Abstract
The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.
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Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J (2003) Danger theory: the link between AIS and IDS. In: Proceedings of the 2nd international conference on artificial immune systems (ICARIS), LNCS 2787, pp 147–155. Springer, Heidelberg
Albayrak S, Scheel C, Milosevic D, Muller A (2005) Combining self-organizing map algorithms for robust and scalable intrusion detection. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, Web technologies and Internet commerce, vol 2
Amini M, Jalili R, Shahriari HR (2006) RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks. Comput Secur 25(6):459–468
Bailey-Lee C, Roedel C, Silenok E (2003) Detection and characterization of port scan attacks. Technical report, University of California San Diego (UCSD)
Balthrop J, Esponda F, Forrest S, Glickman M (2002) Coverage and generaliszation in an artificial immune system. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 3–10
Bejtlich R (2005) Extrusion detection: security monitoring for internal intrusions. Addison-Wesley, Reading
Bentley P, Greensmith J, Ujjin S (2005) Two ways to grow tissue for artificial immune systems. In: Proceedings of the 4th international conference on artificial immune systems (ICARIS), LNCS 3627. Springer, Heidelberg, pp 139–152
Bivens A, Palagiri C, Smith R, Szymanski B, Embrechts M (2002) Network-based intrusion detection using neural networks. Intell Eng Syst Artif Neural Netw 12(1):579–584
Bolzoni D, Etalle S, Hartel P, Zambon E (2006) Poseidon: a 2-tier anomaly-based network intrusion detection system. In: Fourth IEEE international workshop on information assurance (IWIA’06), vol 0, pp 144–156. IEEE Computer Society, Los Alamitos
Sung-Bae Cho (2002) Incorporating soft computing techniques into a probabilitistic intrusion detection system. IEEE Trans Syst Man Cybern 32(2):154–160
Choy J, Cho SB (2001) Anomaly detection of computer usage using artificial intelligence techniques. Adv Artif Intell PRICAI 2000 2112:31–43
Coico R, Sunshine G, Benjamini E (2003) Immunology: a short course. Wiley-Liss, New York
Cross S, Harrison R, Kennedy R (1995) Introduction to neural networks. Lancet 346(8982):1075–1079
de Castro L, Timmis J (2002) Artificial immune systems: a new computational approach. Springer, London
DeLooze L (2006) Attack characterization and intrusion detection using an ensemble of self-organizing maps. In: International joint conference on neural networks (IJCNN’06), pp 2121–2128
Depren O, Topallar M, Anarim E, Ciliz MK (2005) An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Exp Syst Appl 29(4):713–722
Dostoevsky F nmap. http://www.insecure.org, last accessed, 5/10/07
Forrest S, Perelson A, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the IEEE symposium on security and privacy. IEEE Computer Society, pp 202–209
Fox KL, Henning RR, Reed JH, Simonian R (1990) A neural network approach towards intrusion detection. In: Proceedings of the 13th national computer security conference, vol 10
Gollmann D (1999) Computer security. Wiley, Inc., New York
Gonzalez F, Dasgupta D (2002) Neuro-immune and self-organizing map approaches to anomaly detection: a comparison. In: Proceedings of the 1st international conference on artificial immune systems, pp 203–211
Gonzalez F, Dasgupta D (2003) Anomaly detection using real-valued negative selection. J Genet Program Evol Machines 4:383–403
González FA, Galeano JC, Rojas DA, Veloza-Suan A (2005) Discriminating and visualizing anomalies using negative selection and self-organizing maps. In: Proceedings of the 2005 conference on genetic and evolutionary computation. ACM Press, New York, pp 297–304
Greensmith J (2007) The Dendritic cell algorithm. PhD Thesis, School of Computer Science, University Of Nottingham
Greensmith J, Aickelin U (2007) Dendritic cells for syn scan detection. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2007), pp 49–56
Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic Cells as a novel immune-inspired algorithm for anomaly detection. In: Proceedings of the 4th international conference on artificial immune systems (ICARIS), LNCS 3627. Springer, Heidelberg, pp 153–167
