Thursday , June 21 2018

Optimal Infrastructure for Acquiring and Processing of
Data related to Anthropic Computer Incidents

Cornel RESTEANU1, Electra MITAN1, Marin ANDREICA2,
Gheorghe PĂCURAR2

1 I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania,
2 Economic Studies Academy,
Bucharest 010374, Romania,,

Abstract: The paper presents a good practice in choosing optimal software – hardware infrastructure couple engaged with acquiring and processing of data related to computer security anthropic incidents. The authors work in the Multi-Attribute Decision Making paradigm, mono-decision maker and mono-state of nature sub-paradigm. An assessment model is built. The model’s objects are the software – hardware couples. Concerning the first couple’s component, i.e. the software infrastructure, the selection process is done starting with Data Base Management Systems’ complex taxonomy. Concerning hardware infrastructure, the choice of elements is based on considered availabilities. The model’s attributes are couples’ characteristics evaluated by an expert which gives grades according to his / her expertise. Based on this model, the TOPSIS method computes the merit of each couple. The couple with the greatest merit is considered as optimal.

Keywords: Computer Security, Cyber Attacks, Data Base Management Systems, Computer Systems, Clustering, Multi-Attribute Decision Making.

>>Full text
Cornel RESTEANU, Electra MITAN, Marin ANDREICA, Gheorghe PĂCURAR, Optimal Infrastructure for Acquiring and Processing of Data related to Anthropic Computer Incidents, Studies in Informatics and Control, ISSN 1220-1766, vol. 24 (1), pp. 51-60, 2015.

  1. Introduction

Anthropic incidents in the field of computer science are caused by cyber weapons [1] or non-cyber weapons. In this paper only cyber weapons are taken into account. The main incidents produced by cyber weapons are:

  • Infection with computer associated bacteria (with its varieties: regular bomb, timer bomb, logic bomb etc.), viruses (with the varieties: boot sector, appending, companion, crypto, critical, Trojan horse, binary, multi-partite, link, file jumper, stealth, morphy, runtime, parasitic, polymorphic, resident, spy etc.), worms (with the varieties: computer, network, host etc.);
  • Sabotaging the firewalls installed for the protection of web applications;
  • Attacks of email systems by spreading word viruses and flooding messages;
  • The alteration of the functioning of the search engines by over posting ads or information that the user has requested in previous sessions;
  • The violation of the access data of the users` (names, passwords, accounts etc.);
  • Not respecting the access rights to some data;
  • Decrypting data that are supposed to be secret;
  • Destruction of the integrity of the systems’ digital content (files / data bases) etc.

There have been created in almost every country several multi-level structured entities with a view to tracking the current status and the evolution of the anthropic cyber incidents produced by informatics means in order to build strategies against this phenomenon.

Basic organisms are assigned to one area to work in by collecting data on incidents: how the attack happened, place of occurrence, date of occurrence, the damage and how was solved the problem locally. A summary report shall be submitted to a higher level national or international authority. It systematizes the information received, processes it statistically / graphically and transfers it to the integrating international authorities. The minimal purpose of these bodies is to warn the users in specific areas on imminent hazards as well as to define the tools designed to ensure security. Obviously, these entities must have adequate tools to perform the functions listed above. The infrastructure of these tools, both software and hardware, must be optimal for the system functionality to satisfy the needs of IT efficiency, which in this case are quite high. Therefore, paper’s goal is to propose a way to find optimal software – hardware infrastructure for cyber security centres.


  1. VERT, G., R. DOURSAT, A Fuzzy Taxonomic Approach for Classifying and Identifying System Attacks and Automating Attacks Response. In: Proceedings of 4th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics, Miami, Florida, USA, November 17-19, 2005, pp 29-34.
  2. XU, R., D. C. WUNSCH II, Clustering, Wiley-IEEE Press, 2008.
  3. EVERITT, B. S., S. LANDAU, M. LEESE, D. STAHL, Cluster Analysis, Wiley Series in Probability and Statistics, 4th ed., 2009.
  4. TZENG, G. H., J. J. HUANG, Multiple Attribute Decision Making: Methods and Applications, Chapman & Hall, CRC Press, 2011.
  5. YOON, K. P., C. L. HWANG, Multiple Attribute Decision Making: An Introduction, SAGE Publications, 1995.
  6. RESTEANU, C., M. ŞOMODI, M. ANDREICA, E. MITAN, Distributed and Parallel Computing in MADM Domain using the OPT CHOICE Software. Wisconsin, USA: In: Proceedings of the 7th WSEAS International Conference on Applied Computer Science (ACS’07), 2007, pp. 376-384.
  7. STAAB, S., R. STUDER, Handbook on Ontologies, Springer eBooks, Series: International Handbooks on Information Systems, 2009.
  8. SCHWARTZ, B., P. ZAITSEV, V. TKACHENKO, J. D. ZAWODNY, A. LENTZ, D. J. BALLING, High Performance MySQL: Optimization, Backups, and Replication, Sebastopol: O’Reilly Media, 2008.
  9. THOMPSON, L., L. WELLING, PHP and MySQL Web Development. The definitive guide to building database-drive Web applications with PHP and MySQL, Boston: Addison-Wesley Professional, 2008.
  10. VAN DER LANS, R. F., SQL for DB2 Developers: The Complete Guide for Optimal Performance, Indianapolis: IBM Press, 2007.
  11. CHONG, R. F., C. LIU, DB2 Essentials: Understanding DB2 in a Big Data World (3rd Ed.), Indianapolis: IBM Press, 2013.
  12. MULLINS, C. S., DB2 Developer’s Guide: A Solutions-Oriented Approach to Learning the Foundation and Capabilities of DB2 for z/OS (6th Ed.) Indianapolis: IBM Press, 2012.
  13. KYTE, T., Expert Oracle Database Architecture: Oracle Database 9i, 10g, and 11g. Programming Techniques and Solutions, New York: Apress, 2010.
  14. MAFTEI, E., C. MAFTEI, ORACLE from 9i to 11g for Application Developers – Vol. 1 (part. 1+2), Cluj-Napoca: Publisher Albastra, 2010.
  15. RESTEANU, C. MADM – Theory and Practice, ICI Publishing House, Bucharest,(in Romanian), 2006.
  16. ERGU, D., G. KOU, Data Inconsistency and Incompleteness Processing Model in Decision Matrix, Studies in Informatics and Control, vol. 22 (4), 2013, pp. 359-366.