Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
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Dongxi Liu | Vijay Varadharajan | Ibrahim A. Hameed | Suhuai Luo | Shan Chen | Kamran Shaukat | Jiaming Li | V. Varadharajan | K. Shaukat | I. Hameed | S. Luo | Jiaming Li | Dongxi Liu | Shan Chen
[1] Zhenlong Yuan,et al. DroidDetector: Android Malware Characterization and Detection Using Deep Learning , 2016 .
[2] Aziz Mohaisen,et al. Unveiling Zeus: automated classification of malware samples , 2013, WWW.
[3] Youssef B. Mahdy,et al. Behavior-based features model for malware detection , 2016, Journal of Computer Virology and Hacking Techniques.
[4] Nadine Hajj,et al. Deep belief networks and cortical algorithms: A comparative study for supervised classification , 2019, Applied Computing and Informatics.
[5] Joachim Fabini,et al. Malware propagation in smart grid networks: metrics, simulation and comparison of three malware types , 2018, Journal of Computer Virology and Hacking Techniques.
[6] J.B. Grizzard,et al. An investigation of a compromised host on a honeynet being used to increase the security of a large enterprise network , 2004, Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, 2004..
[7] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[8] Munam Ali Shah,et al. Analysis of machine learning solutions to detect malware in android , 2016, 2016 Sixth International Conference on Innovative Computing Technology (INTECH).
[9] Tao Li,et al. An intelligent PE-malware detection system based on association mining , 2008, Journal in Computer Virology.
[10] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[11] Ashkan Sami,et al. Using feature generation from API calls for malware detection , 2014 .
[12] P. J. García-Nieto,et al. Review: machine learning techniques applied to cybersecurity , 2019, International Journal of Machine Learning and Cybernetics.
[13] Banshidhar Majhi,et al. Progress in Intelligent Computing Techniques: Theory, Practice, and Applications , 2018 .
[14] Dewan Md Farid,et al. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).
[15] Sulaiman Mohd Nor,et al. FEATURE SELECTION AND MACHINE LEARNING CLASSIFICATION FOR MALWARE DETECTION , 2015 .
[16] Qiang Ye,et al. A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks , 2019, Inf. Fusion.
[17] Jasmin Kevric,et al. An effective combining classifier approach using tree algorithms for network intrusion detection , 2017, Neural Computing and Applications.
[18] Hemanta Kumar Kalita,et al. Analysis of Machine Learning Techniques Based Intrusion Detection Systems , 2016 .
[19] Manas Ranjan Patra,et al. NETWORK INTRUSION DETECTION USING NAÏVE BAYES , 2007 .
[20] S. M. Elseuofi,et al. MACHINE LEARNING METHODS FOR SPAM E-MAIL CLASSIFICATION , 2011 .
[21] Alwyn Roshan Pais,et al. Detection of phishing websites using an efficient feature-based machine learning framework , 2018, Neural Computing and Applications.
[22] Parikshit N. Mahalle,et al. A Comparative Analysis and Discussion of Email Spam Classification Methods Using Machine Learning Techniques , 2019 .
[23] Chun-I Fan,et al. Malware Detection Systems Based on API Log Data Mining , 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.
[24] A. Nur Zincir-Heywood,et al. User identification via neural network based language models , 2019, Int. J. Netw. Manag..
[25] Ahmed Ahmim,et al. A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models , 2018, 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).
[26] Yuval Elovici,et al. Detecting unknown malicious code by applying classification techniques on OpCode patterns , 2012, Security Informatics.
[27] Haruna Chiroma,et al. Machine learning for email spam filtering: review, approaches and open research problems , 2019, Heliyon.
[28] Roberto Baldoni,et al. Survey on the Usage of Machine Learning Techniques for Malware Analysis , 2017, Comput. Secur..
[29] K. P. Soman,et al. Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.
[30] Bhavani M. Thuraisingham,et al. A new intrusion detection system using support vector machines and hierarchical clustering , 2007, The VLDB Journal.
[31] Dharmaraj R. Patil,et al. Implementation of network intrusion detection system using variant of decision tree algorithm , 2015, 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE).
[32] Ying Gao,et al. A Distributed Network Intrusion Detection System for Distributed Denial of Service Attacks in Vehicular Ad Hoc Network , 2019, IEEE Access.
[33] Ana Isabel Canhoto,et al. Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential , 2020 .
[34] Ammar Almomani,et al. Machine Learning for Phishing Detection and Mitigation , 2019 .
[35] P. V. Shijo,et al. Integrated Static and Dynamic Analysis for Malware Detection , 2015 .
[36] Ramakrishnan Srikant,et al. Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.
