Top

Published in:

2017 | OriginalPaper | Chapter

Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction

Authors : Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Haralambos Mouratidis

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter The 50/50 Recommender: A Method Incorporating Personality into Movie Recommender Systems

next chapter Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters

Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRef

Polatidis, N., Georgiadis, C.K.: Recommender systems: the importance of personalization on e-business environments. Int. J. E-entrepreneursh. Innov. 4, 32–46 (2013)CrossRef

Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)CrossRef

Shams, B., Haratizadeh, S.: TasteMiner: mining partial tastes for neighbor-based collaborative filtering. J. Intell. Inf. Syst. 48, 165–189 (2017)CrossRef

Wang, W., Zhang, G., Lu, J.: Collaborative filtering with entropy-driven user similarity in recommender systems. Int. J. Intell. Syst. 30, 854–870 (2015)CrossRef

Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56, 156–166 (2014)CrossRef

Son, L.H.: HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Syst. Appl. 41, 6861–6870 (2014)CrossRef

Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manag. 48, 204–217 (2012)CrossRef

Gan, M., Jiang, R.: Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities. Decis. Support Syst. 55, 811–821 (2013)CrossRef

10.

Ortega, F., Sánchez, J.L., Bobadilla, J., Gutiérrez, A.: Improving collaborative filtering-based recommender systems results using Pareto dominance. Inf. Sci. (N.Y.) 239, 50–61 (2013)CrossRef

11.

Polatidis, N., Georgiadis, C.K.: A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl. 48, 100–110 (2016)CrossRef

12.

Polatidis, N., Georgiadis, C.K.: A dynamic multi-level collaborative filtering method for improved recommendations. Comput. Stand. Interfaces 51, 14–21 (2017)CrossRef

13.

Toledo, R.Y., Mota, Y.C., Martínez, L.: Correcting noisy ratings in collaborative recommender systems. Knowl.-Based Syst. 76, 96–108 (2015)CrossRef

14.

Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings 18th National Conference on Artificial Intelligence (AAAI), pp. 187–192 (2002)

15.

Anand, D., Bharadwaj, K.K.: Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst. Appl. 38, 5101–5109 (2011)CrossRef

16.

Gan, M.: COUSIN: a network-based regression model for personalized recommendations. Decis. Support Syst. 82, 58–68 (2016)CrossRef

17.

Gan, M.-X., Sun, L., Jiang, R.: Trinity: walking on a user-object-tag heterogeneous network for personalised recommendations. J. Comput. Sci. Technol. 31, 577–594 (2016)CrossRef

18.

Xu, B., Bu, J., Chen, C., Cai, D.: An exploration of improving collaborative recommender systems via user-item subgroups. In: Proceedings of 21st International Conference on World Wide Web - WWW 2012, p. 21 (2012)

19.

Ou, X., Singhal, A.: Attack graph techniques. In: Ou, X., Singhal, A. (eds.) Quantitative Security Risk Assessment of Enterprise Networks. SpringerBriefs in Computer Science, pp. 13–23. Springer, New York (2011). doi:10.1007/978-1-4614-1860-3_2 CrossRef

20.

Templeton, S.J., Levitt, K.: A requires/provides model for computer attacks. In: Proceedings of 2000 Workshop on New Security Paradigms - NSPW 2000, pp. 31–38 (2000)

21.

Ning, P., Xu, D.: Learning attack strategies from intrusion alerts. In: Proceedings of the 10th ACM Conference on Computer and Communication Security - CCS 2003, p. 200 (2003)

22.

Ritchey, R.W., Ammann, P.: Using model checking to analyze network vulnerabilities. In: Proceedings 2000 IEEE Symposium on Security and Privacy, S&P 2000, pp. 156–165 (2000)

23.

Sheyner, O., Haines, J., Jha, S., Lippmann, R., Wing, J.M.: Automated generation and analysis of attack graphs. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 273–284 (2002)

24.

