Description:[0001] This invention generally relates to the field of cloud computing and virtual machine monitoring, and in particular, relates to a method for detecting suspicious nodes in cloud-based virtual machines using trust mechanisms and advanced anomaly detection techniques to enhance security and operational efficiency.
10 BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been
15 previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The rapid adoption of cloud computing has transformed how organizations deploy and manage their IT resources. Virtual machines (VMs) are a fundamental
20 component of cloud infrastructure, enabling the efficient allocation of computing resources and the isolation of applications. However, the dynamic nature of cloud environments introduces significant challenges in monitoring and maintaining the performance and security of these VMs.
[0004] As organizations increasingly rely on VMs for critical operations, the risk of
25 security breaches and performance degradation has also escalated. Suspicious nodes within a cloud environment can lead to unauthorized access, data breaches, and service disruptions. Traditional monitoring tools often fall short in detecting such anomalies due to their reliance on static thresholds and limited adaptability to the evolving behavior of workloads.

5 [0005] Current monitoring solutions typically employ coarse-grained metrics and may generate a high volume of false positives, leading to alert fatigue among system administrators. This not only hampers effective incident response but also increases operational costs. Consequently, there is a pressing need for enhanced detection techniques that leverage advanced algorithms and methodologies to improve the
10 accuracy and reliability of VM monitoring.
[0006] The integration of machine learning and fuzzy logic into monitoring systems presents a promising approach to addressing these challenges. Machine learning algorithms can adaptively learn from historical data patterns, while fuzzy logic provides a framework for handling uncertainty and imprecision in performance
15 metrics. Together, these technologies can facilitate more effective anomaly detection, enabling organizations to proactively manage their cloud environments and mitigate potential risks associated with suspicious nodes.
[0007] Therefore, this invention aims to provide a comprehensive method for detecting suspicious nodes in cloud-based virtual machines, utilizing trust
20 mechanisms and advanced anomaly detection techniques to enhance security, improve operational efficiency, and reduce the environmental impact of cloud computing operations.

5 OBJECTIVES OF THE INVENTION
[0009] The objective of present invention is to provide a method for detecting suspicious nodes in a cloud-based virtual machine (VM) environment.
[0010] Further, the objective of present invention is to develop a method that utilizes advanced statistical techniques and machine learning algorithms to
10 accurately identify suspicious nodes in cloud-based virtual machines.
[0011] Moreover, the objective of the present invention is to implement a dynamic thresholding mechanism based on interquartile range (IQR) analysis, allowing for real-time adaptation to changing performance metrics and workload patterns, ensuring timely identification of potential security threats.
15 [0012] Moreover, the objective of the present invention is to incorporate fuzzy logic into the monitoring framework, enabling a nuanced classification of performance metrics that accounts for uncertainty and variability, thus improving the granularity of abnormal behavior detection.
[0013] Moreover, the objective of the present invention is to create an intuitive
20 user interface that facilitates real-time monitoring and alert generation for system administrators, providing actionable insights and enhancing the overall security posture of cloud environments.

SUMMARY

5 [0015] According to an aspect, the present embodiments discloses a method for detecting suspicious nodes in a cloud-based virtual machine (VM) environment. Further, the method comprising collecting performance metrics from a plurality of virtual machines, including CPU usage, memory utilization, and network traffic. Further, the method comprising the steps of applying a sliding window analysis to
10 the collected performance metrics to identify trends and anomalies. Further, the method comprising the steps of utilizing an interquartile range (IQR) statistical method to establish dynamic thresholds for identifying outliers indicative of suspicious behaviour. Further, the method comprising the steps of integrating fuzzy logic to classify performance metrics into normal and abnormal behaviour based on
15 the established thresholds. Further, the method comprising the steps of generating alerts for potential suspicious nodes and providing a user interface for real-time monitoring of VM performance and alerts.
[0016] In some embodiments, the trust mechanisms further include evaluating the reliability of the virtual machines based on historical performance data and user-
20 defined trust policies.

5 BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g. boxes, groups of boxes, or other shapes) in the figures represent one example
10 of the boundaries. It may be that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Moreover, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions
15 are described with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles.
[0018] FIG. 1 illustrates a flow chart of a method for detecting suspicious nodes in a cloud-based virtual machine (VM) environment, according to an embodiment of
20 the present invention.

