics of intrusion detection systems). The article provides a brief reference on intrusion detection systems, taking into account the classification by the method of monitoring and the technology of detecting attacks. Today, intrusion detection system is a necessary element of a comprehensiv
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Javidi, Mohammad Masoud. "Game Theory Approaches in Taxonomy of Intrusion Detection for MANETs." Computer Engineering and Applications Journal 4, no. 1 (2015): 31–42. http://dx.doi.org/10.18495/comengapp.v4i1.111.

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MANETs are self configuring networks that are formed by a set of wireless mobile nodes and have no fixed network infrastructure nor administrative support. Since transmission range of wireless network interfaces is limited, forwarding hosts may be needed. Each node in a wireless ad hoc network functions is as both a host and a router. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions so, security is a critical issue. Network security is usually provided in the three phases: intrusion prevention, intrusion detection and in
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Ratih Hafsarah Maharrani. "Enhancing Performance Using New Hybrid Intrusion Detection System." Proceeding of The International Conference on Economics and Business 1, no. 1 (2022): 13–15. http://dx.doi.org/10.55606/iceb.v1i1.101.

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Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the infor
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Dissertations / Theses on the topic "Host intrusion detection system"

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Molina, Jesus. "Evaluating host intrusion detection systems." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7697.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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McDonald, Kevin E. (Kevin Edward) 1978. "A lightweight real-time host-based intrusion detection system." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86677.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.<br>Includes bibliographical references (leaves 98-100).<br>by Kevin E. McDonald.<br>M.Eng.
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Yun, Ronald E. "Network defense-in-dept : evaluating host-based intrusion detection systems /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA395808.

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Payne, Bryan D. "Improving host-based computer security using secure active monitoring and memory analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34852.

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Thirty years ago, research in designing operating systems to defeat malicious software was very popular. The primary technique was to design and implement a small security kernel that could provide security assurances to the rest of the system. However, as operating systems grew in size throughout the 1980's and 1990's, research into security kernels slowly waned. From a security perspective, the story was bleak. Providing security to one of these large operating systems typically required running software within that operating system. This weak security foundation made it relatively easy
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Uppströmer, Viktor, and Henning Råberg. "Detecting Lateral Movement in Microsoft Active Directory Log Files : A supervised machine learning approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18337.

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Cyberattacker utgör ett stort hot för dagens företag och organisationer, med engenomsnittlig kostnad för ett intrång på ca 3,86 miljoner USD. För att minimera kostnaden av ett intrång är det viktigt att detektera intrånget i ett så tidigt stadium som möjligt. Avancerande långvariga hot (APT) är en sofistikerad cyberattack som har en lång närvaro i offrets nätverk. Efter attackerarens första intrång kommer fokuset av attacken skifta till att få kontroll över så många enheter som möjligt på nätverket. Detta steg kallas för lateral rörelse och är ett av de mest kritiska stegen i en APT. Syftet me
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Jacoby, Grant Arthur. "Battery-Based Intrusion Detection." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/27092.

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This dissertation proposes an efficacious early warning system via a mobile host-based form of intrusion detection that can alert security administrators to protect their corporate network(s) by a novel technique that operates through the implementation of smart battery-based intrusion detection (B-bid) on mobile devices, such as PDAs, HandPCs and smart-phones by correlating attacks with their impact on device power consumption. A host intrusion detection engine (HIDE) monitors power behavior to detect potential intrusions by noting consumption irregularities and serves like a sensor to trigge
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Abimbola, Abiola. "NetHostSensor Enhancing intrusion detection via an active target host." Thesis, Edinburgh Napier University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506365.

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Sainani, Varsha. "Hybrid Layered Intrusion Detection System." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.

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The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intru
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Maharjan, Nadim, and Paria Moazzemi. "Telemetry Network Intrusion Detection System." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581632.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>Telemetry systems are migrating from links to networks. Security solutions that simply encrypt radio links no longer protect the network of Test Articles or the networks that support them. The use of network telemetry is dramatically expanding and new risks and vulnerabilities are challenging issues for telemetry networks. Most of these vulnerabilities are silent in nature and c
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Sonbul, O., M. Byamukama, S. Alzebda, and A. N. Kalashnikov. "Autonomous intrusion detection information system." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/28777.