Ou, X., Boyer, W.F., McQueen, M.A.: A scalable approach to attack graph generation. In: 13th ACM Conference on Computer and Communications Security, pp. 336–345 (2006)

25.

Ammann, P., Wijesekera, D., Kaushik, S.: Scalable, graph-based network vulnerability analysis. In: Proceedings of 9th ACM Conference on Computer and Communication Security - CCS 2002, p. 217 (2002)

26.

Ammann, P., Pamula, J., Ritchey, R., Street, J.: A host-based approach to network attack chaining analysis. In: Proceedings of Annual Computer Security Applications Conference, ACSAC, pp. 72–81 (2005)

27.

Ingols, K., Lippmann, R., Piwowarski, K.: Practical attack graph generation for network defense. In: Proceedings of Annual Computer Security Applications Conference, ACSAC, pp. 121–130 (2006)

28.

Ingols, K., Chu, M., Lippmann, R., Webster, S., Boyer, S.: Modeling modern network attacks and countermeasures using attack graphs. In: Proceedings of Annual Computer Security Applications Conference, ACSAC, pp. 117–126 (2009)

29.

Kaynar, K., Sivrikaya, F.: Distributed attack graph generation. IEEE Trans. Dependable Secur. Comput. 13, 519–532 (2016)CrossRef

30.

Xie, A., Zhang, L., Hu, J., Chen, Z.: A probability-based approach to attack graphs generation. In: 2nd International Symposium on Electronic Commerce and Security, ISECS 2009, pp. 343–347 (2009)

31.

Ghosh, N., Ghosh, S.K.: A planner-based approach to generate and analyze minimal attack graph. Appl. Intell. 36, 369–390 (2012)CrossRef

32.

Phillips, C., Swiler, L.P.: A graph-based system for network-vulnerability analysis. In: Proceedings of 1998 Workshop on New Security Paradigms, pp. 71–79 (1998)

33.

Almohri, H.M.J., Watson, L.T., Yao, D., Ou, X.: Security optimization of dynamic networks with probabilistic graph modeling and linear programming. IEEE Trans. Dependable Secur. Comput. 13, 474–487 (2016)CrossRef

34.

Bi, K., Han, D., Wang, J.: K maximum probability attack paths dynamic generation algorithm. Comput. Sci. Inf. Syst. 13, 677–689 (2016)CrossRef

35.

Artz, M.L.: NetSPA : a network security planning architecture, pp. 1–97 (2002)

36.

Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using Bayesian attack graphs. IEEE Trans. Dependable Secur. Comput. 9, 61–74 (2012)CrossRef

37.

Ou, X., Govindavajhala, S., Appel, A.W.: MulVAL: a logic-based network security analyzer. In: Proceedings of the 14th Conference on USENIX Security Symposium, vol. 14, p. 8 (2005)

38.

Jajodia, S., Noel, S., O’Berry, B.: Topological analysis of network attack vulnerability. In: Kumar, V., Srivastava, J., Lazarevic, A. (eds.) Managing Cyber Threats, pp. 247–266. Springer, Heidelberg (2005). doi:10.1007/0-387-24230-9_9 CrossRef

39.

Barik, M.S., Mazumdar, C.: A graph data model for attack graph generation and analysis. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds.) SNDS 2014. CCIS, vol. 420, pp. 239–250. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54525-2_22 CrossRef

40.

Common Weakness Enumeration, CWE. http://cwe.mitre.org/. Accessed 20 Apr 2017

41.

Common Vulnerabilities and Exposures, CVE. https://cve.mitre.org/. Accessed 20 Apr 2017

Title: Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction
Authors: Nikolaos Polatidis
Elias Pimenidis
Michalis Pavlidis
Haralambos Mouratidis
Publisher: Springer International Publishing
Book: Engineering Applications of Neural Networks
Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-65172-9_43

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner