CONSTRUCTION OF ATTACK DETECTION SYSTEMS IN INFORMATION NETWORKS ON NEURAL NETWORK STRUCTURES
DOI:
https://doi.org/10.28925/2663-4023.2020.10.169183Keywords:
cyberspace, attack, neural network, information network, attack detection systems, data mining methods, training base, fuzzy logic, cauterization, cyber defenseAbstract
Systems for detecting network intrusions and detecting signs of attacks on information systems have long been used as one of the necessary lines of defense of information systems. Today, intrusion and attack detection systems are usually software or hardware-software solutions that automate the process of monitoring events occurring in an information system or network, as well as independently analyze these events in search of signs of security problems. As the number of different types and ways of organizing unauthorized intrusions into foreign networks has increased significantly in recent years, attack detection systems (ATS) have become a necessary component of the security infrastructure of most organizations.
The article proposes a software prototype of a network attack detection system based on selected methods of data mining and neural network structures. The conducted experimental researches confirm efficiency of the created model of detection for protection of an information network. Experiments with a software prototype showed high quality detection of network attacks based on neural network structures and methods of intelligent data distribution. The state of protection of information systems to counter cyber attacks is analyzed, which made it possible to draw conclusions that to ensure the security of cyberspace it is necessary to implement a set of systems and protection mechanisms, namely systems: delimitation of user access; firewall; cryptographic protection of information; virtual private networks; anti-virus protection of ITS elements; detection and prevention of intrusions; authentication, authorization and audit; data loss prevention; security and event management; security management.
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References
Hacker attacks in Ukraine. [Electronic resource] // Wikipedia: [site]. Kyiv, 2020. https://glavcom.ua/topics/ rosijskikhakeru.html.
Analysis and classification of methods for detecting network attacks / AA Branitsky, AV Kotenko // Tr. SPIIRAN. 2016. № 2 (45). Pp. 207-244.
Modern methods of detecting anomalies in intrusion detection systems / О.М. Kolodchak // Bulletin of the National University "Lviv Polytechnic". Computer systems and networks. 2012. № 745. pp. 98–104.
Research of methods of detection of intrusions into telecommunication systems and networks / DO Danilenko, OA Smirnov, EV Meleshko // Weapons systems and military equipment. H .: Hark. nat. University of the Air Force. I. Kozheduba, 2012. № 1. S. 92-100.
The State of the Art in Intrusion Prevention and Detection [Electronic resourse] / Al-Sakib Khan Pathan. New York: Auerbach Publications, 2014.
Development of a model of intelligent recognition of anomalies and cyberattacks using logical procedures based on feature matrix coatings / G. Beketova, B. Akhmetov, O. Korchenko, V. Lakhno // Information Security. K: NAU, 2016. T. 22, № 3. S. 242-254.
Review of attack detection systems in network traffic / KM Nosenko, OI Pivtorak, TA Likhouzova // Adaptive automatic control systems. K: NTUU KPI, 2014. № 1 (24). Pp. 67-75.
Analysis of Host-Based and Network-Based Intrusion Detection System / Amrit Pal Singh, Manik Deep Singh. India: I. J. Computer Network and Information Security, 2014. Vol. 8. 41-47 pp.
Analysis of modern systems for detecting attacks and preventing invasion / AA Zavada, OV Samchyshyn, VV Okhrimchuk // Information systems. Zhytomyr: Collection of scientific works of ZhVI NAU, 2012. T. 6, № 12. S. 97-106.
An implementation of intrusion detection system using genetic algorithm / Mohammad Sazzadul Hoque, Md. Abdul Mukit, Md., Abu Naser Bikas // International Journal of Network Security & Its Applications (IJNSA). Sylhet, 2012. Vol. 4, no. 2. P. 109-120.
Analysis and Evaluation of Network-Based Intrusion Detectionand Prevention System in an Enterprise Network Using Snort Freeware / O. B. Lawal [et al.] // African Journal of Computing & ICT. Ibadan, 2013. Vol. 6, no. 2. P. 169-184.
IDS / IPS. Netgate Documentation: [website]. Washington: Rubicon Communications LLC, 2017. [Electronic resource]. Online: https: //www.netgate. com / docs / pfsense / ids-ips /.
Dovbeshko SV, Tolyupa SV, Shestak Ya.V. Application of data mining methods to build attack detection systems. Scientific and technical journal "Modern information protection". - “1. 2019. S. 56-62.
Toliupa S., Nakonechnyi V., Uspenskyi O. Signature and statistical analyzers in the cyber attack detection system. Information technology and security. Ukrainian research papers collection Volume 7, Issue 1 (12). with. 69-79.
DARPA Intrusion Detection Data Sets [Electronic resource] - Access mode: https://www.ll.mit.edu/ideval/data/.
KDD Cup 1999 Data [Electronic resource] - Access mode: http://kdd.ics.uci.edu/databases/kddcup99.
Kayacik, HG Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets / HG Kayacik, AN Zincir-Heywood, MI Heywood // Proceedings of the Third Annual Conference on Privacy, Security and Trust (PST- 2005) - 2006. - P. 85–89.
Haykin, S. Neural Networks and Learning Machines / S. Haykin // Pearson Education, 2009. - 937 p.
Cannady, J. Applying CMAC-based On-line Learning to Intrusion Detection / J. Cannady // In: Proc. of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. - 2000. - Vol. 5 - P. 405–410.
Lee, S. C., Heinbuch, D. V. Training a neural-network based intrusion detector to recognize novel attacks / S. C. Lee, D. V. Heinbuch // IEEE Transactions on Systems, Man, and Cybernetics: Part A - 2001. - Vol. 31. - No. 4. - P. 294–299.
Liu, G. A hierarchical intrusion detection model based on the PCA neural networks / G. Liu, Z. Yi, S. Yang // Neurocomputing 70. - 2007 - Vol. 7. - No. 9. ó P. 1561–1568.
Parlos, A. Application of the recurrent multilayer perceptron in modeling complex process dynamics / A. Parlos, K. Chong, A. Atiya // IEEE Transactions on Neural Networks. - 1994. - Vol. 5. - No. 2. - P. 255–266.
Hsu, C-W. A Practical Guide to Support Vector Classification / C-W. Hsu, C-C. Chang, C-J. Lin - Department of Computer Science, National Taiwan University, Taipei 106, Taiwan, 2003. - 16 p.
Miguel, A. Carreira-Perpinan A Review of Dimension Reduction Techniques / A. Carreira-Perpinan Miguel. - Technical Report CS-96-09 Dept. of Computer Science University of Sheffield, 1997. - 69 p.
Fodor, I. K. A Survey of Dimension Reduction Techniques / I. K. Fodor - U.S. Department of Energy by University of California, Lawrence Livennore National Laboratory, 2002. - 26 p.