Greensmith J, Aickelin U, Tedesco G (2008) Information fusion for anomaly detection with the dca. Information Fusion, tbc(tbc):tbc, 2008
Greensmith J, Aickelin U, Twycross J (2006) Articulation and clarification of the dendritic cell algorithm. In: Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp 404–417
Greensmith J, Twycross J, Aickelin U (2006) Dendritic cells for anomaly detection. In: Proceedings of the congress on evolutionary computation (CEC), pp 664–671
Gunes Kayacik H, Nur Zincir-Heywood A, Heywood MI (2007) A hierarchical SOM-based intrusion detection system. Eng Appl Artif Intell 20(4):439–451
Higgins JJ (2004) An introduction to modern nonparametric statistics. Thomson, Brooks/Cole, Pacfic Grove
Hofmeyr S, Forrest S (1998) Intrusion detection using sequences of system calls. J Comput Secur 6:151–180
Hofmeyr S, Forrest S (1999) Immunity by design. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 1289–1296
Höglund A, Hätönen K (1998) Computer network user behaviour visualization using self organizing maps. In: Niklasson L, Bodén M, Ziemke T (eds) Proceedings of ICANN98, the 8th international conference on artificial neural networks, vol 2. Springer, London, pp 899–904
Höglund A, Hätönen K, Sorvari A (2000) A computer host-based user anomaly detection system using the self-organizing map. In: IJCNN (5), pp 411–416
Horeis T (2003) Intrusion detection with neural networks–combination of self-organizing maps and radial basis function networks for human expert integration. Student Research Grants Technical report, IEEE Computational Intelligence Society
Ji Z, Dasgupta D (2004) Real-valued negative selection algorithm with variable-sized detectors. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 287–298
Jirapummin C, Wattanapongsakorn N, Kanthamanon P (2002) Hybrid neural networks for intrusion detection system. In: 2002 international technical conference on circuits/systems, computers and communications (ITC-CSCC 2002), Phuket, Thailand, pp 928–931
Jung J, Paxson V, Berger A, Balakrishnan H (2004) Fast portscan detection using sequential hypothesis testing. In: Proceedings 2004 IEEE symposium, Security and privacy, pp 211–225
Kandel ER, Schwartz JH, Jessell TM (2000) Principles of neural science. McGraw-Hill/Appleton & Lange, New York
Kayacik H, Zincir-Heywood A, Heywood M (2003) On the capability of an SOM based intrusion detection system. In: Proceedings of the international joint conference on neural networks, vol 3
Kayacik HG, Zincir-Heywood N (2005) Analysis of three intrusion detection system benchmark datasets using machine learning algorithms. In: Proceedings of IEEE international conference on intelligence and security informatics (ISI 2005), vol 3495 of LNCS. Springer, Atlanta, pp 362–367
Khanna R, Liu H (2006) System approach to intrusion detection using HMM. In: International conference on communications and mobile computing, pp 349–354
Kim J, Bentley P (2001) Evaluating negative selection in an artificial immune system for network intrusion detection. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 1330–1337
Kim J, Bentley P, Aickelin U, Greensmith J, Tedesco G, Twycross J (2007) Immune system approaches to intrusion detection —a review. Natural computing, page tbc, 2007 (to appear, accepted for publication)
Kohonen T (1981) Automatic formation of topological maps of patterns in a self-organizing system. In: Proceedings of the 2nd scandinavian conference on image analysis, pp 214–220
Kohonen T (1996) Self-organizing maps. Springer, Berlin
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480
Lee SC, Heinbuch DV (2001) Training a neural-network based intrusion detector to recognizenovel attacks. Syst Man Cybern Part A IEEE Trans 31(4):294–299
Lei JZ, Ghorbani A (2004) Network intrusion detection using an improved competitive learning neural network. In: 2nd annual conference on communication networks and services research, pp 190–197
Lichodzijewski P, Nur Zincir-Heywood A, Heywood M (2002) Dynamic intrusion detection using self organizing maps. In: The 14th annual canadian information technology security symposium (CITSS)
Lutz M, Schuler G (2002) Immature, semi-mature and fully mature dendritic cells: which signals induce tolerance or immunity? Trends Immunol 23(9):991–1045
