IP Library Granted Patent US 12,401,686
Granted Patent B2
US 12,401,686 · App. 16/636,282 · Granted Aug 26, 2025

Detecting changes to web page characteristics using machine learning

Inventors: Fadi El-Moussa (London, GB); Xiaofeng Du (London, GB)
Assignee: British Telecommunications Public Limited Company
H04L63/1483G06F16/958G06N3/045H04L63/1416
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,401,686
App. No.
16/636,282
Granted
Aug 26, 2025
Kind
B2
Abstract

A computer implemented method to detect an anomalous change to a web application, the web application executing with a web server, the method including receiving a first set of records for the web application operating in a training mode of operation, each record including characteristics of a content of a web page for the web application; generating a sparse distributed representation of the set of records to form a training set for a hierarchical temporal memory (HTM); training the HTM based on the training set in order that the trained HTM provides a model of the operation of the web application in the training mode of operation; receiving a second set of records for the web application, each record including characteristics of content of the web page; generating a sparse distributed representation of the second set of records to form an input set for the trained HTM; executing the trained HTM based on the input set to determine a degree of recognition of the records of the input set; and responsive to a determination that a degree of recognition of one or more records of the input set is below a threshold degree, identifying an anomalous change to the web page.

Claims (29)

1. A computer implemented method to detect an anomalous change to a configuration of a web application, the web application executing with a web server, the method comprising:

receiving a first set of distinct records for the web application operating in a training mode of operation during which the web application is isolated from a wide area network, each record in the first set of distinct records including characteristics of a content of a web page the web application during a training time period;

generating a sparse distributed representation of the first set of distinct records to form a training set for a hierarchical temporal memory (HTM);

training the HTM based on the training set in order that the trained HTM provides a model of operation of the web application in the training mode of operation, wherein the HTM evaluates an anomaly score for the records in the first set of distinct records and the HTM is trained until the anomaly score meets a predetermined threshold degree of anomaly;

receiving a second set of distinct records for the web application, each record in the second set of distinct records including characteristics of the web application during an operational time period, the operational time period being distinct from the training time period;

generating a sparse distributed representation of the second set of distinct records to form an input set for the trained HTM;

executing the trained HTM based on the input set to determine a degree of recognition of the records of the input set;

responsive to a determination that a degree of recognition of one or more records of the input set is below a threshold degree, identifying an anomalous change to the configuration of the web page; and

in response to the identification of the anomalous change to the web page, causing a responsive measure to the anomalous change to be implemented that includes one or more of the following actions:

interrupting operation of the web application;

identifying client components in communication with the web application as potentially compromised;

executing at least one of an intrusion detection, malware detection, virus removal, or a malware removal process for the web application; or

effecting at least one of a redeployment, a reinstallation, or a reconfiguration of the web application.

2. The method of claim 1 , wherein the characteristics of the web page include records corresponding to hypertext markup language (HTML) tags in the web page.

3. A computer system comprising:

a processor and memory storing computer program code for detecting an anomalous change to a configuration of a web application, the web application executing with a web server, by:

receiving a first set of distinct records for the web application operating in a training mode of operation during which the web application is isolated from a wide area network, each record in the first set of distinct records including characteristics of a content of a web page for the web application during a training time period;

generating a sparse distributed representation of the first set of distinct records to form a training set for a hierarchical temporal memory (HTM);

training the HTM based on the training set in order that the trained HTM provides a model of operation of the web application in the training mode of operation, wherein the HTM evaluates an anomaly score for the records in the first set of distinct records and the HTM is trained until the anomaly score meets a predetermined threshold degree of anomaly;

receiving a second set of records for the web application operating in a production mode of operation, each record in the second set of records including characteristics of content of the web page during an operational time period, the operational time period being distinct from the training time period and the production mode of operation being distinct from the training mode of operation;

generating a sparse distributed representation of the second set of distinct records to form an input set for the trained HTM;

executing the trained HTM based on the input set to determine a degree of recognition of the records of the input set;

responsive to a determination that a degree of recognition of one or more records of the input set is below a threshold degree, identifying an anomalous change to the web page; and

responsive to the identification of the anomalous change to the web page, causing a responsive measure to the anomalous change to be implemented that includes one or more of the following actions:

interrupting operation of the web application;

identifying client components in communication with the web application as potentially compromised;

executing at least one of an intrusion detection, malware detection, virus removal, or a malware removal process for the web application; or

effecting at least one of a redeployment, a reinstallation, or a reconfiguration of the web application.

