A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Tóm tắt
Từ khóa
Tài liệu tham khảo
zhengbing, 0, A novel network intrusion detection system (NIDS) based on signatures search of data mining, Proc 1st Int Conf Forensic Appl Techn Telecommun Inf Multimedia Workshop (e-Forensics ‘08), 10
baralis, 2008, Generalized Itemset Discovery by Means of Opportunistic Aggregation
2014, Politecnico di Torino Analyzer 3 0
han, 0, Using data mining to discover signatures in network-based intrusion detection, Proc IEEE Comput Graph Appl, 212
bivens, 2002, Network-based intrusion detection using neural networks, Intell Eng Syst Artificial Neural Networks, 12, 579
stolfo, 2014, Kdd Cup 1999 Data Set
tavallaee, 0, A detailed analysis of the KDD Cup 1999 data set, Proc IEEE Symp Comput Intell Secur Defense Appl, 1
cannady, 0, Artificial neural networks for misuse detection, Proc 1998 Nat Inf Syst Secur Conf, 443
brauckhoff, 0, Flame: A low-level anomaly modeling engine, Proc Conf Cyber Security Exper Test
2015, IBM
freund, 0, Experiments with a new boosting algorithm, Proc 13th Int Conf Mach Learn, 96, 148
quinlan, 1993, C4 5 Programs for Machine Learning
shearer, 2000, The CRISP-DM model: The new blueprint for data mining, J Data Warehousing, 5, 13
jain, 1988, Algorithms for clustering data
benferhat, 0, A Naïve Bayes approach for detecting coordinated attacks, Proc 32nd Annu IEEE Int Comput Software Appl Conf, 704
leung, 0, Unsupervised anomaly detection in network intrusion detection using clusters, Proc 28th Australas Conf Comput Sci, 38, 333
livadas, 0, Using machine learning techniques to identify botnet traffic, Proc 31st IEEE Conf Local Comput Netw, 967
li, 0, Using genetic algorithms for network intrusion detection, Proc U S Dept Energy Cyber Secur Group 2004 Train Conf, 1
khan, 2011, Rule-based network intrusion detection using genetic algorithms, Int J Comput Appl, 18, 26
jolliffe, 2002, Principal Component Analysis
abraham, 2007, Evolutionary design of intrusion detection programs, Int J Netw Secur, 4, 328
markov, 1971, Extension of the limit theorems of probability theory to a sum of variables connected in a chain, Dynamic Probabilistic Systems, 1
long, 2007, Boosting the area under the ROC curve, Adv Neural Inf Process Syst, 945
koza, 1992, Genetic Programming On the Programming of Computers by Means of Natural Selection
mukkamala, 2005, Cyber security challenges: Designing efficient intrusion detection systems and antivirus tools, Enhancing Computer Security With Smart Technology, 125
ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, Knowl Discov Data Min, 96, 226
vapnik, 2010, The Nature of Statistical Learning Theory
agrawal, 1996, Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, 12, 307
guang-bin, 2011, Extreme learning machines: A survey, Int J Mach Learn Cybern, 2, 107, 10.1007/s13042-011-0019-y
amor, 0, Naïve Bayes vs. decision trees in intrusion detection systems, Proc ACM Symp Appl Comput, 420
panda, 2007, Network intrusion detection using Naive Bayes, Int J Comput Sci Netw Secur, 7, 258
paxson, 2004, Bro 0 9
hu, 0, Robust support vector machines for anomaly detection in computer security, Proc 20th Int Conf Mach Learn, 282
2000, R Core Team
jacobson, 1989, The tcpdump manual page
combs, 2014, Wireshark
2014, The Source
lyon, 2009, Nmap Network Scanning The Official Nmap Project Guide to Network Discovery and Security Scanning
arnes, 2006, Using Hidden markov models to evaluate the risks of intrusions: System architecture and model validation, Lect Notes Comput Sci, 145, 10.1007/11856214_8
dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm, J Roy Statist Soc, 1
ahsan, 0, Practical data hiding in TCP/IP, Proc ACM Multimedia Secur Workshop, 2
witten, 2011, Data Mining Practical Machine Learning Tools and Techniques
lee, 0, A data mining framework for building intrusion detection models, Proc IEEE Symp Secur Privacy, 120