{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853997","patent":{"patent_number":"US-9853997","title":"Multi-channel change-point malware detection","assignee":null,"inventors":[],"filing_date":"2015-04-14T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04L","G06F","G06F","G06F","G06F","H04L","G06F","G06F","H04W"],"num_claims":23,"abstract":"A malware detection system and method detects changes in host behavior indicative of malware execution. The system uses linear discriminant analysis (LDA) for feature extraction, multi-channel change-point detection algorithms to infer malware execution, and a data fusion center (DFC) to combine local decisions into a host-wide diagnosis. The malware detection system includes sensors that monitor the status of a host computer being monitored for malware, a feature extractor that extracts data from the sensors corresponding to predetermined features, local detectors that perform malware detection on each stream of feature data from the feature extractor independently, and a data fusion center that uses the decisions from the local detectors to infer whether the host computer is infected by malware."},"analysis":{"summary":"The Multi-channel Change-point Malware Detection patent presents a novel system and method for detecting changes in host behavior that are indicative of malware execution. The core innovation lies in its use of linear discriminant analysis (LDA) for feature extraction, multi-channel change-point detection algorithms to infer malware execution, and a data fusion center (DFC) to combine local decisions into a host-wide diagnosis. The problem being solved is the increasing sophistication of malware and the inadequacy of traditional signature-based detection methods. This technology addresses this issue by focusing on behavioral changes rather than specific signatures, enabling it to detect zero-day exploits and polymorphic malware.\n\nThe key technical approach involves sensors that monitor the status of a host computer, a feature extractor that extracts data from the sensors corresponding to predetermined features, local detectors that perform malware detection on each stream of feature data independently, and a data fusion center that uses the decisions from the local detectors to infer whether the host computer is infected by malware. The business value and applications of this technology are significant. It offers improved detection accuracy, reduced false positives, and adaptability to new threats. This makes it an invaluable tool for organizations seeking to protect their critical infrastructure and sensitive data from advanced persistent threats (APTs). The market opportunity is substantial, as the demand for effective cybersecurity solutions continues to grow in response to the increasing sophistication of cyberattacks.\n\nThis system has the potential to revolutionize the way organizations approach malware detection. By focusing on behavioral analysis, data fusion, and continuous learning, it provides a robust and intelligent defense against evolving malware threats. The modular design allows for flexible deployment and scalability, making it suitable for both small businesses and large enterprises. The integration of this system into existing security architectures is relatively straightforward, further enhancing its appeal.\n\nThe technology's ability to learn and adapt over time is a critical advantage. By continuously monitoring host behavior and refining its detection algorithms, it can stay ahead of emerging threats and maintain a high level of accuracy. This dynamic learning capability ensures that the system remains effective even as malware evolves, making it a valuable long-term investment for organizations seeking to protect themselves from cyber threats.","layman_explanation":"The Multi-channel Change-point Malware Detection patent addresses a growing problem in cybersecurity: the increasing sophistication of malware and the limitations of traditional detection methods. Signature-based antivirus software, which relies on recognizing known malware patterns, struggles to keep up with new and evolving threats. This leaves businesses vulnerable to zero-day exploits and other advanced attacks.\n\nThis technology works by monitoring the behavior of a computer system and identifying deviations from normal activity. Instead of looking for specific malware signatures, it analyzes various data streams from the system, such as network traffic, CPU usage, and memory access patterns. It then uses statistical techniques to detect subtle changes that may indicate the presence of malware. Think of it like a doctor who monitors a patient's vital signs to detect early signs of illness. If the doctor notices a sudden change in heart rate or blood pressure, it could be a sign of a problem.\n\nThis approach matters because it allows businesses to detect and prevent malware attacks before they cause significant damage. By focusing on behavioral analysis, it can identify zero-day exploits and other advanced threats that would otherwise go undetected. This can save businesses significant amounts of money in terms of reduced downtime, data loss, and incident response costs. It also provides a competitive advantage by ensuring business continuity and protecting brand reputation.\n\nLooking ahead, this technology has the potential to be integrated into a wide range of security products and services. It could be used to enhance existing antivirus software, intrusion detection systems, and security information and event management (SIEM) platforms. It could also be used to develop new managed security services that provide proactive threat detection and prevention. As the threat landscape continues to evolve, this technology will become increasingly valuable for businesses of all sizes. The market adoption timeline will depend on the pace of innovation and the willingness of businesses to invest in advanced security solutions. However, the long-term investment implications are clear: proactive threat detection is essential for maintaining a secure and resilient business in the digital age.","technical_analysis":"The Multi-channel Change-point Malware Detection patent details a system built upon a multi-layered architecture for detecting malware through behavioral analysis. The system comprises sensors, feature extractors, local detectors, and a data fusion center (DFC). The sensors continuously monitor the host computer's status, gathering data on various system parameters. The feature extractor then processes this raw data to identify features indicative of malware execution. Linear discriminant analysis (LDA) plays a crucial role in reducing the dimensionality of the feature space, thereby enhancing the accuracy and efficiency of the detection process. This dimensionality reduction is critical for real-time performance.