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2018 | OriginalPaper | Chapter

A Survey of Machine Learning Algorithms and Their Application in Information Security

Author : Mark Stamp

Published in: Guide to Vulnerability Analysis for Computer Networks and Systems

Publisher: Springer International Publishing

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Abstract

In this survey, we touch on the breadth of applications of machine learning to problems in information security. A wide variety of machine learning techniques are introduced, and a sample of the applications of each to security-related problems is briefly discussed.

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previous chapter Review into State of the Art of Vulnerability Assessment using Artificial Intelligence

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Observations are invariably known as “emissions” in a PHMM.

These VQ codebook vectors are not to be confused with a codebook cipher [82].

Stamp M (2004) A revealing introduction to hidden Markov models. https://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf

Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRef

Cave RL, Neuwirth LP (1980) Hidden Markov models for English. In: Ferguson JD (ed) Hidden Markov models for speech, IDA-CRD, Princeton, pp 16–56

Annachhatre C, Austin TH, Stamp M (2015) Hidden Markov models for malware classification. J Comput Virol Hacking Tech 11(2): 59–73. https://doi.org/10.1007/s11416-014-0215-x

Austin TH, Filiol E, Josse S, Stamp M (2013) Exploring hidden Markov models for virus analysis: a semantic approach. In: 46th Hawaii international conference on system sciences, HICSS 2013, Wailea, USA, 7–10 Jan 2013, pp 5039–5048. IEEE Computer Society?. https://doi.org/10.1109/HICSS.2013.217

Kalbhor A, Austin TH, Filiol E, Josse S, Stamp M (2015) Dueling hidden Markov models for virus analysis. J Comput Virol Hacking Tech 11(2): 103–118. https://doi.org/10.1007/s11416-014-0232-9

Wong W, Stamp M (2006) Hunting for metamorphic engines. J Comput Virol 2(3): 211–229. https://doi.org/10.1007/s11416-006-0028-7

Borello J-M, Mé L (2008) Code obfuscation techniques for metamorphic viruses. J Comput Virol 4(3):211–220CrossRef

Venkatachalam S, Stamp M (2011) Detecting undetectable metamorphic viruses. In: Proceedings of 2011 international conference on security & management. SAM’11, pp 340–345

10.

Cho S-B, Park H-J (2003) Efficient anomaly detection by modeling privilege flows using hidden Markov model. Comput Secur 22(1): 45–55. https://doi.org/10.1016/S0167-4048(03)00112-3

11.

Hu J, Yu X, Qiu D, Chen H-H (2009) A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection. IEEE Netw Mag Glob Internet Working 23(1): 42–47. https://doi.org/10.1109/MNET.2009.4804323

12.

Bertacchini M, Fierens P (2009) A survey on masquerader detection approaches. In: Proceedings of V Congreso Iberoamericano de Seguridad Informática, Universidad de la República de Uruguay, pp 46–60

13.

Okamoto T, Ishida Y (2007) Framework of an immunity-based anomaly detection system for user behavior. In: International conference on knowledge-based and intelligent information and engineering systems. Springer, Berlin, pp 821–829

14.

Posadas R, Mex-Perera C, Monroy R, Nolazco-Flores J (2006) Hybrid method for detecting masqueraders using session folding and hidden Markov models. In: Proceedings of the 5th Mexican international conference on artificial intelligence. MICAI’06. Springer, Berlin, pp 622–631

15.

Kazi S, Stamp M (2013) Hidden Markov models for software piracy detection. Inf Secur J Glob Perspect. 22(3):140–149. https://doi.org/10.1080/19393555.2013.787474

16.

Rana H, Stamp M (2014) Hunting for pirated software using metamorphic analysis. Inf Secur J Glob Perspect 23(3): 68–85. https://doi.org/10.1080/19393555.2014.975557

17.

Simova M, Stamp M, Pollett C (2005) Stealthy ciphertext. In: Arabnia HR, Joshua R (eds) Proceedings of the 2005 international conference on internet computing. ICOMP 2005. CSREA Press, pp 380–388

18.

