1. A method for enhancing cloud security comprising the use of adversarial networks to simulate, detect, and mitigate threats in a cloud environment.

2. The method of claim 1, wherein a generator simulates malicious behavior patterns in the form of synthetic network traffic.

3. The method of claim 1, wherein a discriminator classifies incoming data as benign or malicious using a trained deep neural model.

Description:Title:

Enhancing Cloud Security: Adversarial Networks for Threat Detection and Mitigation

Field of the Invention

[0001] The present invention is related to the networks in computer science and cloud computing field.
Background

[0002] The present invention relates to cybersecurity and cloud computing. More specifically, it describes the use of adversarial neural networks for real-time detection and mitigation of threats in cloud environments.
[0003] With increasing reliance on cloud-based infrastructure, ensuring the security of data and services hosted in cloud environments is a critical concern.
[0004] Traditional intrusion detection and prevention systems often fail to detect novel and dynamic attack patterns.
[0005] Adversarial machine learning, particularly Generative Adversarial Networks (GANs), offers promising capabilities to both simulate and detect complex cyber threats.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] Uses adversarial learning to dynamically generate and detect complex cyber threats.
[0012] Provides adaptive threat mitigation within cloud environments. Improves accuracy of threat classification using adversarial feedback mechanisms.

Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype. This invention presents a framework that leverages adversarial networks in a cloud security system. The system includes a generator that simulates malicious behavior, and a discriminator that distinguishes between legitimate and malicious activity. Upon identifying threats, the system automatically initiates mitigation actions like access control enforcement or network isolation, enhancing the robustness of cloud security infrastructure.

Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system.
[0015] Collect raw data from various cloud sources including logs, API calls, VM behavior, and container metrics. Inputs are continuously fed into the system via a Threat Analysis Module.
[0016] The Threat Analysis Module preprocesses and analyzes incoming data to extract behavioral features. These features represent baseline patterns of both legitimate and suspicious activities.

[0017] A Generator Network (part of a GAN) produces synthetic data resembling known and evolving attack patterns. These simulations serve as adversarial examples to challenge the detection system.
[0018] A Discriminator Network processes both real-time input and simulated attacks. It classifies each instance as either benign or malicious using a deep neural architecture trained through adversarial learning.
[0019] Based on the Discriminator’s output, the system makes a decision on whether an event is a threat. It continuously adapts to new attack vectors through retraining.
[0020] If a threat is detected, the Mitigation Module is activated. It enforces countermeasures such as user isolation, access throttling, or alerting administrators.
[0021] Feedback from threat detection and mitigation is used to retrain both Generator and Discriminator networks. This creates a self-improving system that becomes more effective over time against evolving threats.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. A method for enhancing cloud security comprising the use of adversarial networks to simulate, detect, and mitigate threats in a cloud environment.
2. The method of claim 1, wherein a generator simulates malicious behavior patterns in the form of synthetic network traffic.
3. The method of claim 1, wherein a discriminator classifies incoming data as benign or malicious using a trained deep neural model.