Relevant bibliographies by topics / Intrusion detection and analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intrusion detection and analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Intrusion detection and analysis"

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Li, You Guo. "Analysis of the Snort Building Code Based on IDS." Applied Mechanics and Materials 543-547 (March 2014): 2965–68. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2965.

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Intrusion Detection system (IDS) is a important element of Defense in Depth,which is able to capture all data in the LAN and analyse them for finding intrusional behavior. This paper presents conception of intrusion detection system.Snort that is a network based intrusion detection tool is analyzed,from the aspect of system structure and collectivity flow.Finally,snort base intrusion detection system is constructed and validated by our experiment.The result proves that the intrusional behavior isdetected effectively by using the system.
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T, Krishnakaarthik. "Intrusion Detection and Vulnerability Analysis with Temporal Relationalship." International Journal of Psychosocial Rehabilitation 23, no. 4 (2019): 1205–16. http://dx.doi.org/10.37200/ijpr/v23i4/pr190447.

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Simavoryan, Simon Zhorzhevich, Arsen Rafikovich Simonyan, Georgii Aleksandrovich Popov, and Elena Ivanovna Ulitina. "The procedure of intrusions detection in information security systems based on the use of neural networks." Программные системы и вычислительные методы, no. 3 (March 2020): 1–9. http://dx.doi.org/10.7256/2454-0714.2020.3.33734.

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The subject of the research is the problem of identifying and countering intrusions (attacks) in information security systems (ISS) based on the system-conceptual approach, developed within the framework of the RFBR funded project No. 19-01-00383. The object of the research is neural networks and information security systems (ISS) of automated data processing systems (ADPS). The authors proceed from the basic conceptual requirements for intrusion detection systems - adaptability, learnability and manageability. The developed intrusion detection procedure considers both internal and external th
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Gu, Yue Sheng, Hong Yu Feng, and Jian Ping Wang. "Analysis of Intrusion Detection System." Key Engineering Materials 460-461 (January 2011): 451–54. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.451.

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Intrusion detection system is an important device of information security. This article describes intrusion detection technology concepts, classifications and universal intrusion detection model, and analysis of the intrusion detection systems weaknesses and limitations. Finally, some directions for future research are addressed.
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Chueh, Hao-En, Shun-Chuan Ho, Shih-Peng Chang, and Ping-Yu Hsu. "Online Intrusion Behaviors: Sequences and Time Intervals." Social Behavior and Personality: an international journal 38, no. 10 (2010): 1307–12. http://dx.doi.org/10.2224/sbp.2010.38.10.1307.

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In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder b
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A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection m
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Song, Youngrok, Sangwon Hyun, and Yun-Gyung Cheong. "Analysis of Autoencoders for Network Intrusion Detection." Sensors 21, no. 13 (2021): 4294. http://dx.doi.org/10.3390/s21134294.

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As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e., zero-day attacks) and also alleviate the burden of the laborious labeling task. Although the autoencoders are effective in detecting unknown types of attacks, it takes tremendous time and effort to find the optimal model architecture and hy
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Surasit Songma, Witcha Chimphlee, Kiattisak Maichalernnukul, and Parinya Sanguansat. "Intrusion Detection through Rule Induction Analysis." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 11 (2013): 187–94. http://dx.doi.org/10.4156/aiss.vol5.issue11.23.

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ZHONG, SHI, TAGHI M. KHOSHGOFTAAR, and NAEEM SELIYA. "CLUSTERING-BASED NETWORK INTRUSION DETECTION." International Journal of Reliability, Quality and Safety Engineering 14, no. 02 (2007): 169–87. http://dx.doi.org/10.1142/s0218539307002568.

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Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised cl
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Lee, Se-Yul, and Yong-Soo Kim. "Design and Analysis of Probe Detection Systems for TCP Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (2004): 369–72. http://dx.doi.org/10.20965/jaciii.2004.p0369.

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Advanced computer network technology enables the connectivity of computers in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and cannot detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We propose a n
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Dissertations / Theses on the topic "Intrusion detection and analysis"

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Alessandri, Dominique. "Attack-class-based analysis of intrusion detection systems." Thesis, University of Newcastle Upon Tyne, 2004. http://hdl.handle.net/10443/2094.

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Designers of intrusion detection systems are often faced with the problem that their design fails to meet the specification because the actual implementation is not able to detect attacks as required. This work aims at addressing such shoncomings at an early stage of the design process. The proposed method provides guidance to intrusion detection systems designers by predicting whether or not a given design will be able to detect certain classes of attacks. Our method achieves this by introducing a classification of attacks and a description framework for intrusion detection systems. The attac
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Webster, Seth E. (Seth Emerson) 1975. "The development and analysis of intrusion detection algorithms." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50439.

