{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853999","patent":{"patent_number":"US-9853999","title":"Context-aware knowledge system and methods for deploying deception mechanisms","assignee":null,"inventors":[],"filing_date":"2017-02-03T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04L","H04L"],"num_claims":20,"abstract":"Methods, systems, and computer-readable mediums are described herein to provide context-aware knowledge systems and methods for deploying deception mechanisms. In some examples, a deception profiler can be used to intelligently deploy the deception mechanisms for a network. For example, a method can include identifying a network for which to deploy one or more deception mechanisms. In such an example, a deception mechanism can emulate one or more characteristics of a machine on the network. The method can further include determining one or more asset densities and a summary statistic. An asset density can be associated with a number of assets connected to the network. The summary statistic can be associated with a number of historical attacks on the network. Using at least one or more of the one or more asset densities, the summary statistic, other information associated with the network, or a combination thereof, the method can further include determining a number of deception mechanisms to deploy, and deploying the number of deception mechanisms."},"analysis":{"summary":"The Context-aware Knowledge System and Methods for Deploying Deception Mechanisms patent introduces a novel approach to network security by intelligently deploying deception mechanisms. The core innovation lies in its ability to analyze network context and historical attack data to dynamically adjust the placement and configuration of these mechanisms. The system solves the problem of static and easily detectable security defenses by creating an adaptive and realistic deception environment for attackers.\n\nThe key technical approach involves a deception profiler that assesses asset densities, historical attack patterns, and other relevant information to determine the optimal number of deception mechanisms to deploy. These mechanisms emulate the characteristics of real machines, luring attackers away from critical assets. The system continuously monitors attacker behavior, providing valuable threat intelligence and allowing for adaptive adjustments to the deception strategy.\n\nThe business value of this technology is significant. It reduces the attack surface, minimizes the risk of data breaches, and provides actionable threat intelligence. The system can be applied to various industries, including finance, healthcare, and government, where network security is paramount. The market opportunity is substantial, as organizations increasingly seek proactive and adaptive security solutions to combat sophisticated cyber threats.\n\nBy providing a dynamic and intelligent deception layer, this technology enhances overall security posture and protects critical assets from compromise. The Context-aware Knowledge System and Methods for Deploying Deception Mechanisms offers a compelling solution for organizations looking to stay ahead of evolving cyber threats.","layman_explanation":"The Context-aware Knowledge System and Methods for Deploying Deception Mechanisms patent addresses the growing problem of sophisticated cyberattacks that bypass traditional security measures. Current security systems often rely on static defenses that are easily identified and circumvented by attackers. This patent offers a dynamic and adaptive approach to network security.\n\nInstead of relying solely on firewalls and antivirus software, this invention creates a network of 'deception mechanisms.' Think of these as decoys or traps strategically placed within your network. These decoys mimic real systems and data, luring attackers away from valuable assets. It's like setting up a fake bank vault to protect the real one. When an attacker interacts with these decoys, the system detects their presence and learns about their methods.\n\nThe key innovation is the 'context-aware' aspect. The system doesn't just randomly deploy decoys. It analyzes the network environment, identifies critical assets, and studies historical attack patterns to determine the optimal placement and configuration of these deception mechanisms. This ensures that the decoys are realistic and effective in attracting attackers. The system also continuously monitors the network and adjusts the deception strategy based on changing conditions.\n\nThis technology matters because it provides a more proactive and effective way to protect against cyber threats. By diverting attackers to the deception network, the system protects critical assets from being compromised. It also provides valuable insights into attacker behavior, allowing organizations to improve their overall security posture. The market impact is significant, as organizations increasingly seek adaptive security solutions to combat sophisticated attacks. This patent offers a competitive advantage by providing a dynamic and intelligent deception layer.