ADAPTIVE MACHINE LEARNING SYSTEM FOR REAL-TIME CYBER THREAT DETECTION AND AUTOMATED MITIGATION

Abstract

This invention presents a machine learning-driven cybersecurity system designed to detect and mitigate cyber threats in real time across distributed network environments, including enterprise, cloud, and IoT networks. The system integrates supervised learning for identifying known threats, unsupervised learning for anomaly detection, and reinforcement learning to adapt to emerging attack patterns. The invention includes an automated threat mitigation module that can initiate actions such as isolating infected devices, blocking malicious traffic, and generating alerts without human intervention. A continuous feedback loop allows the system to improve its accuracy over time by reducing false positives and adapting to new threat vectors. This robust, scalable system enhances network security by rapidly identifying and responding to cybersecurity threats, offering reliable defense against both known and unknown attacks.

Specification

Description:[0015].The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
[0016].The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
[0017].It is understood that when an element or layer is referred to as being "on," "connected to," or "coupled to" another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
[0018].The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies.
[0019].The invention describes a machine learning (ML)-based anomaly detection system designed to detect and mitigate cybersecurity threats in real-time across distributed networks. The system utilizes a combination of supervised and unsupervised learning techniques to identify potential threats such as malware, ransomware, phishing attempts, and zero-day attacks. By continuously learning from network traffic data and updating its model in real-time, the system adapts to evolving attack patterns and reduces false positives. The invention also covers an automatic threat mitigation module that initiates appropriate countermeasures such as isolating infected systems or blocking malicious traffic without human intervention. The system is applicable in enterprise-level environments, cloud infrastructure, and Internet of Things (IoT) networks.
[0020].The rise of sophisticated cyber threats has intensified the need for advanced security systems capable of detecting and responding to attacks in real time. Traditional cybersecurity approaches, such as signature-based detection, often struggle to keep pace with rapidly evolving threats like zero-day exploits, advanced persistent threats, and polymorphic malware. These traditional methods lack adaptability and rely on pre-defined patterns, making them ineffective against novel or modified attacks.
[0021].Machine learning (ML) has emerged as a powerful tool in cybersecurity, providing dynamic, data-driven approaches to threat detection by analyzing network traffic, user behavior, and other relevant patterns. However, many existing ML-based systems are limited by high false-positive rates and insufficient adaptability to new types of attacks. Consequently, there is a growing need for an integrated, adaptive system that not only detects threats but can also initiate automated responses to mitigate the risks in real time.
[0022].The invention described addresses these challenges by implementing a machine learning-based cybersecurity system that continuously learns and adapts from real-time data. This system combines supervised, unsupervised, and reinforcement learning models, enhancing its ability to detect both known and unknown threats. In addition, an automated threat mitigation module allows the system to take immediate countermeasures, reducing response times and minimizing reliance on human intervention. The invention provides robust security for a wide range of network environments, including enterprise systems, cloud infrastructures, and IoT ecosystems, offering scalable and adaptive protection against diverse and evolving cyber threats.
[0023].The present invention provides an adaptive machine learning system designed for real-time cybersecurity threat detection and automated mitigation across distributed networks, with particular application in environments such as enterprise networks, cloud infrastructures, and IoT ecosystems. The system comprises multiple integrated machine learning modules-supervised, unsupervised, and reinforcement learning-each serving a distinct purpose in enhancing the accuracy, adaptability, and response capabilities of the overall security framework. This multi-layered approach ensures comprehensive protection against both known and unknown cyber threats.
[0024].The system's data collection module continuously monitors network activity, collecting relevant data points such as IP addresses, packet metadata, device activity logs, and user access patterns. This data is aggregated and pre-processed in a dedicated ingestion layer to eliminate noise and prioritize relevant information. The processed data then undergoes normalization, allowing the system to standardize various data formats and ensure compatibility across sources. By transforming this raw data into a cohesive and structured format, the system enhances the efficiency and accuracy of subsequent threat detection and analysis steps.