[37] Shi-Jinn Horng,et al. A novel intrusion detection system based on hierarchical clustering and support vector machines , 2011, Expert Syst. Appl..
[38] Haytham Elmiligi,et al. The Curious Case of Machine Learning In Malware Detection , 2019, ICISSP.
[39] Aristidis Likas,et al. Deep Belief Networks for Spam Filtering , 2007 .
[40] Kamran Shaukat,et al. Student’s Performance: A Data Mining Perspective , 2017 .
[41] Kamran Shaukat,et al. Student's performance in the context of data mining , 2016, 2016 19th International Multi-Topic Conference (INMIC).
[42] Jian-hua Li,et al. Cyber security meets artificial intelligence: a survey , 2018, Frontiers of Information Technology & Electronic Engineering.
[43] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[44] Rui Li,et al. A Behavior-Based Approach for Malware Detection , 2017, IFIP Int. Conf. Digital Forensics.
[45] Anamika Yadav,et al. Decision Tree Based Intrusion Detection System for NSL-KDD Dataset , 2017 .
[46] K. P. Soman,et al. DeepImageSpam: Deep Learning based Image Spam Detection , 2018, ArXiv.
[47] Azween Abdullah,et al. Artificial neural network approaches to intrusion detection: a review , 2009, IEEE ICT 2009.
[48] Haengnam Sung,et al. A Comparative Study on the Performance of Intrusion Detection using Decision Tree and Artificial Neural Network Models , 2015 .
[49] Shaoning Pang,et al. Multiple sequence alignment and artificial neural networks for malicious software detection , 2012, 2012 8th International Conference on Natural Computation.
[50] Thomas H. Morris,et al. Machine Learning and Cyber Security , 2017, 2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE).
[51] Ali A. Ghorbani,et al. A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.
[52] Ohm Sornil,et al. Classification of malware families based on N-grams sequential pattern features , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).
[53] Eric A. Fischer. Creating a National Framework for Cybersecurity: An analysis of Issues and Options , 2005 .
[54] Liangxiao Jiang,et al. A Novel Bayes Model: Hidden Naive Bayes , 2009, IEEE Transactions on Knowledge and Data Engineering.
[55] Ali A. Ghorbani,et al. Comparative Study of Supervised Machine Learning Techniques for Intrusion Detection , 2007, Fifth Annual Conference on Communication Networks and Services Research (CNSR '07).
[56] Jiankun Hu,et al. Evaluating host-based anomaly detection systems: Application of the one-class SVM algorithm to ADFA-LD , 2014, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[57] Ali A. Ghorbani,et al. DroidKin: Lightweight Detection of Android Apps Similarity , 2014, SecureComm.
[58] Michele Colajanni,et al. On the effectiveness of machine and deep learning for cyber security , 2018, 2018 10th International Conference on Cyber Conflict (CyCon).
[59] Swapan Purkait,et al. Information Management & Computer Security Phishing counter measures and their effectiveness – literature review , 2016 .
[60] Guang Cheng,et al. An Efficient Network Intrusion Detection System Based on Feature Selection and Ensemble Classifier , 2019, ArXiv.
[61] Akshita Tyagi. Content Based Spam Classification- A Deep Learning Approach , 2016 .
[62] Adamu I. Abubakar,et al. A Review on Mobile SMS Spam Filtering Techniques , 2017, IEEE Access.
[63] Anazida Zainal,et al. Spam detection using hybrid Artificial Neural Network and Genetic algorithm , 2013, 2013 13th International Conference on Intellient Systems Design and Applications.
[64] M. Bassiouni,et al. Ham and Spam E-Mails Classification Using Machine Learning Techniques , 2018 .
[65] Jun Zhang,et al. A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection , 2015, IEEE Transactions on Computational Social Systems.
[66] Sachin Ahuja,et al. Machine learning and its applications: A review , 2017, 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC).
[67] Kalamullah Ramli,et al. Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS) , 2015, 2015 International Conference on Quality in Research (QiR).
[68] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[69] Yudong Zhang,et al. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection , 2014, Knowl. Based Syst..
[70] P. Shanthi Bala,et al. Cyber Threats Detection and Mitigation Using Machine Learning , 2020 .
[71] Alva Erwin,et al. Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection , 2010, 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies.
[72] Wei Huang,et al. A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine , 2017 .
[73] Nayyer Masood,et al. Dengue Fever Prediction: A Data Mining Problem , 2015 .
[74] 이상헌,et al. Deep Belief Networks , 2010, Encyclopedia of Machine Learning.
[75] Sebastian Zander,et al. Intrusion Detection System using Ripple Down Rule learner and Genetic Algorithm , 2014 .
