2. Literature survey
[12] HammiBadis, Guillaume Doyen and RidaKhatoun had presented a
Collaborative Approach for a Source based Detection of Botclouds. Each
tenant is treated separately and for each one a tree is constructed. The
objective of using a tree-based hierarchical architecture is to reduce
the complexity of the time and cycles of execution of the detection
algorithm. Indeed, the proposed approach is based on the idea of (1)
applying the PCA-based detection algorithm, small sets of VMs after
their data has been standardized, and (2) aggregating the result set to
decide on the state of the tenant (attacker or not). However, it doesn’t
prevent against attacks in CSPs by cloud infrastructure.
[13] RémiCogranne, Guillaume Doyen, NisrineGhadban and BadisHammi
had submitted a Decentralized and Robust Detection Method for
Multi-Tenant Virtualized Environments This technique focused on
developing and validating a solution to detect malicious operations by
virtual hosts infected with botnets in the public cloud context.
Although, this method doesn’t prevent from data leakage problem thus
leads to major security issues in cloud.
[14] GauravSomani, Manoj Singh Gaur Suggested a fresh ”Inside-out
Scale” approach that reduces the ”Resource Utilization Factor” to a
minimum value for fast assault absorption. The proposed strategy
sacrifices victim service resources in relation to other co-located
facilities and provides mitigation facilities with those resources to
determine their accessibility during the assault. Eventhough, this
method does not deter data collection assaults in the cloud processing.
[15]WesamBhaya, Mehdi EbadyManaa A combination of unsupervised data
mining techniques was introduced with the introduction of an intrusion
detection system. The entropy idea in terms of windowing incoming
packets is implemented using information mining method using Clustering
Using Representative (CURE) as a cluster analysis to detect the network
flow DDoS attack. However, only the information theory was regarded to
transform nominal data to numerical data using entropy windows is
considered. It doesn’t prevent from the attacks in frequency of packets
during network flow.
[16]AmjadAlsirhani, SrinivasSampalli and Peter Bodorik the DDoS
detection scheme provided those advantages from the resources of cloud
computing. It entails of three notions: algorithms of classification,
similarcalculating, besides a system of fuzzy logic. The notion of
parallelism is used to effectively speed up the implementation of the
classification algorithms used. It assessed the classification algorithm
and the DDoS detection parallel processing by configuring a test-bed
that consists of one master and three slaves. Although, this method only
detect the attacks not prevent the system from the detection.
[17] Salman Iqbal et al. In terms of their cloud models, cloud-based
attacks and vulnerabilities are collected and classified. We also
present taxonomy of cloud security assaults and prospective mitigation
strategies in order to provide a thorough knowledge of safety needs in
the cloud setting. However, it fails to ensure integrity and
confidentiality.
[18] Bo Li et al. New cluster-based intrusion detection system to
track network traffic in the cloud environment to support intra-VM
networks traffic monitor. A traffic deduction framework is also designed
to eliminate redundant network traffic and reduce the burden of IDS
clusters. It does not involve physical switch support and uses intra-VM
network traffic to control virtual network traffic instead of
virtualized IDS. Although in real cloud environments, this system is not
efficiently suited.
[19] Mohamed Idhammad et al. had presented a distributed intrusion
detection system for cloud settings based on machine learning. This
system had been intended to be inserted side by side in the cloud with
the cloud provider’s edge network parts. It is used to intercept
incoming network traffic to the physical layer’s edge network routers.
Even though, it is not well suitable for real world’s IDS deployment.
Thus from the above discussion, it is revealed that, in [12] doesn’t
prevent against attacks in CSPs, [13] doesn’t prevent from data
leakage problem, [14] doesn’t prevent from the attacks in frequency
of packets, [15] does not deter data collection assaults, [16]
only detect the attacks not prevent the system from the detection,
[17] fails to ensure integrity and confidentiality, [18] not
efficiently suitable in real cloud environments, [19] not well
suitable for real world’s IDS deployment. Hence in order to tackle such
issues, there is a need to innovate a new strategy in the field of cloud
environment.