A Big Data Analytics Based Approach to Anomaly Detection

We present a novel Cyber Security analytics framework. Wedemonstrate a comprehensive cyber security monitoring system toconstruct cyber security correlated events with feature selection toanticipate behaviour based on various sensors.

[1]  Chase Qishi Wu,et al.  Monitoring security events using integrated correlation-based techniques , 2009, CSIIRW '09.

[2]  I. Sumaiya Thaseen,et al.  Intrusion detection model using fusion of PCA and optimized SVM , 2014, 2014 International Conference on Contemporary Computing and Informatics (IC3I).

[3]  Alina Madalina Lonea,et al.  Detecting DDoS Attacks in Cloud Computing Environment , 2012, Int. J. Comput. Commun. Control.

[4]  Praveen Bhanodia,et al.  Literature survey - IDS for DDoS attacks , 2014, 2014 Conference on IT in Business, Industry and Government (CSIBIG).

[5]  Christopher Krügel,et al.  Comprehensive approach to intrusion detection alert correlation , 2004, IEEE Transactions on Dependable and Secure Computing.

[6]  Jiankun Hu,et al.  A statistical framework for intrusion detection system , 2014, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[7]  Anindya Maiti,et al.  Cloud controlled intrusion detection and burglary prevention stratagems in home automation systems , 2012, 2012 2nd Baltic Congress on Future Internet Communications.

[8]  Colin Puri,et al.  Analyzing and Predicting Security Event Anomalies: Lessons Learned from a Large Enterprise Big Data Streaming Analytics Deployment , 2015, 2015 26th International Workshop on Database and Expert Systems Applications (DEXA).

[9]  Hamid H. Jebur,et al.  Machine Learning Techniques for Anomaly Detection: An Overview , 2013 .

[10]  Dongho Won,et al.  A Practical Study on Advanced Persistent Threats , 2012 .

[11]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[12]  Gong Shang-fu,et al.  Intrusion detection system based on classification , 2012, 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment.

[13]  Christin Schäfer,et al.  Learning Intrusion Detection: Supervised or Unsupervised? , 2005, ICIAP.

[14]  Huaglory Tianfield,et al.  Evaluation of Experiments on Detecting Distributed Denial of Service (DDoS) Attacks in Eucalyptus Private Cloud , 2012, SOFA.

[15]  Yasir Mehmood,et al.  Intrusion Detection System in Cloud Computing: Challenges and opportunities , 2013, 2013 2nd National Conference on Information Assurance (NCIA).

[16]  Zhang Xue-qin,et al.  Intrusion Detection System Based on Feature Selection and Support Vector Machine , 2006, 2006 First International Conference on Communications and Networking in China.

[17]  Medromi Hicham,et al.  A collaborative intrusion detection and Prevention System in Cloud Computing , 2013, 2013 Africon.

[18]  Ahmed Patel,et al.  An intrusion detection and prevention system in cloud computing: A systematic review , 2013, J. Netw. Comput. Appl..

[19]  Naixue Xiong,et al.  Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications , 2014, Inf. Sci..

[20]  Lingfeng Wang,et al.  A neural network based distributed intrusion detection system on cloud platform , 2012, 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems.

[21]  G. Cybenko,et al.  Temporal and spatial distributed event correlation for network security , 2004, Proceedings of the 2004 American Control Conference.

[22]  Yong Hu,et al.  Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..

[23]  Haider Abbas,et al.  Feasibility analysis for incorporating/deploying SIEM for forensics evidence collection in cloud environment , 2015, 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS).

[24]  Vijay Varadharajan,et al.  Intrusion Detection Techniques for Infrastructure as a Service Cloud , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.