[76] Vineet Richariya,et al. Intrusion Detection in KDD99 Dataset using SVM-PSO and Feature Reduction with Information Gain , 2014 .
[77] V. Tiwari,et al. Enhanced Method for Intrusion Detection over KDD Cup 99 Dataset , 2016 .
[78] Vacius Jusas,et al. Logical filter approach for early stage cyber-attack detection , 2019, Comput. Sci. Inf. Syst..
[79] Issa Joseph Alkaht,et al. Filtering SPAM Using Several Stages Neural Networks , 2016 .
[80] Farrukh Aslam Khan,et al. A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection , 2018, Cluster Computing.
[81] Shamkant B. Navathe,et al. Managing vulnerabilities of information systems to security incidents , 2003, ICEC '03.
[82] Kamran Shaukat,et al. A Socio-Technological analysis of Cyber Crime and Cyber Security in Pakistan , 2017 .
[83] Christian Viard-Gaudin,et al. A Convolutional Neural Network Approach for Objective Video Quality Assessment , 2006, IEEE Transactions on Neural Networks.
[84] Abbas Javed,et al. RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection , 2019, Comput..
[85] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[86] Ali A. Ghorbani,et al. Toward developing a systematic approach to generate benchmark datasets for intrusion detection , 2012, Comput. Secur..
[87] Yehuda Afek,et al. Zero-Day Signature Extraction for High-Volume Attacks , 2019, IEEE/ACM Transactions on Networking.
[88] Anuradha Pillai,et al. Applications of Machine Learning in Cyber Security , 2020, Handbook of Research on Machine and Deep Learning Applications for Cyber Security.
[89] Mohammed Awad,et al. EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION , 2016 .
[90] S. K. Sharma,et al. An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).
[91] Bhavna Dharamkar,et al. A Review of Cyber Attack Classification Technique Based on Data Mining and Neural Network Approach , 2014 .
[92] Sailesh Suryanarayan Iyer,et al. Applications of Machine Learning in Cyber Security Domain , 2020 .
[93] M. Soranamageswari,et al. A Novel Approach towards Image Spam Classification , 2011 .
[94] Sumant Sharma,et al. Adaptive Approach for Spam Detection , 2013 .
[95] Adel Ammar. A Decision Tree Classifier for Intrusion Detection Priority Tagging , 2015 .
[96] Khan Muhammad,et al. A local and global event sentiment based efficient stock exchange forecasting using deep learning , 2020, Int. J. Inf. Manag..
[97] Dan Craigen,et al. Defining Cybersecurity , 2014 .
[98] Mohamed Amine Ferrag,et al. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study , 2020, J. Inf. Secur. Appl..
[99] Anil K. Jain,et al. Artificial Neural Networks: A Tutorial , 1996, Computer.
[100] Christian Igel,et al. An Introduction to Restricted Boltzmann Machines , 2012, CIARP.
[101] Bruce Ndibanje,et al. Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction , 2019, Applied Sciences.
[102] Roberto Baldoni,et al. Survey on the Usage of Machine Learning Techniques for Malware Analysis , 2017, ArXiv.
[103] Usman Qamar,et al. Text Mining Approach to Detect Spam in Emails , 2016 .
[104] Nasir Fareed Shah,et al. A Comparative Analysis of Various Spam Classifications , 2018 .
[105] Kijun Han,et al. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles , 2019, IEEE Access.
[106] Vacius Jusas,et al. Classification of Motor Imagery Using Combination of Feature Extraction and Reduction Methods for Brain-Computer Interface , 2019, Inf. Technol. Control..
[107] Xiao Chun Yin,et al. Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach , 2019, Sensors.
[108] Vijay Varadharajan,et al. A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection , 2019, IEEE Communications Surveys & Tutorials.
[109] Carlos Borrego,et al. Applications in Security and Evasions in Machine Learning: A Survey , 2020 .
[110] Robert C. Atkinson,et al. Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey , 2017, ArXiv.
[111] K. Renuka,et al. A Hybrid ACO Based Feature Selection Method for Email Spam Classification , 2015 .
[112] Md. Rafiqul Islam,et al. Classification of malware based on integrated static and dynamic features , 2013, J. Netw. Comput. Appl..
[113] Md Zahangir Alom,et al. Intrusion detection using deep belief networks , 2015, 2015 National Aerospace and Electronics Conference (NAECON).
[114] Ali A. Ghorbani,et al. Detecting Malicious URLs Using Lexical Analysis , 2016, NSS.
[115] Parvez Ahammad,et al. SoK: Applying Machine Learning in Security - A Survey , 2016, ArXiv.
[116] Ying Zhang,et al. Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network , 2019, IEEE Access.