Srivastava A, Kundu A, Sural S, Majumdar A (2008) Credit card fraud detection using hidden Markov model. IEEE Trans Dependable Secur Comput 5(1): 37–48. https://doi.org/10.1109/TDSC.2007.70228

19.

Ariu D, Tronci R, Giacinto G (2011) HMMPayl: an intrusion detection system based on hidden Markov models. Comput Secur30(4): 221–241 (2011). https://doi.org/10.1016/j.cose.2010.12.004

20.

Okamoto T, Ishida Y (2011) Towards an immunity-based anomaly detection system for network traffic. Int J Knowl Based Intell Eng Syst 15(4): 215–225. https://doi.org/10.3233/KES-2011-0223

21.

Oza A, Ross K, Low RM, Stamp M (2014) HTTP attack detection using \(n\)-gram analysis. Comput. Secur 45: 242–254. https://doi.org/10.1016/j.cose.2014.06.002

22.

Sperotto A, Sadre R, de Boer P-T, Pras A (2007) Hidden Markov model modeling of SSH brute-force attacks. In: Bartolini C, Gaspary LP (eds) Proceedings of 20th IFIP/IEEE international workshop on distributed systems: operations and management (DSOM 2009). Lecture notes in computer science, vol 5841. Springer, Berlin, pp 164–176

23.

Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, CambridgeCrossRef

24.

Stamp M (2017) Introduction to machine learning with applications in information security. Chapman and hall/CRC, Boca RatonCrossRef

25.

Vemparala S, Troia FD, Visaggio CA, Austin TH, Stamp M (2016) Malware detection using dynamic birthmarks. In: Verma RM, Rusinowitch M (eds) Proceedings of the 2016 ACM on international workshop on security and privacy analytics, IWSPA 2016. ACM, USA, pp 41–46. https://doi.org/10.1145/2875475.2875476

26.

Attaluri S, McGhee S, Stamp M (2009) Profile hidden Markov models and metamorphic virus detection. J Comput Virol 5(2):151–169. https://doi.org/10.1007/s11416-008-0105-1

27.

Ravi S, Balakrishnan N, Venkatesh B (2013) Behavior-based malware analysis using profile hidden Markov models. In: Proceedings of 2013 international conference on security and cryptography, SECRYPT

28.

Huang L, Stamp M (2011) Masquerade detection using profile hidden Markov models. Comput Secur 30(8): 732–747. https://doi.org/10.1016/j.cose.2011.08.003

29.

Pathan A-SK (2014) The state of the art in intrusion prevention and detection. Auerbach publications, Boston, USACrossRef

30.

Wright C, Monrose F, Masson GM (2004) HMM profiles for network traffic classification. In: Proceedings of the 2004 ACM workshop on visualization and data mining for computer security, VizSEC/DMSEC ’04. ACM, USA, pp. 9–15. https://doi.org/10.1145/1029208.1029211

31.

Wright CV, Monrose F, Masson GM (2005) Towards better protocol identification using profile HMMs, Information security institute, Johns Hopkins university, JHU Technical Report JHU-SPAR051201. https://www.cs.jhu.edu/~cwright/hmm-techreport.pdf

32.

Wright CV, Ballard L, Coull SE, Monrose F, Masson GM (2010) Uncovering spoken phrases in encrypted voice over ip conversations. ACM Trans Inf Syst Secur 13(4): 35–13530. https://doi.org/10.1145/1880022.1880029

33.

Yadwadkar NJ, Bhattacharyya C, Gopinath K, Niranjan T, Susarla S (2010) Discovery of application workloads from network file traces. In: Proceedings of the 8th USENIX conference on file and storage technologies, FAST’10. USENIX Association, Berkeley, USA, pp 183–196. http://dl.acm.org/citation.cfm?id=1855511.1855525

34.

Shlens J (2005) A tutorial on principal component analysis. http://www.cs.cmu.edu/~elaw/papers/pca.pdf

35.

Shalizi C Principal component analysis. https://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch18.pdf

36.