\n\nThe local detectors operate independently on each stream of feature data, employing change-point detection algorithms to identify deviations from normal behavior. These algorithms are specifically designed to be highly sensitive to subtle changes that may signal the presence of malware. The DFC then aggregates the decisions from the local detectors, using a weighted voting scheme to combine the evidence and provide a comprehensive diagnosis of the system's overall security posture. This fusion of evidence from multiple sources is key to reducing false positives and increasing detection accuracy.\n\nA key technical innovation is the use of multi-channel change-point detection. By analyzing host behavior across multiple channels simultaneously, the system can detect malicious activity that might be missed by single-channel approaches. This is particularly important in the context of advanced persistent threats (APTs), which often employ sophisticated techniques to evade detection. The system's adaptive learning capability is another significant technical advantage. By continuously monitoring host behavior and refining its detection algorithms, the system can stay ahead of emerging threats and maintain a high level of accuracy. This dynamic learning ensures that the system remains effective even as malware evolves.\n\nIntegration into existing security architectures is facilitated by the modular design, which allows for flexible deployment and scalability. The system can be easily integrated with existing security information and event management (SIEM) systems, providing a unified view of the organization's security posture. From a coding perspective, the LDA and change-point detection algorithms would likely be implemented using libraries such as SciPy or scikit-learn in Python. The DFC could be implemented using a distributed computing framework such as Apache Spark to handle the large volumes of data generated by the sensors. Performance is optimized through careful selection of features, efficient implementation of algorithms, and distributed processing of data. The system's overall effectiveness depends on the quality of the sensors, the accuracy of the feature extractor, and the robustness of the change-point detection algorithms.","business_analysis":"The Multi-channel Change-point Malware Detection patent addresses a critical need in the cybersecurity market: the detection of increasingly sophisticated malware that evades traditional signature-based solutions. The market opportunity for this technology is substantial, driven by the rising costs of cybercrime and the growing awareness of the limitations of existing security measures. The global cybersecurity market is projected to reach hundreds of billions of dollars in the coming years, with a significant portion of that spending allocated to advanced threat detection and prevention solutions.\n\nThe competitive advantages of this technology stem from its proactive and adaptive approach. By focusing on behavioral analysis rather than specific signatures, it can detect zero-day exploits and polymorphic malware that would otherwise go undetected. The use of multi-channel change-point detection and data fusion further enhances its accuracy and reduces false positives. These advantages translate into significant revenue potential for companies that license or implement this technology. Potential business models include software licensing, managed security services, and integration with existing security products.\n\nFrom a strategic positioning perspective, this technology can be positioned as a premium solution for organizations seeking to protect their critical infrastructure and sensitive data from advanced persistent threats (APTs). It can be targeted at industries such as finance, healthcare, and government, where the consequences of a successful cyberattack are particularly severe. A key element of the business strategy should be to establish partnerships with leading cybersecurity vendors and integrators to facilitate market penetration and adoption.\n\nROI projections for this technology are highly favorable. By preventing successful breaches and reducing the need for costly incident response efforts, it can save organizations significant amounts of money. Moreover, by improving the security of critical infrastructure, it can help to maintain the stability of the global economy. The ability to detect and prevent malware attacks before they cause significant damage is crucial for maintaining business continuity and protecting brand reputation. This technology provides a valuable tool for organizations seeking to mitigate these risks.\n\nHowever, there are also challenges to consider. The implementation of this technology requires specialized expertise and may involve significant upfront costs. Moreover, the effectiveness of the system depends on the quality of the data collected by the sensors and the accuracy of the feature extractor. Ongoing maintenance and updates are also required to ensure that the system remains effective against evolving threats. Despite these challenges, the potential benefits of this technology far outweigh the risks. The Multi-channel Change-point Malware Detection patent represents a valuable asset for organizations seeking to enhance their cybersecurity posture and protect themselves from advanced threats.","faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Multi-channel change-point malware detection","description":"A malware detection system and method detects changes in host behavior indicative of malware execution. The system uses linear discriminant analysis (LDA) for feature extraction, multi-channel change-","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853997","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-9853997","citation_suggestion":"Patentable. \"Multi-channel change-point malware detection\" (US-9853997). https://patentable.app/patents/US-9853997","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853997","json":"https://patentable.app/api/llm-context/US-9853997","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T04:32:20.648Z"}