Advances in Complex Systems and Their Applications to Cybersecurity

[1]  Taimoor Akhtar,et al.  Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates , 2016, AAAI.

[2]  Daniel Sánchez,et al.  Text Mining: Techniques, Applications, and Challenges , 2018, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Yoshua Bengio,et al.  Gradient-Based Optimization of Hyperparameters , 2000, Neural Computation.

[4]  Álvaro Herrero,et al.  VISUALIZATION AND CLUSTERING FOR SNMP INTRUSION DETECTION , 2013, Cybern. Syst..

[5]  Terry A. Welch,et al.  A Technique for High-Performance Data Compression , 1984, Computer.

[6]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[7]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[8]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[9]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[10]  Nuno Santos,et al.  Effective Detection of Multimedia Protocol Tunneling using Machine Learning , 2018, USENIX Security Symposium.

[11]  Kevin Leyton-Brown,et al.  Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.

[12]  Héctor Quintián-Pardo,et al.  Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit , 2017, Int. J. Neural Syst..

[13]  Xing Chen,et al.  DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model , 2018, Neurocomputing.

[14]  Tim Kraska,et al.  Automating model search for large scale machine learning , 2015, SoCC.

[15]  Masaki Onishi,et al.  Effective hyperparameter optimization using Nelder-Mead method in deep learning , 2017, IPSJ Transactions on Computer Vision and Applications.

[16]  J. Dunn Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[17]  Ali Feizollah,et al.  Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.

[18]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[19]  Jiye Liang,et al.  Incomplete Multigranulation Rough Set , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[20]  Rafał Renk,et al.  Cyber Threats Impacting Critical Infrastructures , 2016 .

[21]  Saleem Ullah,et al.  Security Issues in the Internet of Things (IoT): A Comprehensive Study , 2017 .

[22]  Roberto Tronci,et al.  Machine Learning in Security Applications , 2015, Trans. Mach. Learn. Data Min..

[23]  Yiyu Yao,et al.  A Partition Model of Granular Computing , 2004, Trans. Rough Sets.

[24]  S. P. Shantharajah,et al.  A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms , 2015 .

[25]  Noemí DeCastro-García,et al.  On Detecting and Removing Superficial Redundancy in Vector Databases , 2018 .

[26]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[27]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[28]  Luo Si,et al.  A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code , 2015, IEEE Transactions on Dependable and Secure Computing.

[29]  Fernando Gomide,et al.  Evolving granular analytics for interval time series forecasting , 2016, Granular Computing.

[30]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[31]  Veelasha Moonsamy,et al.  Mining permission patterns for contrasting clean and malicious android applications , 2014, Future Gener. Comput. Syst..

[32]  Sandro Etalle,et al.  N-Gram against the Machine: On the Feasibility of the N-Gram Network Analysis for Binary Protocols , 2012, RAID.

[33]  Andrzej Bargiela,et al.  The roots of granular computing , 2006, 2006 IEEE International Conference on Granular Computing.

[34]  Tapani Raiko,et al.  Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters , 2015, ICML.

[35]  Eibe Frank,et al.  Accelerating the XGBoost algorithm using GPU computing , 2017, PeerJ Comput. Sci..

[36]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[37]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[38]  Daniel A. Keim,et al.  A Survey of Visualization Systems for Malware Analysis , 2015, EuroVis.

[39]  Wenke Lee,et al.  McPAD: A multiple classifier system for accurate payload-based anomaly detection , 2009, Comput. Networks.

[40]  Zhixiong Lu,et al.  A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering , 2018, Complex..

[41]  Piotr Jedrzejowicz,et al.  An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques , 2018, Complex..

[42]  Noemí DeCastro-García,et al.  Expert knowledge and data analysis for detecting advanced persistent threats , 2017 .

[43]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[44]  AIIDA-SQL: An Adaptive Intelligent Intrusion Detector Agent for detecting SQL Injection attacks , 2010, 2010 10th International Conference on Hybrid Intelligent Systems.

[45]  Simin Nadjm-Tehrani,et al.  Detection and Visualization of Android Malware Behavior , 2016, J. Electr. Comput. Eng..

[46]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[47]  Bijan Davvaz,et al.  Some properties of generalized rough sets , 2013, Inf. Sci..

[48]  P J García Nieto,et al.  A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique. , 2012, The Science of the total environment.

[49]  Saba Arshad,et al.  Android Malware Detection & Protection: A Survey , 2016 .

[50]  Ameet Talwalkar,et al.  Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization , 2016, J. Mach. Learn. Res..

[51]  Kevin Leyton-Brown,et al.  Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.

[52]  W. Loh,et al.  SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .

[53]  Bing Huang,et al.  Information granulation and uncertainty measures in interval-valued intuitionistic fuzzy information systems , 2013, Eur. J. Oper. Res..

[54]  Koen Vanhoof,et al.  Detecting malicious URLs using machine learning techniques , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[55]  Ming-Wen Shao,et al.  Concept granular computing systems and their approximation operators , 2017, Int. J. Mach. Learn. Cybern..

[56]  Curtis Busby-Earle,et al.  The role of machine learning in botnet detection , 2016, 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST).