Stack exchange: making sense of principal component analysis (2015). https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues

37.

Deshpande S, Park YH, Stamp M (2014) Eigenvalue analysis for metamorphic detection. J Comput Virol Hacking Tech 10(1): 53–65. https://doi.org/10.1007/s11416-013-0193-4

38.

Jidigam RK, Austin TH, Stamp M (2015) Singular value decomposition and metamorphic detection. J Comput Virol Hacking Tech 11(4): 203–216. https://doi.org/10.1007/s11416-014-0220-0

39.

Annadatha A, Stamp M (2017) Image spam analysis and detection. J Comput Virol Hacking Tech [online first] 1–14. https://doi.org/10.1007/s11416-016-0287-x

40.

Norko A (2015) Simple image classification using principal component analysis (PCA). https://ece.gmu.edu/~hayes/courses/MachineLearning/Projects/Presentations/Norko.pdf

41.

Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. In: Proceedings of the 8th international symposium on visualization for cyber security, VizSec ’11. ACM, USA, pp. 4–147. https://doi.org/10.1145/2016904.2016908

42.

Selvin VRS (2017) Malware scores based on image processing. Master’s project, Department of computer science, San Jose state university. http://scholarworks.sjsu.edu/etd_projects/546

43.

Wang W, Battiti R (2006) Identifying intrusions in computer networks with principal component analysis. In: The first international conference on availability, reliability and security, ARES 2006. IEEE, USA. https://doi.org/10.1109/ARES.2006.73

44.

Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM computing surveys (CSUR) 41(3):15CrossRef

45.

Chen T, Hsu YJ, Liu X, Zhang W (2002) Principle component analysis and its variants for biometrics. In: 2002 international conference on image processing, proceedings. IEEE, USA. https://doi.org/10.1109/ICIP.2002.1037959

46.

Sprager S, Zazula D (2009) A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. WSEAS Trans Signal Process 5(11):369–378

47.

Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1): 71–86. https://doi.org/10.1162/jocn.1991.3.1.71

48.

Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other Kernel-based learning methods. Cambridge University Press, CambridgeCrossRef

49.

Law M (2011) A simple introduction to support vector machines. http://www.cise.ufl.edu/class/cis4930sp11dtm/notes/intro_svm_new.pdf

50.

OpenCV: introduction to support vector machines. http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html

51.

Berwick R (2003) An idiots guide to support vector machines (SVMs). http://www.svms.org/tutorials/Berwick2003.pdf

52.

Firdausi I, lim C, Erwin A, Nugroho AS (2010) Analysis of machine learning techniques used in behavior-based malware detection. In: Proceedings of the 2010 second international conference on advances in computing, control, and telecommunication technologies. ACT ’10. IEEE Computer Society, Washington, USA, pp. 201–203. https://doi.org/10.1109/ACT.2010.33

53.

Ye Y, Li T, Adjeroh D, Iyengar SS (2017) A survey on malware detection using data mining techniques. ACM Comput Surv 50(3):41–14140. https://doi.org/10.1145/3073559

54.

Singh T, Troia FD, Visaggio CA, Austin TH, Stamp M (2016) Support vector machines and malware detection. J Comput Virol Hacking Tech 12(4):203–212. https://doi.org/10.1007/s11416-015-0252-0

55.

Rieck K, Trinius P, Willems C, Holz T (2011) Automatic analysis of malware behavior using machine learning. J Comput Secur 19(4):639–668CrossRef

56.

Arp D, Spreitzenbarth M, Gascon H, Rieck K (2014) DREBIN: effective and explainable detection of android malware in your pocket

57.

Chavda A (2017) Image spam detection. Master’s project, Department of computer science, San Jose State University. http://scholarworks.sjsu.edu/etd_projects/543/

58.

Krasser S, Tang Y, Gould J, Alperovitch D, Judge P (2007) Identifying image spam based on header and file properties using c4.5 decision trees and support vector machine learning. In: Proceedings of information assurance and security workshop, IAW ’07. IEEE, USA. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4267526

59.