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Abstract – Implementation of security arrangements for insecure premises, for example, for temporary exhibitions or infrequent public events, usually results in substantial security personnel costs which can be potentially reduced by employing an easily installable ad hoc intrusion detection information system. In the paper we described the architecture, design and experimental results for a fully prototyped information system that utilizes ultrasonic sensors operating in the pulse echo mode for the perimeter control and ZigBee transceivers for wireless networking. The system consists of inexp
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Books on the topic "Host intrusion detection system"

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Tsukerman, Emmanuel. Designing a Machine Learning Intrusion Detection System. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6591-8.

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Ingram, Dennis J. Autonomous agents for distributed intrusion detection in a multi-host environment. Naval Postgraduate School, 1999.

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Herrero, Alvaro. Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System. Springer Berlin Heidelberg, 2011.

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Barrus, Joseph D. Intrusion detection in real-time in a multi-node, multi-host environment. Naval Postgraduate School, 1997.

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Toxen, Bob. Real-world Linux security: Intrusion, prevention, detection, and recovery. Prentice Hall, 2001.

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Spenneberg, Ralf. Intrusion Detection fu r Linux-Server: Mit Open-Source-Tools Angriffe erkennen und analysieren ; mit einer Einfu hrung in die digitale Forensik. Markt-und-Technik-Verl., 2003.

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Lhotsky, Brad. Instant OSSEC Host-Based Intrusion Detection System. Packt Publishing, Limited, 2013.

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Instant Ossec Host-Based Intrusion Detection System. Packt Publishing, Limited, 2013.

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Network Defense-in-Depth: Evaluating Host-Based Intrusion Detection Systems. Storming Media, 2001.

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OSSEC Host-Based Intrusion Detection Guide. Elsevier, 2008. http://dx.doi.org/10.1016/b978-1-59749-240-9.x0001-4.

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Book chapters on the topic "Host intrusion detection system"

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Deshpande, Prachi S., Subhash C. Sharma, and Sateesh K. Peddoju. "A Host-Based Intrusion Detection System." In Studies in Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6089-3_2.

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Gautam, Sunil Kumar, and Hari Om. "Host-Based Intrusion Detection Using Statistical Approaches." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2695-6_40.

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Gayathri Harshitha, Ch, M. Kameswara Rao, and P. Neelesh Kumar. "A Novel Mechanism for Host-Based Intrusion Detection System." In First International Conference on Sustainable Technologies for Computational Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0029-9_42.

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Prasad, Vivek Kumar, A. Raval Abhishek, and Madhuri Bhavsar. "HIDSC2: Host-Based Intrusion Detection System in Cloud Computing." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4960-9_6.

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Yedukondalu, G., J. Anand Chandulal, and M. Srinivasa Rao. "Host-Based Intrusion Detection System Using File Signature Technique." In Innovations in Computer Science and Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3818-1_25.

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Li, Hao, Zehui Wang, Shang Shang, et al. "Joint Spatial-Temporal Representation for Host Intrusion Detection System." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97632-2_18.

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Páez, Rafael, Joan Tomàs-Buliart, Jordi Forné, and Miguel Soriano. "Securing Agents against Malicious Host in an Intrusion Detection System." In Critical Information Infrastructures Security. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89173-4_9.

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Chawla, Ashima, Brian Lee, Sheila Fallon, and Paul Jacob. "Host Based Intrusion Detection System with Combined CNN/RNN Model." In ECML PKDD 2018 Workshops. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13453-2_12.

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El Jaouhari, Saad, Andrei Vavilov, Julia Soloveva, Alexandru Archip, and Nour El Madhoun. "MIDPS: A Multi-agent Host Intrusion Detection and Prevention System." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87772-8_22.

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Can, Ozgu, Murat Osman Unalir, Emine Sezer, Okan Bursa, and Batuhan Erdogdu. "An Ontology Based Approach for Host Intrusion Detection Systems." In Metadata and Semantic Research. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70863-8_8.

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Conference papers on the topic "Host intrusion detection system"

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Alla, Omayma, and Stéphane Mocanu. "Host-Based Intrusion Detection for Industrial Control Systems." In 2025 IEEE 11th International Conference on Network Softwarization (NetSoft). IEEE, 2025. https://doi.org/10.1109/netsoft64993.2025.11080639.

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Taha, Ahmed, and Pabitra Kumar Roy. "Improvising Host-Based Intrusion Detection Systems by Integrating Open-Source Tools." In 2024 4th Intelligent Cybersecurity Conference (ICSC). IEEE, 2024. https://doi.org/10.1109/icsc63108.2024.10895713.