[57]  Tsau Young Lin,et al.  Granular Computing and Rough Sets , 2005, The Data Mining and Knowledge Discovery Handbook.

[58]  Ricardo Baeza-Yates,et al.  Big Data or Right Data? , 2013, AMW.

[59]  Vijay V. Raghavan,et al.  Big Data: Promises and Problems , 2015, Computer.

[60]  Yang Yuan,et al.  Hyperparameter Optimization: A Spectral Approach , 2017, ICLR.

[61]  James C. Bezdek,et al.  Objective Function Clustering , 1981 .

[62]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[63]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[64]  Christian Wagner,et al.  From Interval-Valued Data to General Type-2 Fuzzy Sets , 2015, IEEE Transactions on Fuzzy Systems.

[65]  Igor Santos,et al.  Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying , 2015, Log. J. IGPL.

[66]  Álvaro Herrero,et al.  Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization , 2008, Innovations in Hybrid Intelligent Systems.

[67]  Vittorio Rosato,et al.  Advanced services for critical infrastructures protection , 2015, Journal of Ambient Intelligence and Humanized Computing.

[68]  Jason J. Jung,et al.  Social big data: Recent achievements and new challenges , 2015, Information Fusion.

[69]  Salvatore J. Stolfo,et al.  Anagram: A Content Anomaly Detector Resistant to Mimicry Attack , 2006, RAID.

[70]  Hajime Inoue,et al.  Comparing Anomaly Detection Techniques for HTTP , 2007, RAID.

[71]  T. L. McCluskey,et al.  Intelligent rule-based phishing websites classification , 2014, IET Inf. Secur..

[72]  Michal Choras,et al.  The Concept of Applying Lifelong Learning Paradigm to Cybersecurity , 2017, ICIC.

[73]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[74]  Tim Oates,et al.  Efficient progressive sampling , 1999, KDD '99.

[75]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[76]  Ryan P. Adams,et al.  Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.

[77]  Witold Pedrycz,et al.  Building the fundamentals of granular computing: A principle of justifiable granularity , 2013, Appl. Soft Comput..

[78]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[79]  Eliane Tschaen Barbieri,et al.  The YouTube Jihadists: A Social Network Analysis of Al-Muhajiroun’s Propaganda Campaign , 2012 .

[80]  B. Wujek,et al.  Automated Hyperparameter Tuning for Effective Machine Learning , 2017 .

[81]  Aladdin Ayesh,et al.  Intelligent intrusion detection systems using artificial neural networks , 2018, ICT Express.

[82]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[83]  Jianhua Zhang,et al.  Predicting electrical power output by using Granular Computing based Neuro-Fuzzy modeling method , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[84]  Jacques Klein,et al.  IccTA: Detecting Inter-Component Privacy Leaks in Android Apps , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[85]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[86]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[87]  Kevin Leyton-Brown,et al.  Efficient Benchmarking of Hyperparameter Optimizers via Surrogates , 2015, AAAI.

[88]  Feng Gao,et al.  Reduction of Large Training Set by Guided Progressive Sampling: Application to Neonatal Intensive Care Data , 2007 .

[89]  Adel M. Alimi,et al.  Fuzzy Rules for Ant Based Clustering Algorithm , 2016, Adv. Fuzzy Syst..

[90]  Der-Bang Wu,et al.  Fuzzy C-mean algorithm based on “complete” Mahalanobis distances , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[91]  T. L. McCluskey,et al.  An assessment of features related to phishing websites using an automated technique , 2012, 2012 International Conference for Internet Technology and Secured Transactions.

[92]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[93]  Massimiliano Zanin,et al.  Credit card fraud detection through parenclitic network analysis , 2017, Complex..

[94]  Jinhai Li,et al.  Parallel computing techniques for concept-cognitive learning based on granular computing , 2018, International Journal of Machine Learning and Cybernetics.

[95]  James C. Bezdek,et al.  Efficient Implementation of the Fuzzy c-Means Clustering Algorithms , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[96]  W. Loh,et al.  REGRESSION TREES WITH UNBIASED VARIABLE SELECTION AND INTERACTION DETECTION , 2002 .

[97]  Yao Zhang,et al.  A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data , 2017, Sci. Program..

[98]  Yiyu Yao,et al.  Granular Computing , 2008 .

[99]  Mansour Ahmadi,et al.  DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware , 2017, CODASPY.

[100]  Manoranjan Dash,et al.  An Evaluation of Progressive Sampling for Imbalanced Data Sets , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[101]  Alan F. Smeaton,et al.  Combining Social Network Analysis and Sentiment Analysis to Explore the Potential for Online Radicalisation , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[102]  Won Ryu,et al.  Analyzing and detecting method of Android malware via disassembling and visualization , 2014, 2014 International Conference on Information and Communication Technology Convergence (ICTC).

[103]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[104]  Rupinder Singh,et al.  Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks , 2017, Wirel. Commun. Mob. Comput..

[105]  José Luís Calvo-Rolle,et al.  Expert condition monitoring on hydrostatic self-levitating bearings , 2013, Expert Syst. Appl..