Hu W (2003) Robust support vector machines for anomaly detection. In: Proceedings of 2003 international conference on machine learning and applications, ICMLA03, pp. 23–24

60.

Khan L, Awad M, Thuraisingham B (2007) A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J 16(4):507–521. https://doi.org/10.1007/s00778-006-0002-5

61.

Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 international joint conference on neural networks, IJCNN’02, vol. 2. IEEE, USA, pp 1702–1707

62.

Drucker H, Wu D, Vapnik V (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5)

63.

Sohn T, Seo J, Moon J (2003) A study on the covert channel detection of tcp/ip header using support vector machine. In: Qing S, Gollmann D, Zhou J (eds) Information and communications security (ICICS 2003), vol 2836. Lecture notes in computer science. Springer, Berlin, pp 313–324CrossRef

64.

Lyu S, Farid H (2004) Steganalysis using color wavelet statistics and one-class support vector machines. Proc SPIE 5306:35–45CrossRef

65.

Heisele B, Serre T, Prentice S, Poggio TA (2003) Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognit 36(9):2007–2017. https://doi.org/10.1016/S0031-3203(03)00062-1

66.

Biggio B, Nelson B, Laskov P (2012) Poisoning attacks against support vector machines. In: Proceedings of the 29th international conference on machine learning, ICML’12. Omnipress, USA, pp 1467–1474. http://dl.acm.org/citation.cfm?id=3042573.3042761

67.

Jain AK, Dubes RC (1988) Algorithms for Clustering Data. Prentice-Hall, USA

68.

Mirkin B (2011) Choosing the number of clusters. http://www.hse.ru/data/2011/06/23/1215441450/noc.pdf

69.

Mooi E, Sarstedt M (2011) Cluster analysis. In: A concise guide to market research. Springer, Berlin, pp 237–284. Chap 9

70.

Tan P-N, Steinbach M, Kumar V (2005) 8. Cluster analysis: basic concepts and algorithms, pp. 487–568. Addison-Wesley, Boston (2005)

71.

Shabalin AA \({K}\)-means clustering. http://shabal.in/visuals/kmeans/1.html

72.

Lad A. EM algorithm for estimating a Gaussian mixture model. http://www.cs.cmu.edu/~alad/em/

73.

Kinable J, Kostakis O (2011) Malware classification based on call graph clustering. J Comput Virol 7(4):233–245. https://doi.org/10.1007/s11416-011-0151-y

74.

Narra U, Troia FD, Visaggio CA, Austin TH, Stamp M (2016) Clustering versus SVM for malware detection. J Comput Virol Hacking Tech 12(4): 213–224. https://doi.org/10.1007/s11416-015-0253-z

75.

Pai S, Troia FD, Visaggio CA, Austin TH, Stamp M (2017) Clustering for malware classification. J Comput Virol Hacking Tech 13(2):95–107. https://doi.org/10.1007/s11416-016-0265-3

76.

Perdisci R (2012) Vamo: towards a fully automated malware clustering validity analysis. In: Proceedings of the 28th annual computer security applications conference, ACSAC ’12. ACM, New York, USA, pp 329–338. https://doi.org/10.1145/2420950.2420999

77.

Wei C, Sprague A, Warner G (2009) Clustering malware-generated spam emails with a novel fuzzy string matching algorithm. In: Proceedings of the 2009 ACM symposium on applied computing, SAC ’09. ACM, New York, USA, pp 889–890. https://doi.org/10.1145/1529282.1529473

78.

Perdisci R, Lee W, Feamster N (2010) Behavioral clustering of http-based malware and signature generation using malicious network traces. In: Proceedings of the 7th USENIX conference on networked systems design and implementation, NSDI’10. USENIX Association, Berkeley, USA, pp. 26–26. http://dl.acm.org/citation.cfm?id=1855711.1855737

79.

Portnoy L, Eskin E, Stolfo S (2001) Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS workshop on data mining applied to security (DMSA-2001), pp 5–8

80.

