A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

2008, Estonia Fines Man for ‘Cyber War’

Dignan, 2008, Amazon Exploits Its S3 Outage

Dekker, 2013, Cloud security incident reporting: Framework for reporting about major cloud security incidents

2014, Ello Social Network Hit by Suspected Bloody DDoS Attack

10.1109/DSN.2005.18

2014, Cisco Anual Report

2017, Cisco annual cyber security report

2017, Snort 2.9.7.6

2018, Suricata 4.0.4

Lunt, Ides: An intelligent system for detecting intruders, Proc. Symp. Comput. Security Threat Countermeasures, 30

Ertöz, 2004, MINDS-minnesota intrusion detection system, Next Generation Data Mining, 199

1999, KDD Cup 1999 Data

10.1109/MilCIS.2015.7348942

10.1145/2716260

10.1109/IKT.2013.6620045

10.1007/978-3-540-89900-6_20

10.1201/b16390

10.1109/IJCNN.1989.118638

Quinlan, 1993, C4.5: Programs for Machine Learning

10.1007/BF00994018

Kumar, 2007, Survey of Current Network Intrusion Detection Techniques, 1

10.1016/j.jnca.2015.11.016

10.1016/j.cose.2008.08.003

10.1109/WICT.2011.6141351

10.1109/5.58325

10.1016/j.comcom.2012.01.016

10.1007/978-3-662-48386-2_49

10.1016/j.eswa.2013.08.066

10.1109/COMST.2015.2402161

Zhang, 2003, Intrusion detection techniques for mobile wireless networks, Wireless Netw., 9, 545, 10.1023/A:1024600519144

10.1007/s10207-016-0335-z

Halilovic, 2012, Intrusion detection on smartphones, arXiv e-print 1211.6610, 1

10.1371/journal.pone.0150077

10.1007/s10844-010-0148-x

Sikder, 6thSense: A context-aware sensor-based attack detector for smart devices, Proc. 26th USENIX Security Symp., 397

10.1109/COMST.2014.2386139

10.1016/j.jnca.2016.10.015

10.1016/j.jnca.2017.06.001

10.1016/j.procs.2015.08.220

Haq, 2015, Application of machine learning approaches in intrusion detection system: A survey, Int. J. Adv. Res. Artif. Intell., 4, 9

10.1109/COMST.2015.2494502

10.1007/978-3-319-39381-0_19

10.1007/978-3-319-11933-5_44

10.1109/CASoN.2013.6622595

10.3758/BF03195358

Crenshaw, 2008, OSfuscate: Change Your Windows OS TCP/IP Fingerprint to Confuse P0f, NetworkMiner, Ettercap, Nmap and Other OS Detection Tools

Norton, 2004, An Ettercap Primer

10.1016/j.jnca.2013.08.001

10.1504/IJICS.2015.073028

10.1007/s10994-014-5473-9

Guillén, Detection of non-content based attacks using GA with extended KDD features, Proc. World Congr. Eng. Comput. Sci., 30

10.4018/978-1-4666-8761-5.ch007

1999, Darpa Intrusion Detection Attacks Database

10.5815/ijieeb.2015.06.03

10.1109/CCCS.2015.7374128

10.1109/65.681925

10.1007/978-3-319-24315-3_4

10.1109/SURV.2013.102913.00020

10.1109/IADCC.2010.5422935

10.1145/1978582.1978597

10.1016/j.comcom.2011.07.001

10.21236/ADA406438

10.1007/s007660200016

10.1109/.2006.1629454

10.1007/978-3-319-17172-2_11

10.1109/ICCKE.2015.7365863

10.1007/978-81-322-2523-2_32

10.1007/978-1-4842-1712-2_8

10.1007/s10676-005-4585-0

10.1007/978-0-387-88771-5_1

10.1109/TSE.2010.60

Singh, 2009, A survey of cyber attack detection systems, Int. J. Comput. Sci. Netw. Security, 9, 1

Sabhnani, KDD feature set complaint heuristic rules for R2L attack detection, Proc. Security Manag., 310

10.1007/978-3-319-23204-1_15

10.1109/ICCCT.2012.69

10.1002/cpe.4133

10.3233/JIFS-169234

10.1016/j.cose.2011.12.012

10.1007/978-0-387-34749-3_34

Quinlan, 1993, C4.5: Programs for Machine Learning

10.1007/PL00011678

Mitchell, 1997, Machine Learning

10.1080/01431160110055804

Moya, 1993, One-class classifier networks for target recognition applications

10.1016/S0893-6080(00)00060-5

10.1109/IJCNN.1991.155491

10.1109/72.661127

Bivens, 2002, Network-based intrusion detection using neural networks, Intell. Eng. Syst. Artif. Neural Netw., 12, 579

John, Estimating continuous distributions in Bayesian classifiers, Proc. 11th Conf. Uncertainty Artif. Intell., 338

McCallum, A comparison of event models for naive Bayes text classification, Proc. AAAI Workshop Learn. Text Categorization, 752, 41

10.1007/978-3-540-30549-1_43

10.1109/TKDE.2008.234

10.1016/j.cor.2004.03.019

10.1007/s10462-007-9052-3

10.1007/s11235-011-9541-1

10.1137/1.9781611974348.25

Schölkopf, Support vector method for novelty detection, Proc. Adv. Neural Inf. Process. Syst., 582

10.1109/CISIM.2008.49

Selvakani, 2007, Genetic algorithm for framing rules for intrusion detection, Int. J. Comput. Sci. Netw. Security, 7, 285

