[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Fuzzy approach for intrusion detection based on user’s commands

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user’s activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user’s profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users’ behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user’s profiles based on any countable features derived from user’s actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Anderson JP (1980) Computer security threat monitoring and surveillance. In: James P (ed) Technical report. Anderson Co., Fort Washington

  • Banerjee SP, Woodard DL (2012) Biometric authentication and identification using keystroke dynamics: a survey. J Pattern Recognit Res 7:116–139

    Article  Google Scholar 

  • Bartkowiak AM (2011) Block and command profiles for legitimate users of a computer network. Commun Comput Inf Sci 245:295–304 (Springer)

    Article  Google Scholar 

  • Bhukya WN, Suresh KG, Negi A (2006) A study of effectiveness in masquerade detection. In: Proceedings of TENCON 2006, 2006 IEEE Region 10 Conference, pp 1–4

  • Borah S, Chetry SPK, Singh PK (2011) Hashed-k-means: a proposed intrusion detection algorithm. In: Das VV, Thankachan N (eds) Computational Intelligence and Information Technology, vol 250. Communications in Computer and Information Science. Springer, Berlin Heidelberg, pp 855–860

  • Chinchani R, Muthukrishnan A, Chandrasekaran M, Upadhyaya S (2004) Racoon: rapidly generating user command data for anomaly detection from customizable templates. In: 20th Annual Computer Security Applications Conference, pp 189–202

  • Corchado E, Herrero A, Baruque A, Saiz JM (2005) Intrusion detection system based on a cooperative topology preserving method intrusion detection system based on a cooperative topology preserving method. In: Proceedings of the International Conference on Adaptive and Natural Computing Algorithms in Coimbra, vol IV, Portugal. Springer, Wien, pp 454–457

  • Coull S, Szymanski B (2008) Sequence alignment for masquerade detection. Comput Stat Data Anal 52(8):4116–4131

    Article  MathSciNet  MATH  Google Scholar 

  • Coull S, Branch J, Szymanski B, Breimer E (2003) Intrusion detection: a bioinformatics approach. In: Proceedings of 19th Annual Computer Security Applications Conference (ACSAC 2003), pp 24–33

  • Czogała E, Łęski J (2000) Fuzzy and neuro-fuzzy intelligent systems. Physica-Verlag, Springer-Verlag Comp

  • Dash SK, Reddy KS, Pujari AK (2005) Episode based masquerade detection. LNCS 3803:251–262

    Google Scholar 

  • Denning DE (1997) Internet besieged, chapter cyberspace attacks and countermeasures. ACM Press, New York, pp 29–55

    Google Scholar 

  • Estrada VC, Nakao A, Segura EC (2009) Classifying computer session data using self-organizing maps. In: International Conference on Computational Intelligence and Security, 2009 IEEE, pp 48–53

  • Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted boltzmann machine. Neurocomputing 122:13–23

    Article  Google Scholar 

  • Greenberg S (1998) Using unix: collected traces of 168 users. Research report 1988–333-45, Department of Computer Science, University of Calgary, Canada

  • Guan X, Wang W, Zhang X (2009) Fast intrusion detection based on non-negative matrix factorization model. J Netw Comput Appl 32:31–44

    Article  Google Scholar 

  • Gunes I, Bilge A, Polat H (2013) Shilling attacks against memory-based privacy-preserving recommendation algorithms. KSII Trans Internet Inf Syst 7:1272–1290

    Article  Google Scholar 

  • Herrero A, Corchado E, Gastaldo P, Zunino R (2007) A comparison of neural projection techniques applied to intrusion detection systems. LNCS 4507:1138–1146 (Springer-Verlag, Berlin Heidelberg)

    Google Scholar 

  • Jain AK, Flynn P, Ross AA (2007) Handbook of biometrics. Springer, New York

    Google Scholar 

  • Ju W, Vardi Y (1999) A hybrid high-order markov chain model for computer intrusion detection. Technical Report 92, National Institute for Statistical Sciences, Research Triangle Park, North Carolina, pp 27709–4006

  • Kdd cup (1999) http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  • Kim HS, Cha SD (2005) Empirical evaluation of svm-based masquerade detection using unix commands. Comput Security 24:160–168

    Article  Google Scholar 

  • Klir GJ, Folger TA (1988) Fuzzy sets, uncertanity, and information. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic. Theory and applications. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  • Kudłacik P (2008) Operations on fuzzy sets with piecewise-linear membership function (polish). Studia Informatica, 29(3A(78)):91–111

  • Kudłacik P (2010) Advantages of an approximate reasoning based on a fuzzy truth value. J Med Inf Technol 15:57–61

  • Kudłacik P (2010) Structure of a knowledge base in the fuzzlib library (polish). Studia Informatica 31(2A(89)):469–478

  • Kudłacik P (2012) Improving a signature recognition method using the fuzzy approach. J Med Inf Technol 21:85–93

    Google Scholar 

  • Kudłacik P, Porwik P (2012) A new approach to signature recognition using the fuzzy method. Pattern Anal Appl. doi:10.1007/s10044-012-0283-9

  • Lane T (1999) Purdue unix user data. http://www.cs.unm.edu/~terran/research/anomaly_detection_for_computer_security

  • Lane T (2002) Machine learning techniques for the computer security domain of anomaly detection. PhD thesis, Purdue University, USA

  • Lane T (2006) A decision-theoretic, semi-supervised model for intrusion detection. Mach Learn Data Mining Computer Security 157–177

  • Lunt TF, Jagannathan R (1988) A prototype real-time intrusion-detection expert system. In Proceedings of IEEE Symposium on Security and Privacy. IEEE Computer Society Press, Washington DC, pp 59–66