Gu G, Perdisci R, Zhang J, Lee W (2008) Botminer: clustering analysis of network traffic for protocol- and structure-independent botnet detection. In: Proceedings of the 17th conference on security symposium, SS’08. USENIX Association, Berkeley, USA, pp 139–154. http://dl.acm.org/citation.cfm?id=1496711.1496721

81.

Vaidya J, Clifton C (2003) Privacy-preserving \(k\)-means clustering over vertically partitioned data. In: Proceedings of the Ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’03. ACM, New York, USA, pp. 206–215. https://doi.org/10.1145/956750.956776

82.

Stamp M (2011) Information security: principles and practice, 2nd edn. Wiley, USA (2011)

83.

Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28:84–95CrossRef

84.

Chen T-S, Chang C-C, Hwang M-S (1998) A virtual image cryptosystem based upon vector quantization. IEEE Trans Image Process 7(10):1485–1488MathSciNetCrossRef

85.

Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Proces 6(12):1673–1687CrossRef

86.

Chang C-C, Tai W-L, Lin M-H (2005) A reversible data hiding scheme with modified side match vector quantization. In: 19th international conference on advanced information networking and applications, AINA 2005, vol. 1. IEEE, USA, pp. 947–952

87.

Zheng J, Hu M (2006) An anomaly intrusion detection system based on vector quantization. IEICE Trans Inf Syst 89(1):201–210CrossRef

88.

Ichino M, Kawamoto K, Iwano T, Hatada M, Yoshiura H (2015) Evaluating header information features for malware infection detection. J Inf Process 23(5):603–612

89.

Farag AA, Elhabian SY (2009) A tutorial on data reduction: linear discriminant analysis (LDA). http://www.di.univr.it/documenti/OccorrenzaIns/matdid/matdid437773.pdf

90.

Balakrishnama S, Ganapathiraju A (2007) Linear discriminant analysis — a brief tutorial. http://www.music.mcgill.ca/~ich/classes/mumt611_07/classifiers/lda_theory.pdf

91.

Raschka S (2014) Linear discriminant analysis — Bit by bit. http://sebastianraschka.com/Articles/2014_python_lda.html

92.

Welling M. Fisher linear discriminant analysis. http://www.ics.uci.edu/~welling/classnotes/papers_class/Fisher-LDA.pdf

93.

Farag AA, Elhabian SY (2008) A tutorial on data reduction: linear discriminant analysis (LDA). http://www.di.univr.it/documenti/OccorrenzaIns/matdid/matdid437773.pdf

94.

Kan M, Shan S, Xu D, Chen X (2011) Side-information based linear discriminant analysis for face recognition. BMVC 11:1–12

95.

Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476. https://doi.org/10.1109/TIP.2002.999679

96.

Lu J, Plataniotis KN, Venetsanopoulos AN (2005) Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recog Lett 26(2):181–191CrossRef

97.

Boulgouris NV, Chi ZX (2007) Gait recognition using radon transform and linear discriminant analysis. IEEE Trans Image Process 16(3):731–740MathSciNetCrossRef

98.

Zakka K (2016) A complete guide to \(k\)-nearest-neighbors with applications in python and R. https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/

99.

Liao Y, Vemuri VR (2002) Use of \(k\)-nearest neighbor classifier for intrusion detection. Comput Secur 21(5):439–448CrossRef

100.

Hu J, Gingrich D, Sentosa A (2008) A \(k\)-nearest neighbor approach for user authentication through biometric keystroke dynamics. In: IEEE international conference on communications, ICC’08. IEEE, USA, pp 1556–1560

101.

Elmehdwi Y, Samanthula BK, Jiang W (2014) Secure \(k\)-nearest neighbor query over encrypted data in outsourced environments. In: 30th international conference on data engineering, ICDE 2014. IEEE, USA, pp 664–675

102.

Chen E (2011) How does randomization in a random forest work? https://www.quora.com/How-does-randomization-in-a-random-forest-work

103.

Liaw A, Wiener M (2011) Classification and regression by randomforest. R News 2(3):18–22

104.