10.1007/978-3-642-22555-0_14

10.1016/j.eswa.2005.05.002

10.1016/j.datak.2007.03.016

10.1145/1541880.1541882

10.1016/j.asoc.2015.10.011

10.1145/283751.283845

10.1016/S0019-9958(65)90241-X

10.1002/sec.1547

10.1007/s40815-016-0147-3

Gyanchandani, 2012, Taxonomy of anomaly based intrusion detection system: A review, Int. J. Sci. Res. Publ., 2, 1

10.1109/MASSP.1986.1165342

Ariu, HMMPayl: An application of HMM to the analysis of the HTTP payload, Proc. WAPA, 81

10.1186/1471-2105-9-224

10.1016/j.cose.2011.08.009

Abadi, 2006, An ant colony optimization algorithm for network vulnerability analysis, Iran. J. Elect. Elect. Eng., 2, 106

10.1007/978-3-540-74089-6_2

10.1201/b12207

Sewell, 2008, Ensemble learning, Res. Note, 11, 1

10.1006/inco.1995.1136

10.3103/S0146411616010028

10.1007/978-3-540-45235-5_73

10.1145/967900.967989

Bouzida, Neural networks vs. decision trees for intrusion detection, Proc. IEEE/IST Workshop Monitor. Attack Detection Mitigation (MonAM), 28, 29

10.1016/j.patrec.2004.09.045

10.1016/j.jnca.2004.01.003

10.1109/ARES.2006.73

10.1007/s00778-006-0002-5

10.1007/978-3-540-71549-8_12

10.1002/int.20203

10.1016/j.patrec.2008.01.008

10.1016/j.cpc.2009.05.004

10.1016/j.asoc.2008.06.001

10.1016/j.eswa.2010.02.102

10.1016/j.jnca.2011.01.002

10.1016/j.eswa.2010.06.066

10.1007/978-3-642-34500-5_64

10.1016/j.asoc.2012.05.004

10.1016/j.eswa.2011.06.013

10.1016/j.eswa.2011.07.032

10.1109/ICCCI.2013.6466310

10.1109/ICACCCT.2014.7019145

10.1016/j.asoc.2014.01.028

10.1016/j.future.2013.06.027

10.1016/j.eswa.2012.07.009

10.1016/j.knosys.2015.01.009

Amoli, 2016, Unsupervised network intrusion detection systems for zero-day fast-spreading attacks and botnets, Int. J. Digit. Content Technol. Its Appl., 10, 1

10.1007/978-3-319-08624-8_8

10.1016/j.comcom.2007.05.002

10.1016/j.eswa.2014.08.002

Yassin, Anomaly-based intrusion detection through k-means clustering and Naives Bayes classification, Proc. 4th Int. Conf. Comput. Informat. (ICOCI), 298

10.1109/TDSC.2008.20

Mamun, An entropy based encrypted traffic classifier, Proc. Int. Conf. Inf. Commun. Security, 282

10.1109/ICISSEC.2016.7885853

10.1109/ISTEL.2016.7881798

Chowdhury, Network intrusion detection using machine learning, Proc. Int. Conf. Security Manag. (SAM), 1

Moustafa, A hybrid feature selection for network intrusion detection systems: Central points, Proc. 16th Aust. Inf. Warfare Conf., 1

10.1109/ICCCI.2013.6466310

10.1016/j.comnet.2017.03.018

10.1016/j.knosys.2017.09.014

10.1016/j.eswa.2017.07.005

10.1109/TC.2016.2519914

10.1016/j.neucom.2016.03.031

10.1007/978-94-015-7744-1_2

Olusola, Analysis of KDD ’99 intrusion detection dataset for selection of relevance features, Proc. World Congr. Eng. Comput. Sci., 1, 20

Kayacik, Selecting features for intrusion detection: A feature relevance analysis on KDD 99 intrusion detection datasets, Proc. 3rd Annu. Conf. Privacy Security Trust, 1

10.1109/HPCC-SmartCity-DSS.2016.0141

2016, Weka 3.8.1: Data Mining Software in Java

Pedregosa, 2011, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825

2017, Installing Tensorflow

2017, Knime 3.4.1: Download Knime Analytics Platform & SDK

2017, Real Data Science, Fast and Simple (Stable Release 7.5)

10.1007/978-3-540-69497-7_41

2014, MOA (Massive Online Analysis)

10.1109/ICDMW.2014.24

Meng, 2016, MLlib: Machine learning in apache spark, J. Mach. Learn. Res., 17, 1235

10.1561/2000000039

Aminanto, Deep learning in intrusion detection system: An overview, Proc. Int. Res. Conf. Eng. Technol., 1

10.1109/MALWARE.2015.7413680

Wang, The applications of deep learning on traffic identification, BlackHat, 1

10.14257/ijsia.2015.9.5.21

Yan, On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach, Proc. Annu. Conf. Prognostics Health Manag. Soc., 1

10.1109/CBD.2014.41

Jung, Poster: Deep learning for zero-day flash malware detection, Proc. 36th IEEE Symp. Security Privacy, 1

10.1038/nature16961

10.1016/j.neucom.2012.11.050

10.1109/TST.2016.7399288

10.1002/sec.1441

10.1007/978-3-642-20505-7_26

10.13089/JKIISC.2016.26.1.197

10.1109/ICCSN.2016.7586590

10.1016/j.engappai.2015.01.013

10.1007/978-3-540-77949-0_15

10.1007/11538059_103

10.1007/BF00115009

1998, UNM Dataset

Hodo, 2017, Shallow and deep networks intrusion detection system: A taxonomy and survey, ACM Survey

10.1109/MEC.2011.6025669

10.1609/aaai.v30i1.10295