MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202611099207 A) filed by Mr. Shiva Shankar Mummidi on August 17, 2026, for Machine Learning-Based Real-Time Data Exfiltration Detection And Prevention System For Distributed Enterprise Networks.
Inventor includes Mr. Shiva Shankar Mummidi.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The present invention is directed to systems and methods of securing data, and more specifically to a framework which utilizes machine learning based mechanisms to detect and prevent exfiltration of sensitive information from an enterprise's interconnected computing infrastructure. In this regard, many modern enterprises are faced with the challenge of protecting their data as it is transferred between various servers, workstations, cloud platforms, remote endpoints, and external networks. Existing data security solutions are often inadequate in detecting and responding to sophisticated exfiltration attempts that utilize obfuscation, polymorphic code, and other techniques to mask their presence during transit. As such, enterprises continue to suffer losses due to valuable information being exfiltrated in real-time without timely response and mitigation. The present invention provides systems and methods that detect and prevent exfiltration of data in real-time. A machine learning engine is trained on data sets reflecting known normal and exfiltration traffic patterns, enabling the engine to recognize patterns indicative of exfiltration attempts. The engine performs supervised classification, unsupervised detection, and reinforcement learning to adapt its response to different traffic patterns. The system extracts features from network packets and analyzes them using a combination of packet header inspection, packet size distribution analysis, destination analysis, timing analysis, and pattern analysis to differentiate between legitimate bulk data transfers and exfiltration traffic. When an exfiltration attempt is detected, the system responds by taking one or more preventive actions. Such actions may include throttling traffic, blocking connections, rewriting contents of data packets, encrypting data, isolating a computing device that is leaking information, and injecting misleading information to confuse exfiltration tools. The invention is further directed to systems and methods that detect and prevent exfiltration of data in real-time, wherein the system is configured to operate in complex distributed enterprise networks, wherein detection and prevention actions may occur across a computing infrastructure of disparate devices. Lightweight agents may be used on each computer device to analyze patterns in network packets and report features to one or more correlation servers that operate the machine learning engine. Such an approach enables the invention to be used even when some computing devices experience poor network connectivity. The machine learning engine can be further used in distributed manners across different networks to provide collaborative exfiltration detection and prevention, wherein model training may occur in a federated manner to ensure that sensitive data across different enterprises is not exposed to any single entity. The present invention further includes feedback loops that allow the machine learning engine to continue to learn and improve its detection performance. Feedback may be provided in the form of prevention actions taken by the system and subsequent analysis of whether such prevention actions were successful or not. In this manner, the machine learning engine can update itself to reduce false positives and false negatives in its detection results over time. The system also includes contextual enrichment modules that provide additional information about network traffic to the machine learning engine. In such a manner, the machine learning engine can take into account data about the type and sensitivity of the data that is being transferred and the purpose of such a transfer to determine whether such a transfer is legitimate or not. The system includes temporal models that analyze traffic patterns over time to infer normal behavior for enterprise users and computer devices, which can be used to detect anomalous behavior. Additionally, the system includes spatial models that analyze patterns of computer network traffic to detect distributed exfiltration attempts that utilize multiple computers or other devices to transfer data in stages. The present invention also includes systems and methods of securing data which provide improved exfiltration traffic detection and prevention performance over existing data protection systems by utilizing machine learning approaches. In this regard, the systems and methods of the invention can detect a significantly greater percentage of stealthy data exfiltration attempts in real-time than conventional approaches which often require long duration analysis of network traffic to build up enough statistical information to infer that an exfiltration attempt is occurring. Further, the machine learning based prevention component allows the system to prevent exfiltration attempts by taking preventive actions before all of the data is transferred out of the enterprise network. The invention is further capable of handling large scale enterprise networks, wherein the machine learning engine can be distributed among different servers to handle increased computing demands. The invention is further able to handle attempts to circumvent the exfiltration detection and prevention system using adversarial machine learning methods. The invention further provides a general approach to utilizing machine learning to detect and prevent exfiltration of data in real-time that can be applied in different manners and in different systems. In this regard, the invention provides technical improvements that can be utilized in conjunction with other data security solutions. For example, the different approaches described in the invention can be used in combination with network monitoring systems to determine when a particular computing device is exhibiting exfiltration patterns in its network traffic. The detailed description below provides additional details of different embodiments of the invention that can be used in different computing environments. It should be noted that additional embodiments that fall within the scope of the present invention are contemplated. The various components of the invention as described above have certain advantages over equivalent conventional components. For example, the system allows machine learning models to be trained on network traffic features to recognize exfiltration patterns. Such models are then used to detect exfiltration in real-time and respond by taking preventive actions. The system is able to detect exfiltration patterns in large scale enterprise environments using a combination of machine learning and network analysis techniques. The system can be implemented in different hardware and software configurations. In a hardware configuration, specialized processors such as neural network processors or secure enclaves can be used to run the machine learning features. Further, packet processing accelerators can be used to allow the system to inspect packets traveling on computer networks at high speeds. In a software configuration, different elements of the system can be implemented in various programming languages and run on containers or virtual machines executing on computer servers. Additionally, the system can provide application programming interfaces (APIs) that can allow system administrators to customize the operating parameters of the system, for example, to set policies for responding to detected exfiltration attempts. The system components described in this invention are further used in combination with each other to implement the various methods of the invention.
Disclaimer: Curated by HT Syndication.