[117] Kien A. Hua,et al. Decision tree classifier for network intrusion detection with GA-based feature selection , 2005, ACM Southeast Regional Conference.
[118] Jong Hyuk Park,et al. DTB-IDS: an intrusion detection system based on decision tree using behavior analysis for preventing APT attacks , 2015, The Journal of Supercomputing.
[119] Sabyasachi Patra,et al. Machine Learning Approach for Intrusion Detection on Cloud Virtual Machines , 2013 .
[120] Kathleen Goeschel,et al. Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis , 2016, SoutheastCon 2016.
[121] D. Karthika Renuka,et al. Improving E-Mail Spam Classification using Ant Colony Optimization Algorithm , 2015 .
[122] Alberto Perez Veiga. Applications of Artificial Intelligence to Network Security , 2018, ArXiv.
[123] Jianfeng Ma,et al. A Novel Dynamic Android Malware Detection System With Ensemble Learning , 2018, IEEE Access.
[124] Dushyant Kumar Singh,et al. Review of Machine Learning Methods for Windows Malware Detection , 2019, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
[125] Gonzalo Álvarez,et al. An Anomaly-based Web Application Firewall , 2009, SECRYPT.
[126] M. Siddiqui,et al. Detecting Internet Worms Using Data Mining Techniques , 2008 .
[127] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[128] Andrew B. Whinston,et al. How Would Information Disclosure Influence Organizations' Outbound Spam Volume? Evidence from a Field Experiment , 2016, J. Cybersecur..
[129] Manisha Sharma,et al. Spam Detection on Social Media Using Semantic Convolutional Neural Network , 2018, Int. J. Knowl. Discov. Bioinform..
[130] Lalu Banoth,et al. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .
[131] Hui-Juan Zhu,et al. HEMD: a highly efficient random forest-based malware detection framework for Android , 2017, Neural Computing and Applications.
[132] Chunhua Wang,et al. Machine Learning and Deep Learning Methods for Cybersecurity , 2018, IEEE Access.
[133] Nithin Kashyap,et al. Providing Cyber Security using Artificial Intelligence – A survey , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).
[134] B. Geluvaraj,et al. The Future of Cybersecurity: Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Cyberspace , 2018, International Conference on Computer Networks and Communication Technologies.
[135] P. Priyatharsini,et al. CLASSIFICATION TECHNIQUES USING SPAM FILTERING EMAIL , 2018 .
[136] Megha Rathi,et al. Spam Mail Detection through Data Mining – A Comparative Performance Analysis , 2013 .
[137] Govind P. Gupta,et al. A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark , 2016 .
[138] Fabio Roli,et al. A survey and experimental evaluation of image spam filtering techniques , 2011, Pattern Recognit. Lett..
[139] Daniel S. Berman,et al. A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..
[140] M. Chuah,et al. Spam Detection on Twitter Using Traditional Classifiers , 2011, ATC.
[141] Jean-Luc Gauvain,et al. Optimization of RNN-Based Speech Activity Detection , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[142] S. Thamarai Selvi,et al. DDoS detection and analysis in SDN-based environment using support vector machine classifier , 2014, 2014 Sixth International Conference on Advanced Computing (ICoAC).
[143] Mansour Sheikhan,et al. Intrusion detection using reduced-size RNN based on feature grouping , 2010, Neural Computing and Applications.
[144] Weiqing Sun,et al. Efficient spam detection across Online Social Networks , 2016, 2016 IEEE International Conference on Big Data Analysis (ICBDA).
[145] Amit Kumar Dewangan,et al. An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set , 2014 .
[146] Jinoh Kim,et al. A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.
[147] Yanfang Ye,et al. DL 4 MD : A Deep Learning Framework for Intelligent Malware Detection , 2016 .
[148] Dong Seong Kim,et al. Spam Detection Using Feature Selection and Parameters Optimization , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.
[149] Yuancheng Li,et al. A Hybrid Malicious Code Detection Method based on Deep Learning , 2015 .
[150] Vacius Jusas,et al. Comparison of Feature Extraction Methods for EEG BCI Classification , 2015, ICIST.
[151] R.K. Cunningham,et al. Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.
[152] Ping Yan,et al. A survey on dynamic mobile malware detection , 2017, Software Quality Journal.
[153] Divya Bansal,et al. Malware Analysis and Classification: A Survey , 2014 .
[154] Mariette Awad,et al. Ham or spam? A comparative study for some content-based classification algorithms for email filtering , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.
[155] Igor Santos,et al. Opcode sequences as representation of executables for data-mining-based unknown malware detection , 2013, Inf. Sci..
[156] Peter Szor,et al. The Art of Computer Virus Research and Defense , 2005 .