Detection of Worms Over Cloud Environment

In recent years, computer worms are the remarkable difficulties found in the distributed computing. The location of worms turns out to be more unpredictable since they are changing quickly and much more refined. The difficulties in gathering worm’s payload were recognized for identifying and gathering worm’s payloads and the honey pot which is high-intelligent to gather the payload of zero-day polymorphic heterogeneous and homogeneous stages in distributed computing. The Signaturebased discovery of worms strategies work with a low false-positive rate. We propose an irregularity based interruption location instrument for the cloud which specifically benefits from the virtualization advancements all in all. Our proposed abnormality location framework is detached from spreading computer worm contamination and it can recognize new computer worms. Utilizing our methodology, a spreading computer worm can be distinguished on the spreading conduct itself without getting to or straightforwardly affecting running virtual machines of the cloud. Detection of Worms Over Cloud Environment: A Literature Survey

[1]  Jörg Schwenk,et al.  On Technical Security Issues in Cloud Computing , 2009, 2009 IEEE International Conference on Cloud Computing.

[2]  G. Fenu,et al.  An approach to a Cloud Computing network , 2008, 2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT).

[3]  Lorenzo Martignoni,et al.  A Framework for Behavior-Based Malware Analysis in the Cloud , 2009, ICISS.

[4]  Victor A. Skormin,et al.  Behavioral Modeling for Suspicious Process Detection in Cloud Computing Environments , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[5]  Teresa F. Lunt,et al.  A survey of intrusion detection techniques , 1993, Comput. Secur..

[6]  Sugata Sanyal,et al.  Adaptive neuro-fuzzy intrusion detection systems , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[7]  McGrawGary,et al.  Attacking Malicious Code , 2000 .

[8]  Madihah Mohd Saudi,et al.  A SYSTEMATIC ANALYSIS ON WORM DETECTION IN CLOUD BASED SYSTEMS , 2015 .

[9]  Ming-Yang Su,et al.  A real-time network intrusion detection system for large-scale attacks based on an incremental mining approach , 2009, Comput. Secur..

[10]  Yi Ming Chen,et al.  Retrospective Detection of Malware Attacks by Cloud Computing , 2010, CyberC 2010.

[11]  Ashraf A. Shahin Polymorphic Worms Collection in Cloud Computing , 2014, ArXiv.

[12]  Pratit Santiprabhob Fuzzy Intrusion Detection System , 2002 .

[13]  Radu State,et al.  Malware behaviour analysis , 2008, Journal in Computer Virology.

[14]  Gary McGraw,et al.  Attacking Malicious Code: A Report to the Infosec Research Council , 2000, IEEE Software.

[15]  K. G. Srinivasa,et al.  Application of Genetic Algorithms for Detecting Anomaly in Network Intrusion Detection Systems , 2012 .

[16]  Wei Li,et al.  Using Genetic Algorithm for Network Intrusion Detection , 2004 .

[17]  Madihah Mohd Saudi,et al.  Detecting worm attacks in cloud computing environment: Proof of concept , 2014, 2014 IEEE 5th Control and System Graduate Research Colloquium.

[18]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[19]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[20]  Ashraf Darwish,et al.  Intelligent Hybrid Anomaly Network Intrusion Detection System , 2011, FGIT-FGCN.

[21]  James Cannady,et al.  Artificial Neural Networks for Misuse Detection , 1998 .

[22]  Wei Lu,et al.  Detecting New Forms of Network Intrusion Using Genetic Programming , 2004, Comput. Intell..

[23]  Muttukrishnan Rajarajan,et al.  A survey of intrusion detection techniques in Cloud , 2013, J. Netw. Comput. Appl..

[24]  I. Ramesh Babu Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms , 2008 .

[25]  Andrés G. Castillo Sanz,et al.  Malware detection based on Cloud Computing integrating Intrusion Ontology representation , 2010 .

[26]  Carla Merkle Westphall,et al.  Intrusion Detection for Grid and Cloud Computing , 2010, IT Professional.