Lin Y, Jeon Y (2002) Random forests and adaptive nearest neighbors, Technical Report 1055, Department of statistics, University of Wisconsin. https://www.stat.wisc.edu/sites/default/files/tr1055.pdf

105.

Breiman L, Cutler A (2001) Random forests™. https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

106.

Thaseen S, Kumar CA (2013) An analysis of supervised tree based classifiers for intrusion detection system. In: 2013 International conference on pattern recognition, informatics and mobile engineering, PRIME 2013. IEEE, USA, pp 294–299

107.

Zhang J, Zulkernine M (2006) A hybrid network intrusion detection technique using random forests. In: Proceedings of the first international conference on availability, reliability and security, ARES ’06. IEEE, USA, pp 262–269. https://doi.org/10.1109/ARES.2006.7

108.

Santos I, Brezo F, Ugarte-Pedrero X, Bringas PG (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci 231:64–82MathSciNetCrossRef

109.

Sanz B, Santos I, Laorden C, Ugarte-Pedrero X, Bringas PG, Álvarez G (2013) Puma: permission usage to detect malware in Android. In: International joint conference CISIS12-ICEUTE\({}^\prime \) 12-SOCO\({}^\prime \), vol. 189. Springer, Berlin, pp 289–298

110.

Shabtai A, Fledel Y, Elovici Y (2010) Automated static code analysis for classifying Android applications using machine learning. In: International conference on computational intelligence and security, CIS 2010. IEEE, USA, pp 329–333

111.

Smutz C, Stavrou A (2012) Malicious pdf detection using metadata and structural features. In: Proceedings of the 28th annual computer security applications conference. ACM, USA, pp 239–248

112.

Ma L, Ofoghi B, Watters P, Brown S (2009) Detecting phishing emails using hybrid features. In: Symposia and workshops on ubiquitous, autonomic and trusted computing, UIC-ATC’09. IEEE, USA, pp 493–497

113.

Ghosal V, Tikmani P, Gupta P (2009) Face classification using Gabor wavelets and random forest. In: Canadian conference on computer and robot vision, CRV’09. IEEE, USA, pp 68–73

114.

Nguyen VH, Tran LMS (2010) Predicting vulnerable software components with dependency graphs. In: Proceedings of the 6th international workshop on security measurements and metrics. ACM, USA, p 3

115.

Rojas R (2009) AdaBoost and the super bowl of classifiers: a tutorial introduction to adaptive boosting. http://www.inf.fu-berlin.de/inst/ag-ki/adaboost4.pdf

116.

Stamp M (2017) Boost your knowledge of adaboost. https://www.cs.sjsu.edu/~stamp/ML/files/ada.pdf

117.

Hu W, Maybank S (2008) Adaboost-based algorithm for network intrusion detection. IEEE Trans Syst Man Cyber Part B (Cybernetics) 38(2): 577–583

118.

Miyamoto D, Hazeyama H, Kadobayashi Y (2007) A proposal of the adaboost-based detection of phishing sites. In: Proceedings of the joint workshop on information security

119.

Aswini A, Vinod P (2014) Droid permission miner: Mining prominent permissions for android malware analysis. In: 2014 fifth international conference on the applications of digital information and web technologies (ICADIWT). IEEE, USA, pp. 81–86

120.

Khan MS, Siddiqui S, McLeod RD, Ferens K, Kinsner W (2016) Fractal based adaptive boosting algorithm for cognitive detection of computer malware. In: 5th international conference on cognitive informatics & cognitive computing, ICCI*CC. IEEE, USA, pp 50–59

121.

Yang P, Shan S, Gao W, Li SZ, Zhang D (2004) Face recognition using ada-boosted Gabor features. In: Proceedings of sixth IEEE international conference on automatic face and gesture recognition. IEEE, pp 356–361

Title: A Survey of Machine Learning Algorithms and Their Application in Information Security
Author: Mark Stamp
Publisher: Springer International Publishing
Book: Guide to Vulnerability Analysis for Computer Networks and Systems
Print ISBN: 978-3-319-92623-0

Electronic ISBN: 978-3-319-92624-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-92624-7_2

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