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Hendry, Gilbert R. "Applicability of clustering to cyber intrusion detection /." Online version of thesis, 2007. http://hdl.handle.net/1850/4777.

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Schiavo, Sandra Jean. "An intrusion-detection tutoring system using means-ends analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA294283.

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Cetin, Burak. "Wireless Network Intrusion Detection and Analysis using Federated Learning." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729.

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Zomlot, Loai M. M. "Handling uncertainty in intrusion analysis." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17603.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Xinming Ou<br>Intrusion analysis, i.e., the process of combing through Intrusion Detection System (IDS) alerts and audit logs to identify true successful and attempted attacks, remains a difficult problem in practical network security defense. The primary cause of this problem is the high false positive rate in IDS system sensors used to detect malicious activity. This high false positive rate is attributed to an inability to differentiate nearly certain attacks from those that are merely possible. This inefficacy has
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Satam, Pratik. "An Anomaly Behavior Analysis Intrusion Detection System for Wireless Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595654.

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Wireless networks have become ubiquitous, where a wide range of mobile devices are connected to a larger network like the Internet via wireless communications. One widely used wireless communication standard is the IEEE 802.11 protocol, popularly called Wi-Fi. Over the years, the 802.11 has been upgraded to different versions. But most of these upgrades have been focused on the improvement of the throughput of the protocol and not enhancing the security of the protocol, thus leaving the protocol vulnerable to attacks. The goal of this research is to develop and implement an intrusion detection
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Borek, Martin. "Intrusion Detection System for Android : Linux Kernel System Salls Analysis." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222382.

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Smartphones provide access to a plethora of private information potentially leading to financial and personal hardship, hence they need to be well protected. With new Android malware obfuscation and evading techniques, including encrypted and downloaded malicious code, current protection approaches using static analysis are becoming less effective. A dynamic solution is needed that protects Android phones in real time. System calls have previously been researched as an effective method for Android dynamic analysis. However, these previous studies concentrated on analysing system calls captured
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Sakthivelmurugan, Sakthiyuvaraja. "An empirical approach to modeling uncertainty in intrusion analysis." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2337.

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Mitchel, Robert Raymondl III. "Design and Analysis of Intrusion Detection Protocols in Cyber Physical Systems." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19338.

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In this dissertation research we aim to design and validate intrusion detection system (IDS) protocols for a cyber physical system (CPS) comprising sensors, actuators, control units, and physical objects for controlling and protecting physical infrastructures.<br />The design part includes host IDS, system IDS and IDS response designs. The validation part includes a novel model-based analysis methodology with simulation validation. Our objective is to maximize the CPS reliability or lifetime in the presence of malicious nodes performing attacks which can cause security failures. Our host IDS d
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Books on the topic "Intrusion detection and analysis"

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Trost, Ryan. Practical intrusion analysis: Prevention and detection for the twenty-first century. Addison-Wesley, 2009.

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Davide, Balzarotti, Maier Gregor, and SpringerLink (Online service), eds. Recent Advances in Intrusion Detection: 14th International Symposium, RAID 2011, Menlo Park, CA, USA, September 20-21, 2011. Proceedings. Springer Berlin Heidelberg, 2011.

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Intrusion detection. Macmillan Technical Publishing, 2000.

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Sengupta, Nandita, and Jaya Sil. Intrusion Detection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6.

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Judy, Novak, ed. Network intrusion detection. 3rd ed. New Riders Pub., 2002.

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Intrusion detection systems. 2nd ed. Butterworths, 1988.

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Roberto, Di Pietro, and SpringerLink (Online service), eds. Intrusion Detection Systems. Springer-Verlag US, 2008.

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Sobirey, Michael. Datenschutzorientiertes Intrusion Detection. Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-86850-3.

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Beale, Jay. Snort 2.0 intrusion detection. Syngress, 2003.

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C, Foster James, ed. Snort 2.0 intrusion detection. Syngress, 2003.

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Book chapters on the topic "Intrusion detection and analysis"

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Debar, Hervé, and Jouni Viinikka. "Intrusion Detection: Introduction to Intrusion Detection and Security Information Management." In Foundations of Security Analysis and Design III. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554578_7.

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Gunetti, Daniele, and Giancarlo Ruffo. "Intrusion Detection through Behavioral Data." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_32.

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Shin, Moon Sun, and Kyeong Ja Jeong. "Alert Correlation Analysis in Intrusion Detection." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_114.

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Rawat, Sanjay, and Challa S. Sastry. "Network Intrusion Detection Using Wavelet Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30561-3_24.