\n\nLooking ahead, this technology has the potential to be integrated with other security tools and platforms to provide a comprehensive security solution. Market adoption is expected to increase as organizations recognize the limitations of traditional security measures. The investment implications are positive, as this technology offers a strong ROI by reducing the risk of data breaches and improving threat intelligence.","technical_analysis":"The Context-aware Knowledge System and Methods for Deploying Deception Mechanisms patent presents a sophisticated architecture for adaptive network security. The system's core components include a deception profiler, a deception mechanism deployment engine, and a threat monitoring and analysis module. The deception profiler analyzes network topology, asset characteristics, and historical attack data to create a context-aware profile of the network.\n\nThe deception mechanism deployment engine uses this profile to determine the optimal number and placement of deception mechanisms. These mechanisms are designed to emulate the characteristics of real machines, including operating systems, applications, and data. The engine can dynamically adjust the configuration of these mechanisms based on changing network conditions and attacker behavior.\n\nThe threat monitoring and analysis module continuously monitors the interactions of attackers with the deception network. This module uses machine learning algorithms to identify suspicious activity and generate alerts. The alerts provide valuable threat intelligence, including attacker tactics, techniques, and procedures (TTPs).\n\nThe integration of these components allows the system to adapt to evolving threats in real-time. The deception profiler continuously updates the network profile based on new data. The deployment engine adjusts the configuration of the deception mechanisms to maintain their effectiveness. The monitoring and analysis module provides actionable threat intelligence to security teams.\n\nFrom a code-level perspective, the system relies on a combination of programming languages and frameworks. The deception profiler may be implemented using Python and data analysis libraries like Pandas and NumPy. The deployment engine may be implemented using Java or Go. The threat monitoring and analysis module may be implemented using a combination of Python and machine learning frameworks like TensorFlow or PyTorch. The integration of these components requires a robust API and messaging infrastructure.\n\nThe performance characteristics of the system depend on the size and complexity of the network. The deception profiler can be computationally intensive, especially for large networks. The deployment engine must be able to quickly adjust the configuration of the deception mechanisms to minimize latency. The monitoring and analysis module must be able to process large volumes of data in real-time. Optimizing the performance of these components is critical for ensuring the effectiveness of the system.","business_analysis":"The Context-aware Knowledge System and Methods for Deploying Deception Mechanisms patent addresses a significant market need for proactive and adaptive cybersecurity solutions. The market opportunity for deception technology is growing rapidly, driven by the increasing sophistication of cyberattacks and the limitations of traditional security defenses. The competitive advantages of this technology include its context-aware approach, its adaptive deployment strategy, and its ability to provide actionable threat intelligence.\n\nThe revenue potential for this technology is substantial. The system can be offered as a standalone product or as a managed service. It can be targeted at organizations of all sizes, from small businesses to large enterprises. The business model can be based on a subscription fee, a usage-based fee, or a combination of both.\n\nThe strategic positioning of this technology is strong. It aligns with the growing trend towards proactive and adaptive security. It complements existing security defenses, providing an additional layer of protection. It can be integrated with other security tools and platforms to provide a comprehensive security solution.\n\nThe ROI projections for this technology are compelling. By reducing the attack surface and minimizing the risk of data breaches, the system can save organizations significant amounts of money. It can also improve their security posture and enhance their reputation. The payback period for the investment in this technology is typically short.\n\nFrom an investment perspective, the Context-aware Knowledge System and Methods for Deploying Deception Mechanisms represents an attractive opportunity. The market is growing rapidly, the technology is innovative, and the business model is sound. Investors can expect a high return on their investment.","faqs":null,"topics":["network security","deception technology","cybersecurity","threat mitigation","attack detection","context","aware","knowledge"],"tech_cluster":null},"seo":{"title":"Context-aware Deception - Network Security Patent US-9853999","description":"Discover how this context-aware deception system enhances network security. Full patent analysis, claims, and technical details available.","keywords":["network security","deception technology","cybersecurity","threat mitigation","attack detection","adaptive security","patent","patent US-9853999"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853999","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9853999","citation_suggestion":"Patentable. \"Context-aware knowledge system and methods for deploying deception mechanisms\" (US-9853999). https://patentable.app/patents/US-9853999","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853999","json":"https://patentable.app/api/llm-context/US-9853999","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T18:06:31.639Z"}