5 DETAILED DESCRIPTION

[0020] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant
10 to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0021] Although any systems and methods similar or equivalent to those described
15 herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described. Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments
20 of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.
[0022] The present invention discloses method for detecting suspicious nodes in
25 cloud-based virtual machines using trust mechanisms and advanced anomaly detection techniques to enhance security and operational efficiency.
[0023] FIG. 1 illustrates a flow chart of a method (100) for detecting suspicious nodes in a cloud-based virtual machine (VM) environment, according to an embodiment of the present invention.
30 [0024] At step 102, the method (100) comprising steps of collecting performance metrics from a plurality of virtual machines, including CPU usage, memory utilization, and network traffic. This includes critical parameters such as CPU usage, memory utilization, and network traffic. Collecting these metrics is essential for establishing a comprehensive understanding of each VM's operational behavior. By
35 continuously monitoring these parameters, the system can create a rich dataset that

5 reflects the normal functioning of the VMs over time. This foundational data is crucial for subsequent analysis, as it provides the baseline from which trends and anomalies can be identified.
[0025] At step 104, the method (100) comprising steps of applying a sliding window analysis to the collected performance metrics to identify trends and anomalies. This
10 technique involves analyzing a subset of data points over a specified time frame, allowing for the observation of changes in performance metrics as they evolve. By continuously shifting the analysis window forward, the method can detect patterns or deviations in real time. This dynamic approach helps in recognizing both gradual trends and sudden spikes in activity, which may indicate potential issues or
15 irregularities within the VMs, thereby facilitating early detection of suspicious behavior.
[0026] At step 106, the method (100) comprising step of utilizing an interquartile range (IQR) statistical method to establish dynamic thresholds for identifying outliers indicative of suspicious behaviour. The IQR is a measure of statistical
20 dispersion and is calculated by determining the range between the first quartile (Q1) and the third quartile (Q3) of the performance metrics. By setting thresholds based on the IQR, the method can adapt to the variability of the collected data, allowing for the identification of outliers that fall outside the established range. This approach enhances the system's ability to detect unusual patterns or behaviors that deviate
25 from the norm, providing a robust mechanism for flagging potential security threats. [0027] At step 108, the method (100) comprising step of integrating fuzzy logic to classify performance metrics into normal and abnormal behaviour based on the established thresholds. Fuzzy logic allows for a more nuanced interpretation of performance data by accommodating uncertainty and variability inherent in real-
30 world scenarios. Instead of relying solely on binary classifications, fuzzy logic enables the system to categorize behaviors on a spectrum, taking into account degrees of abnormality. This classification enhances the system's sensitivity to subtle changes in performance, allowing for a more accurate detection of suspicious nodes that may not be easily identified through traditional binary methods.

5 [0028] At step 110, the method (100) comprising step of generating alerts for potential suspicious nodes and providing a user interface for real-time monitoring of VM performance and alerts. When the system detects anomalies or behaviors classified as abnormal, it triggers alerts to notify system administrators of potential security threats. The user interface is designed to be intuitive, allowing
10 administrators to easily visualize performance metrics, trends, and alerts in real time. This functionality not only aids in prompt incident response but also empowers administrators to make informed decisions regarding the management and security of their cloud-based virtual machines, ultimately enhancing the overall security posture of the cloud environment.
15 [0029] In some embodiments, the trust mechanisms in this invention are designed to evaluate the reliability of virtual machines (VMs) by analyzing historical performance data alongside user-defined trust policies. By leveraging a comprehensive dataset that tracks various performance metrics such as CPU usage, memory consumption, and network activity. This approach establishes a baseline of
20 normal behavior for each VM.
[0030] User-defined trust policies, which can be tailored to the specific operational requirements and security standards of an organization, further refine this evaluation process by setting criteria for acceptable performance thresholds and behaviors. This dual approach not only enhances the accuracy of reliability assessments but
25 also allows for adaptive monitoring that evolves with changing workloads and user expectations. As a result, VMs that deviate from established norms or fail to meet trust policy criteria can be flagged for further investigation, thereby improving overall security and operational integrity within cloud environments.
[0031] It should be noted that the method for detecting suspicious nodes in a cloud-
30 based virtual machine (VM) environment in any case could undergo numerous modifications and variants, all of which are covered by the same innovative concept; moreover, all of the details can be replaced by technically equivalent elements. In practice, the components used, as well as the numbers, shapes, and sizes of the components can be of any kind according to the technical requirements. The scope
35 of protection of the invention is therefore defined by the attached claims.
, Claims:WE CLAIM:

1. A method (100) for detecting suspicious nodes in a cloud-based virtual machine (VM) environment, the method comprising the steps of:
collecting performance metrics from a plurality of virtual
machines, including CPU usage, memory utilization, and network traffic; applying a sliding window analysis to the collected performance
metrics to identify trends and anomalies;
utilizing an interquartile range (IQR) statistical method to establish dynamic thresholds for identifying outliers indicative of suspicious
behaviour;
integrating fuzzy logic to classify performance metrics into normal and abnormal behaviour based on the established thresholds; and
generating alerts for potential suspicious nodes and providing a user interface for real-time monitoring of VM performance and alerts.

2. The method (100) as claimed in claim 1, wherein the trust mechanisms further include evaluating the reliability of the virtual machines based on historical performance data and user-defined trust policies.