[0025].Once data is processed, the machine learning models are employed to detect potential threats. The supervised learning model, trained on labeled datasets that include historical attack patterns, detects known threats, such as malware, phishing, and ransomware attacks. The unsupervised learning model, on the other hand, analyzes network traffic to identify anomalies or deviations from established normal behaviors, which may indicate novel or emerging threats. For example, unusual login patterns, abnormal traffic spikes, or communication with previously unknown IP addresses could all be flagged as suspicious activity. Reinforcement learning further enhances the system's adaptability by allowing it to learn from newly detected threats and adjust detection parameters, thereby evolving continuously to stay ahead of emerging attack strategies.
[0026].The invention also incorporates an automated threat mitigation module that initiates real-time responses based on the type and severity of detected threats. When a threat is identified, the mitigation module can autonomously isolate infected devices from the network, block suspicious IP addresses, or update firewall rules, preventing potential damage from escalating. Additionally, in high-risk scenarios, the system may deploy honeypots to divert attackers and gather intelligence on their methods, enhancing the system's future threat detection capabilities. These automated actions minimize response times and reduce dependency on human intervention, which is crucial for large, complex networks where manual oversight can introduce delays.
[0027].A key feature of the system is its continuous learning and feedback loop, which refines the machine learning models over time. As new data is ingested and analyzed, the system incorporates feedback on the outcomes of previous detections and responses, allowing it to recalibrate its algorithms to reduce false positives and improve overall accuracy. This adaptive feedback mechanism ensures that the system remains responsive to emerging threats, adjusts to evolving patterns in network behavior, and strengthens its predictive capabilities over time.
[0028].The system's modular design provides scalability and flexibility, making it suitable for varied network sizes and configurations. For instance, the system can be scaled to monitor complex enterprise networks or streamlined to protect smaller IoT networks. It also supports the integration of additional data sources as new attack vectors are identified, ensuring that the system remains robust and adaptable as cybersecurity challenges evolve.
[0029].In summary, the invention leverages a sophisticated combination of machine learning techniques to offer real-time cybersecurity threat detection and automated mitigation. Through its layered approach to data collection, multi-modal threat detection, and automated response, it provides a resilient and adaptive defense against the dynamic landscape of cyber threats. The system's continuous feedback loop, modular architecture, and automated threat response make it a powerful and versatile solution for securing diverse network environments against both conventional and advanced cyber attacks.
[0030].This patentable machine learning-based cybersecurity system offers a powerful, real-time solution for detecting and mitigating cyber threats in distributed networks. By leveraging advanced machine learning and data science techniques, it addresses evolving cyber threats while providing an automated and adaptive defense mechanism suitable for various industries.
[0031].The rise of sophisticated cyber threats has intensified the need for advanced security systems capable of detecting and responding to attacks in real time. Traditional cybersecurity approaches, such as signature-based detection, often struggle to keep pace with rapidly evolving threats like zero-day exploits, advanced persistent threats, and polymorphic malware. These traditional methods lack adaptability and rely on pre-defined patterns, making them ineffective against novel or modified attacks.
[0032].Machine learning (ML) has emerged as a powerful tool in cybersecurity, providing dynamic, data-driven approaches to threat detection by analyzing network traffic, user behavior, and other relevant patterns. However, many existing ML-based systems are limited by high false-positive rates and insufficient adaptability to new types of attacks. Consequently, there is a growing need for an integrated, adaptive system that not only detects threats but can also initiate automated responses to mitigate the risks in real time.
[0033].The invention described addresses these challenges by implementing a machine learning-based cybersecurity system that continuously learns and adapts from real-time data. This system combines supervised, unsupervised, and reinforcement learning models, enhancing its ability to detect both known and unknown threats. In addition, an automated threat mitigation module allows the system to take immediate countermeasures, reducing response times and minimizing reliance on human intervention. The invention provides robust security for a wide range of network environments, including enterprise systems, cloud infrastructures, and IoT ecosystems, offering scalable and adaptive protection against diverse and evolving cyber threats.
