Cloud computing provides large scale computing
resource to each customers. Cloud systems can be threatened by
numerous attacks as cloud provides services to no trustworthy
system. Cloud needs to contain intrusion detection system for
protecting system against threads. If IDS is having stronger
security using more rules or patterns then it need much more
computing resources. Current cloud monitoring systems can rely
on signature-based and supervised-learning-based detection
methods to check out attacks and anomalies. Propose work
introduce UCAD, an Unsupervised Cloud Anomaly Detection
for knowledge-independent detection of anomalous traffic.
UCAD uses a novel clustering technique based on Partition
based clustering to identify clusters and outliers in multiple lowdimensional
spaces. The evidence of traffic structure provided
by these multiple clustering is then combined to produce an
abnormality ranking of traffic flows, using a correlationdistance-
based approach.
Saurabh P.Taley : CSE(ESC-IV Sem) , G.H.Raisoni College of Engineering, Nagpur, Maharastra, India
J.J.Shah : Assistant prof. IT DEPARTMENT, G.H.Raisoni College of Engineering, Nagpur, Maharastra, India
Intrusion Detection System
Anomaly detection
Clustering
IDS methods leads to effective resources usages by
applying differentiated level of security strength to users
based on the degree of anomaly. Through the cloud
computing it is possible to judge all users and
administrators as potential attacker and apply strong
security policy to all traffic, but it is not efficient at all. If
any security hazards occur, economic damages are
unavoidable. proposed work Unsupervised cloud Anomaly
Detection UCAD verified the effectiveness of the system
detect real single source-destination and distributed
attacks in real traffic all in a completely blind fashion,
without assuming any particular traffic model, clustering
parameters, or even clusters structure beyond a basic
definition of what an anomaly. So it’s effective way of
detecting attacks in cloud environment.
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