[106]  Nicola Horsburgh,et al.  Strengths and Weaknesses of Grassroot Jihadist Networks: The Madrid Bombings , 2008 .

[107]  Vicente Matellán Olivera,et al.  Detection of Cyber-attacks to indoor real time localization systems for autonomous robots , 2018, Robotics Auton. Syst..

[108]  Abraham Lempel,et al.  A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.

[109]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[110]  Zhou Su,et al.  Big data in mobile social networks: a QoE-oriented framework , 2016, IEEE Network.

[111]  Koen Vanhoof,et al.  A Granular Intrusion Detection System Using Rough Cognitive Networks , 2016, Recent Advances in Computational Intelligence in Defense and Security.

[112]  Álvaro Herrero,et al.  Neural Analysis of HTTP Traffic for Web Attack Detection , 2015, CISIS-ICEUTE.

[113]  José Luís Calvo-Rolle,et al.  Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions , 2014, Sensors.

[114]  Daniel Sánchez,et al.  Mining Text Data: Special Features and Patterns , 2002, Pattern Detection and Discovery.

[115]  Maysam F. Abbod,et al.  Granular computing approach for the design of medical data classification systems , 2015, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[116]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[117]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[118]  Budi Rahardjo,et al.  Attack scenarios and security analysis of MQTT communication protocol in IoT system , 2017, 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI).

[119]  Witold Pedrycz,et al.  Analysis of spatiotemporal data relationship using information granules , 2015, International Journal of Machine Learning and Cybernetics.

[120]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[121]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[122]  Yuval Elovici,et al.  ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis , 2017, SAC.

[123]  Konrad Rieck,et al.  DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.

[124]  Francisco Javier de Cos Juez,et al.  Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions , 2017, J. Comput. Appl. Math..

[125]  Reid A. Johnson,et al.  Calibrating Probability with Undersampling for Unbalanced Classification , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[126]  Héctor Quintián-Pardo,et al.  Gaining deep knowledge of Android malware families through dimensionality reduction techniques , 2018, Log. J. IGPL.

[127]  T. L. McCluskey,et al.  Predicting phishing websites based on self-structuring neural network , 2013, Neural Computing and Applications.

[128]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[129]  Chengying Mao,et al.  A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk , 2018, Complex..

[130]  Thomas Bartz-Beielstein,et al.  Tuned data mining: a benchmark study on different tuners , 2011, GECCO '11.

[131]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[132]  Martin J. Wainwright,et al.  Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.

[133]  X. C. Guo,et al.  A novel LS-SVMs hyper-parameter selection based on particle swarm optimization , 2008, Neurocomputing.

[134]  Francisco Javier de Cos Juez,et al.  Artificial neural networks applied to cancer detection in a breast screening programme , 2010, Math. Comput. Model..

[135]  Peter D. Turney Types of Cost in Inductive Concept Learning , 2002, ArXiv.

[136]  Stephanie Forrest,et al.  Learning DFA representations of HTTP for protecting web applications , 2007, Comput. Networks.

[137]  Israa Abdzaid Atiyah,et al.  KC-Means: A Fast Fuzzy Clustering , 2018, Adv. Fuzzy Syst..

[138]  P. J. García Nieto,et al.  Using multivariate adaptive regression splines and multilayer perceptron networks to evaluate paper manufactured using Eucalyptus globulus , 2012, Appl. Math. Comput..

[139]  Paolo Rosso,et al.  Exploring high-level features for detecting cyberpedophilia , 2014, Comput. Speech Lang..

[140]  David D. Cox,et al.  Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms , 2013, SciPy.

[141]  Salvatore J. Stolfo,et al.  Anomalous Payload-Based Network Intrusion Detection , 2004, RAID.

[142]  Kwangjo Kim,et al.  Wi-Fi intrusion detection using weighted-feature selection for neural networks classifier , 2017, 2017 International Workshop on Big Data and Information Security (IWBIS).

[143]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[144]  Srinivas Mukkamala,et al.  Mobile malware visual analytics and similarities of Attack Toolkits (Malware gene analysis) , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[145]  Mansour Ahmadi,et al.  Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification , 2015, CODASPY.

[146]  Pieter H. Hartel,et al.  POSEIDON: a 2-tier anomaly-based network intrusion detection system , 2006, Fourth IEEE International Workshop on Information Assurance (IWIA'06).

[147]  Álvaro Herrero,et al.  Key features for the characterization of Android malware families , 2017, Log. J. IGPL.

[148]  Liangxing Fang,et al.  Research on economic dispatch of large power grid based on granular computing , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[149]  N. Christakis,et al.  Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study , 2008, BMJ : British Medical Journal.

[150]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[151]  Xing-Kong Ma,et al.  Attentional Payload Anomaly Detector for Web Applications , 2018, ICONIP.

[152]  M. Jayasree,et al.  Recognizing faces from surgically altered face images using granular approach , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[153]  Ameet Talwalkar,et al.  Non-stochastic Best Arm Identification and Hyperparameter Optimization , 2015, AISTATS.

[154]  Álvaro Herrero,et al.  Neural Visualization of Android Malware Families , 2016, SOCO-CISIS-ICEUTE.

[155]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..