Matzinger P (1994) Tolerance, danger and the extended family. Annu Rev Immunol 12:991–1045
Matzinger P (2007) Friendly and dangerous signals: is the tissue in control? Nat Immunol 8(1):11–13
Miller P, Inoue A (2003) Collaborative intrusion detection system. In: 22nd international conference of the north American fuzzy information processing society (NAFIPS 2003), pp 519–524
Murphy K, Travers P, Walport M (2008) Janeway’s Immunobiology. Garland science, 7th edn
Oates R, Greensmith J, Aickelin U, Garibaldi J, Kendall G (2007) The application of a dendritic cell algorithm to a robotic classifier. In: Proceedings of the 6th international conference on artificial immune systems (ICARIS), LNCS 4628, pp 204–215
Oates R, Kendall G, Garibaldi J (2007) Frequency analysis for dendritic cell population tuning: decimating the dendritic cell. Evol Intell (submitted)
Ramadas M, Ostermann S, Tjaden B (2003) Detecting anomalous network traffic with self-organizing maps. In: Proceedings of recent advances in intrusion detection: 6th international symposium (RAID 2003). Springer, Pittsburgh
Rhodes BC, Mahaffey JA, Cannady JD (2000) Multiple self-organizing maps for intrusion detection. In: Proceedings of the 23rd national information systems security conference
Ritter H, Martinetz T, Schulten K (1992) Neural computation and self-organizing maps: an introduction. Addison-Wesley Longman Publishing Co., Inc., Boston
Roesch M (1999) Snort—lightweight intrusion detection for networks. In: Proceedings of the 13th USENIX conference on system administration (LISA), USENIX Association, pp 229–238
Sarasamma S, Zhu Q (2006) Min–max hyperellipsoidal clustering for anomaly detection in network security. Syst Man Cybern Part B IEEE Trans 36(4):887–901
Sarasamma ST, Zhu QA, Huff J (2005) Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans Syst Man Cybern Part B Cybern 35(2):302–312
Garner S (1995) Weka: the waikato environment for knowledge analysis. In: Proceedings of the New Zealand computer science research students conference, pp 57–64
Somayaji A, Locasto M, Feyereisl J (2007) Panel on the future of biologically-inspired security: is there anything left to learn? In: New security paradigms workshop (NSPW’07)
Sporri R, Caetano C (2005) Inflammatory mediators are insufficient for full dendritic cell activation and promote expansion of cd4+ t cell populations lacking helper function. Nat Immunol 6(2):163–170
Staniford S, Hoagland J, McAlerney J (2002) Practical automated detection of stealthy portscans. J Comput Secur 10(1–2):105–136
Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In: Proceedings of genetic and evolutionary computation conference (GECCO), pp 321–328
Stibor T, Eckert C, Timmis J (2006) Artificial immune systems for IT-security. Inf Technol 48(3):168–173
Stibor T, Timmis J, Eckert C (2006) On permutation masks in hamming negative selection. In: Proceedings of the 5th international conference on artificial immune systems (ICARIS), LNCS 4163, pp 122–135
Timmis J (2007) Artificial immune systems: today and tomorrow. Nat Comput 6(1):1–18
Twycross J (2007) Integrated innate and adaptive artificial immune systems applied to process anomaly detection. PhD Thesis, University Of Nottingham
Twycross J, Aickelin U (2005) Towards a conceptual framework for innate immunity. In: Proceedings of the 4th international conference on artificial immune systems (ICARIS), LNCS 3627. Springer, Heidelberg, pp 112–125
Twycross J, Aickelin U (2006) Libtissue—implementing innate immunity. In: Proc of the congress on evolutionary computation (CEC), pp 499–506
Wang W, Guan X, Zhang X, Yang L (2006) Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data. Comput Secur 25(7):539–550
Williams C, Harry R, McLeod J (2007) Mechanisms of apoptosis induced DC suppression. J Immunol (submitted)
Yoo IS (2004) Visualizing windows executable viruses using self-organizing maps. In: Proceedings of the 2004 ACM workshop on visualization and data mining for computer security. ACM Press, New York, pp 82–89
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Greensmith, J., Feyereisl, J. & Aickelin, U. The DCA: SOMe comparison. Evol. Intel. 1, 85–112 (2008). https://doi.org/10.1007/s12065-008-0008-6
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DOI: https://doi.org/10.1007/s12065-008-0008-6