  • Mamdani EH, Assilan S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 20:1–13

    Article  MATH  Google Scholar 

  • Maxion RA (2003) Masquerade detection using enriched command lines. In: Dependable Systems and Networks. Int. Conf., pp 5–14

  • Maxion RA, Townsend TN (2002) Masquerade detection using truncated command lines. In: Proceedings of the International Conference on Dependable Systems and Networks (DSN’02). IEEE Computer Society Press, pp 219–228

  • Olszewski D (2012) A probabilistic approach to fraud detection in telecommunications. Knowl Based Syst 26:246–258

    Article  Google Scholar 

  • Palmieri F, Fiore U (2010) Network anomaly detection through nonlinear analysis. Comput Security 29(7):737–755

    Article  Google Scholar 

  • Palmieri F, Fiore U, Castiglione A (2014) A distributed approach to network anomaly detection based on independent component analysis. Concurr Comput Pract Exper 26:1113–1129

    Article  Google Scholar 

  • Pao Hsing-Kuo, Fadlil Junaidillah, Lin Hong-Yi, Chen Kuan-Ta (2012) Trajectory analysis for user verification and recognition. Knowl Based Syst 34:81–90

    Article  Google Scholar 

  • Porwik P, Sosnowski M, Wesołowski T, Wróbel K (2011) Computational assessment of a blood vessels compliance: a procedure based on computed tomography coronary angiography. LNAI 6678(1):428–435 (Springer)

    Google Scholar 

  • Raiyn J (2014) A survey of cyber attack detection strategies. Int J Security Appl 8(1):247–256

    Article  Google Scholar 

  • Salem MB, Hershkop S, Stolfo SJ (2008) A survey of insider attack detection research. In: Salvatore SJ, Bellovin SM, Keromytis AD, Hershkop S, Smith SW, Sinclair S (eds) Insider Attack and Cyber Security, volume 39 of Advances in Information Security, pp 69–90. Springer US

  • Salem MB, Stolfo SJ (2012) A comparison of one-class bag-of-words user behavior modeling techniques for masquerade detection. Security Commun Netw 5:863–872

    Article  Google Scholar 

  • Schonlau M (2000) Masquerading user data. http://www.schonlau.net

  • Schonlau M, Theus M (2000) Detecting masquerades in intrusion detection based on unpopular commands. Inf Process Lett 76:33–38

    Article  Google Scholar 

  • Schonlau M, Mouchel WD, Ju WH, Karr AF, Theus M, Vardi Y (2001) Computer intrusion: detecting masquerades. Stat Sci 16(1):58–74

    Article  MathSciNet  MATH  Google Scholar 

  • Smaha SE (1988) Haystack: an intrusion detection system. In: Fourth Aerospace Computer Security Applications Conference, pp 37–44

  • Sodiya AS, Folorunso O, Onashoga SA, Ogunderu OP (2011) An improved semiglobal alignment algorithm for masquerade detection. Int J Netw Security 13:31–40

    Google Scholar 

  • Szymanski BK, Zhang Y (2004) Recursive data mining for masquerade detection and author identification. In: Proceedings 5th Annual IEEE System, Man, and Cybernetics Information Assurance Workshop, pp 424–431

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  MATH  Google Scholar 

  • Tavallaee M, Bagheri E, Lu W, Ghorbani Ali A (2009) A detailed analysis of the kdd cup 99 data set. In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009)

  • Viejo A, Sanchez D, Castella-Roca J (2012) Preventing automatic user profiling in web 2.0 applications. Knowl Based Syst 36:191–205

    Article  Google Scholar 

  • Wan MD, Wu HC, Kuo YW, Marshall J, Huang SHS (2008) Detecting masqueraders using high frequency commands as signatures. In: 22nd International Conference on Advanced Information Networking and Applications—Workshops

  • Wang LX (1998) A course in fuzzy systems and control. Prentice-Hall, New York

    Google Scholar 

  • Wang W, Guan X, Zhang X (2004) A novel intrusion detection method based on principal component analysis in computer security. LNCS Adv Neural Netw 3174:657–662

    Google Scholar 

  • Wang W, Guan X, Zhang X (2008) Proc. massive audit data streams for real-time anomaly intrusion detection. Comput Commun 31:58–72

    Article  Google Scholar 

  • Wesołowski T, Kudłacik P (2013) Data clustering for the block profile method of intruder detection. J MIT 22:209–216

    Google Scholar 

  • Wesołowski T, Kudłacik P (2014) User profiling based on multiple aspects of activity in a computer system. J MIT 23:121–129

    Google Scholar 

  • Wespi A, Dacier M, Debar H (1999) An intrusion-detection system based on the teiresias pattern-discovery algorithm. In: EICAR 1999 Best Paper Proceedings

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

  • Zimmermann HJ (1985) Fuzzy set theory and it’s applications. Kluwer-Nijhoff, Boston

    Book  Google Scholar 

Download references

Acknowledgments

This work was supported by the Polish National Science Centre under the grant no. DEC-2013/09/B/ST6/02264.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Silesia, Będzińska 39, 41-200, Sosnowiec, Poland

    Przemysław Kudłacik, Piotr Porwik & Tomasz Wesołowski

Authors
  1. Przemysław Kudłacik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Porwik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomasz Wesołowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Przemysław Kudłacik.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kudłacik, P., Porwik, P. & Wesołowski, T. Fuzzy approach for intrusion detection based on user’s commands. Soft Comput 20, 2705–2719 (2016). https://doi.org/10.1007/s00500-015-1669-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1669-6

Keywords

Search

Navigation