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Vyas, Abhishek, Po-Ching Lin, and Ren-Hung Hwang. "FedSplit-HIDS: A Host-Based Intrusion Detection Framework for Heterogeneous Industrial IoT Environments." In 2024 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, 2024. https://doi.org/10.1109/ants63515.2024.10898417.

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Nam, Yujin, Rachel King, Quinn Burke, Minxuan Zhou, Patrick McDaniel, and Tajana Rosing. "Efficient Host Intrusion Detection using Hyperdimensional Computing." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825247.

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Vazquez, B. J., K. D. Boomer, T. J. Venetz, and J. K. Engeman. "241-AY-102 Leak Detection Pit Drain Line Inspection." In CORROSION 2015. NACE International, 2015. https://doi.org/10.5006/c2015-06198.

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Abstract On August 1,2012 a routine visual inspection showed the presence of unexpected material on the floor of the secondary liner in Tank 241-AY-102 (AY-102). In October 2012, it was determined that waste had leaked into the annulus of AY-102. As a result, an extensive review of Tank AY-102 was conducted quickly triggering increased inspection and monitoring of the tank; however, the precise cause and location of the leak could not be determined. At the same time the Tank AY-102 leak detection pit (LDP) was accumulating water through the drain system external to the secondary tank liner. Li
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Praveen, Siriki Jyothi, Vulsa Prahladh, and B. S. Kiruthika Devi. "Host-based Intrusion Detection Framework for IoT using Machine Learning." In 2025 7th International Conference on Inventive Material Science and Applications (ICIMA). IEEE, 2025. https://doi.org/10.1109/icima64861.2025.11074165.

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Vokorokos, L., and A. Balaz. "Host-based intrusion detection system." In 2010 IEEE 14th International Conference on Intelligent Engineering Systems (INES 2010). IEEE, 2010. http://dx.doi.org/10.1109/ines.2010.5483815.

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Sayed, Mahmoud Abdelhafeez, and Mostafa Taha. "Oblivious Intrusion Detection System." In 2022 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 2022. http://dx.doi.org/10.1109/host54066.2022.9840140.

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Liu, Jiannan, Kun Xiao, Lei Luo, Yun Li, and Lirong Chen. "An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection." In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2020. http://dx.doi.org/10.1109/qrs51102.2020.00028.

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Castanhel, Gabriel Ruschel, Tiago Heinrich, Fabrício Ceschin, and Carlos A. Maziero. "Sliding Window: The Impact of Trace Size in Anomaly Detection System for Containers Through Machine Learning." In XVIII Escola Regional de Redes de Computadores. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/errc.2020.15203.

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Anomaly intrusion detection in Host-based Intrusion Detection System (HIDS) is a process intended to monitor operations on a host to identify behaviors that differ from a “normal ” system behavior. System call based HIDS uses traces of calls to represent the behavior of a system. Due to the volume of data generated by applications and the operating system, sliding windows are applied in order to asses an online environment, allowing intrusions to be detected in real time while being still executed. The respective study explores the impact that the size of the observation window has on Machine
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Reports on the topic "Host intrusion detection system"

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Marsh, Kennedy, Piyush Sabharwall, and Palash Bhowmik. Integrating PCTRAN with AI-Driven Host-Intrusion Detection and Secured Container Systems for Advanced Malware Analysis. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2448404.

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DRAELOS, TIMOTHY J., MICHAEL J. COLLINS, DAVID P. DUGGAN, EDWARD V. THOMAS, and DONALD WUNSCH. Experiments on Adaptive Techniques for Host-Based Intrusion Detection. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/787645.

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Schmid, Matthew. Computing Platform Coverage via Light Host-based Intrusion Detection. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada402859.

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Lundy, Philip A., George W. Pittman, and Heinz J. Pletsch. Intrusion Detection System Methodology Investigation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198210.

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Zage, Dolores M., and Wayne M. Zage. Intrusion Detection System Visualization of Network Alerts. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532723.

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Singh, Abhinav. Distributed Intrusion Detection System for Modbus Protocol. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-562.

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Speed, Ann. Intrusion Detection System Alarm Station Operator Interface Improvements. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1762330.

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Heady, R., G. Luger, A. Maccabe, and M. Servilla. The architecture of a network level intrusion detection system. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/425295.

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Skormin, Victor A. Anomaly-Based Intrusion Detection Systems Utilizing System Call Data. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada568124.

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Chen, Yan. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1108982.

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