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Laskov, Pavel, Patrick Düssel, Christin Schäfer, and Konrad Rieck. "Learning Intrusion Detection: Supervised or Unsupervised?" In Image Analysis and Processing – ICIAP 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_6.

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Bostami, Biozid, and Mohiuddin Ahmed. "Intrusion Detection for Big Data." In Data Analytics. CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-15.

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Sridevi, S., and R. Anandan. "Analysis of Classification-Based Intrusion-Detection Techniques." In Information Security and Optimization. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003045854-9.

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Zhi, Zhang. "IPv6 Network Intrusion Detection Protocol Analysis Techniques." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33030-8_15.

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Massicotte, Frédéric. "Packet Space Analysis of Intrusion Detection Signatures." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04342-0_26.

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Roy, Sanjiban Sekhar, Omsai Jadhav, Saptarshi Chakraborty, Swapnil Saurav, and Madhu Viswanatham. "Multicriteria Decision Analysis for Intrusion Detection Data." In Advances in Intelligent Systems and Computing. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_79.

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Conference papers on the topic "Intrusion detection and analysis"

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Hogo, Mofreh A. "Temporal analysis of intrusion detection." In 2014 International Carnahan Conference on Security Technology (ICCST). IEEE, 2014. http://dx.doi.org/10.1109/ccst.2014.6987012.

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Yu-Xin Wei and Mu-Qing Wu. "KFDA-waveletcluster based intrusion detection technology." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421766.

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Titorenko, Aleksey A., and Alexey A. Frolov. "Analysis of modern intrusion detection system." In 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2018. http://dx.doi.org/10.1109/eiconrus.2018.8317049.

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Ahmad, Iftikhar, Azween B. Abdullah, and Abdullah S. Alghamdi. "Comparative Analysis of Intrusion Detection Approaches." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.112.

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Nagaraja, Arun, Uma Boregowda, and Radhakrishna Vangipuram. "Regression analysis for network intrusion detection." In DATA'21: International Conference on Data Science, E-learning and Information Systems 2021. ACM, 2021. http://dx.doi.org/10.1145/3460620.3460751.

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TERAI, ASUKA, TATSUYA CHIBA, HIDEYUKI SHINTANI, SHOYA KOJIMA, SHINGO ABE, and ICHIRO KOSHIJIMA. "INTRUSION DETECTION METHOD FOR INDUSTRIAL CONTROL SYSTEMS USING SINGULAR SPECTRUM ANALYSIS." In RISK ANALYSIS 2018. WIT Press, 2018. http://dx.doi.org/10.2495/risk180171.

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Ahmad, Umair, Hira Asim, Malik Tahir Hassan, and Sheraz Naseer. "Analysis of Classification Techniques for Intrusion Detection." In 2019 International Conference on Innovative Computing (ICIC). IEEE, 2019. http://dx.doi.org/10.1109/icic48496.2019.8966675.

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Brandao, Andre, and Petia Georgieva. "Log Files Analysis For Network Intrusion Detection." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199976.

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Sahu, Santosh Kumar, Sauravranjan Sarangi, and Sanjaya Kumar Jena. "A detail analysis on intrusion detection datasets." In 2014 IEEE International Advance Computing Conference (IACC). IEEE, 2014. http://dx.doi.org/10.1109/iadcc.2014.6779523.

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"Application of clustering analysis in Intrusion Detection." In The 3rd International Conference on Intelligent Energy and Power Systems. Clausius Scientific Press Inc., 2017. http://dx.doi.org/10.23977/ieps.2017.1021.

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Reports on the topic "Intrusion detection and analysis"

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Etoty, Renee E., and Robert F. Erbacher. A Survey of Visualization Tools Assessed for Anomaly-Based Intrusion Detection Analysis. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada601590.

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Skormin, Victor A. High Order Non-Stationary Markov Models and Anomaly Propagation Analysis in Intrusion Detection System (IDS). Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada464796.

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Tomko, Albert A., Christian J. Rieser, Louis H. Buell, David R. Zaret, and William M. Turner. Wireless Intrusion Detection. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada466332.

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Bace, Rebecca, and Peter Mell. Intrusion detection systems. National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.sp.800-31.

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Giffin, Jonathan T., David Dagon, Somesh Jha, Wenke Lee, and Barton P. Miller. Environment-Sensitive Intrusion Detection. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada448428.

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Cowles, Robert D. Intrusion Detection and Physics. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/10009.

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Rodriguez, J. R., J. C. Matter, and B. Dry. Interior intrusion detection systems. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5977693.

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Wood, M., and M. Erlinger. Intrusion Detection Message Exchange Requirements. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4766.

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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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Chang, Raymond J., Richard E. Harang, and Garrett S. Payer. Extremely Lightweight Intrusion Detection (ELIDe). Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada592893.

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