TY - GEN
T1 - Efficient discovery of malicious symptoms in clouds via monitoring virtual machines
AU - Alshamrani, Sultan S.
AU - Kowalski, Dariusz R.
AU - Gasieniec, Leszek A.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - In this new era of life, where technology covers our life from the early morning to late night, cybercrime is becoming more developed and challenging for the systems designers. The reason is reflected in the increased number of ways used by criminals. Cloud computing systems are natural goals due to their complexity and increasing popularity. The Cloud system provides an environment with a big number of Virtual Machines (VMs) that available to many users accessing this system via the Internet. This way of access makes cloud systems weaker than physical networks. In order to reduce the number of attacks and secure data storage, any malicious behaviour should be discovered and halted if possible. In this paper, we focus on discovery of malicious behaviour via determining unwanted symptoms rather than via targeting particular malicious behaviour of the system directly. The main motivation for our approach is that malicious behaviour (e.g., a new form of threat) is very often hard to specify directly, but it can be characterized by a set of undesired symptoms. The main contribution of this paper refers to several new mechanisms for monitoring Virtual Machines and further experimental work targeting efficient ways of visiting VMs in order to discover malicious symptoms.
AB - In this new era of life, where technology covers our life from the early morning to late night, cybercrime is becoming more developed and challenging for the systems designers. The reason is reflected in the increased number of ways used by criminals. Cloud computing systems are natural goals due to their complexity and increasing popularity. The Cloud system provides an environment with a big number of Virtual Machines (VMs) that available to many users accessing this system via the Internet. This way of access makes cloud systems weaker than physical networks. In order to reduce the number of attacks and secure data storage, any malicious behaviour should be discovered and halted if possible. In this paper, we focus on discovery of malicious behaviour via determining unwanted symptoms rather than via targeting particular malicious behaviour of the system directly. The main motivation for our approach is that malicious behaviour (e.g., a new form of threat) is very often hard to specify directly, but it can be characterized by a set of undesired symptoms. The main contribution of this paper refers to several new mechanisms for monitoring Virtual Machines and further experimental work targeting efficient ways of visiting VMs in order to discover malicious symptoms.
KW - Cloud computing, Reduce-Max
KW - Cloud monitoring
KW - Malicious behaviour
KW - Network patrolling
KW - Virtual Machines
UR - http://www.scopus.com/inward/record.url?scp=84964296351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964296351&partnerID=8YFLogxK
U2 - 10.1109/CIT/IUCC/DASC/PICOM.2015.257
DO - 10.1109/CIT/IUCC/DASC/PICOM.2015.257
M3 - Conference contribution
AN - SCOPUS:84964296351
T3 - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
SP - 1703
EP - 1710
BT - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
A2 - Atzori, Luigi
A2 - Jin, Xiaolong
A2 - Jarvis, Stephen
A2 - Liu, Lei
A2 - Calvo, Ramon Aguero
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Wu, Yulei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
Y2 - 26 October 2015 through 28 October 2015
ER -