[0034].Traditional cybersecurity systems primarily rely on rule-based and signature-based detection methods, which are effective against known threats but struggle to identify novel or evolving attack vectors. Signature-based methods require prior knowledge of threat characteristics to be effective, meaning they are inherently limited in detecting zero-day attacks or customized malicious software designed to evade known signatures. Furthermore, these conventional systems often produce high rates of false positives, leading to alert fatigue among cybersecurity professionals and increasing the likelihood of real threats going undetected.
[0035].The emergence of machine learning (ML) in cybersecurity offers a new paradigm by enabling data-driven anomaly detection and pattern recognition. ML-based systems can analyze vast amounts of data to identify deviations from normal behavior, even when there are no predefined attack signatures. However, existing ML-based solutions in cybersecurity face critical limitations, including a high rate of false positives, challenges in model adaptability, and reliance on human operators for mitigation actions. The lack of an integrated, continuously adaptive system diminishes the effectiveness of these solutions in high-stakes environments that demand real-time responses to emerging threats.
[0036].To address these challenges, this invention introduces an advanced, adaptive machine learning system specifically tailored for real-time threat detection and automated mitigation. The system integrates multiple ML techniques-supervised learning, unsupervised learning, and reinforcement learning-to create a comprehensive, multi-layered defense strategy. The supervised learning component enables the system to identify known threats based on labeled historical data, while the unsupervised learning module detects anomalies that could signal previously unknown or novel attacks. Additionally, reinforcement learning allows the system to self-improve by learning from past incidents, adjusting detection parameters dynamically in response to new information, and reducing false positives over time.
[0037].A critical feature of this invention is its automated threat mitigation module, which enables real-time responses to detected threats. Upon identifying a threat, the system can autonomously initiate predefined countermeasures, such as isolating compromised devices, blocking suspicious network traffic, or deploying honeypots to divert and monitor attacker behavior. This capability significantly reduces response times and minimizes the need for human intervention, making the system highly effective in environments where immediate action is required.
[0038].The invention's continuous feedback loop ensures that detection models are regularly updated based on new data, enhancing the system's accuracy and resilience against evolving attack vectors. This adaptive learning mechanism minimizes the risk of false positives and allows the system to adjust rapidly to changing threat patterns, maintaining robust protection even as cyber threats grow more advanced. Furthermore, the modular and scalable architecture of the system supports deployment across a variety of network environments, including enterprise networks, cloud infrastructure, and IoT ecosystems, allowing for flexible integration and expansive threat visibility.
[0039].In summary, this invention addresses the limitations of traditional cybersecurity approaches and existing ML-based systems by providing a comprehensive, adaptive solution. Through its combination of advanced machine learning techniques, automated threat response, and continuous feedback, the system delivers a proactive and resilient defense against modern cybersecurity threats, offering critical infrastructure and organizations a powerful tool to safeguard their networks against both known and unknown cyber attacks. , Claims:1.A machine learning-based cybersecurity threat detection system comprising:
a) A supervised learning module to detect known cybersecurity threats using labeled data,
b) An unsupervised learning module for anomaly detection by identifying deviations in network traffic,
c) A reinforcement learning module to continuously update threat detection capabilities based on feedback from past detections.
2.The system as claimed in claim 1, wherein the unsupervised learning module flags network anomalies indicating novel or unknown threats, such as zero-day attacks.
3.The system as claimed in claim 1, wherein the reinforcement learning module adapts detection parameters dynamically in response to newly detected threats, enhancing the system's responsiveness to evolving attack vectors.
4.An automatic threat mitigation system integrated with the machine learning modules, configured to:
a) Isolate compromised network components,
b) Block malicious IP traffic,
c) Trigger alerts for further investigation by cybersecurity personnel.
5.The system as claimed in claim 4, wherein the threat mitigation module automatically applies predefined countermeasures based on the threat's classification, including actions such as isolating infected devices, updating firewall rules, and deploying honeypots.

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