4. A non-transitory computer-readable storage element comprising computer program code to, when loaded into a computer system and executed thereon, cause the computer system to perform the method as claimed in claim 1 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 31, 2021
From: EL-MOUSSA, FADI; DU, XIAOFENG
To: BRITISH TELECOMMUNICATIONS PUBLIC LIMITED COMPANY
Reel/Frame 055781/0068 →
Priority Claims (1)
EP 17184578 · Aug 2, 2017 · regional
Continuity (1)
Related Publication 20200228569A1 · Jul 16, 2020
References Cited (61)
US 10769292B2 · Daniel · 2020 [cited by applicant]
US 10951651B1 · Golan et al. · 2021 [cited by applicant]
US 11050768B1 · Stickle · 2021 [cited by examiner]
US 20090282480A1 · Lee · 2009 [cited by examiner]
US 20090313193A1 · Hawkins et al. · 2009 [cited by applicant]
US 20120005753A1 · Provos et al. · 2012 [cited by applicant]
US 20120047581A1 · Banerjee et al. · 2012 [cited by applicant]
US 20130054496A1 · Marianetti, II et al. · 2013 [cited by applicant]
US 20140067734A1 · Hawkins · 2014 [cited by examiner]
US 20150127595A1 · Hawkins, II · 2015 [cited by examiner]
US 20160028762A1 · Di Pietro et al. · 2016 [cited by applicant]
US 20160321557A1 · Hawkins et al. · 2016 [cited by applicant]
US 20170053025A1 · De Sousa Webber · 2017 [cited by examiner]
US 20170068816A1 · Cavazos · 2017 [cited by applicant]
US 20170104773A1 · Flacher · 2017 [cited by examiner]
US 20170116412A1 · Stopel et al. · 2017 [cited by applicant]
US 20170116415A1 · Stopel et al. · 2017 [cited by applicant]
US 20170126718A1 · Baradaran · 2017 [cited by examiner]
US 20180020024A1 · Chao · 2018 [cited by examiner]
US 20180124080A1 · Christodorescu · 2018 [cited by examiner]
US 20210089650A1 · Daniel · 2021 [cited by applicant]
US 20210089670A1 · Daniel · 2021 [cited by applicant]
CN 102411687A · 2012 [cited by applicant]
CN 106951783A · 2017 [cited by applicant]
EP 1814055A2 · 2007 [cited by applicant]
WO WO2017194637A1 · 2017 [cited by applicant]
Ahmad S., et al., “How do Neurons Operate on Sparse Distributed Representations? A Mathematical Theory of Sparsity, Neurons and Active Dendrites,” Retrieved from https://arxiv.org/ftp/arxiv/papers/1601/1601.00720.pdf, 2… [cited by applicant]
Ahmad S., et al., “Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory,” retrieved from https://arxiv.org/ftp/arxiv/papers/1503/1503.07469.pdf on Mar. 28, 2018, Numenta… [cited by applicant]
Ahmad S., et al., “Real-Time Anomaly Detection for Streaming Analytics,” retrieved from https://arxiv.org/pdf/1607.02480.pdf on Mar. 28, 2018, Numenta, Inc., Jul. 8, 2016, 10 pages. [cited by applicant]
Antonopoulos A.M., “Mastering Bitcoin, Unlocking Digital Crypto-Currencies,” O'Reilly Media, Apr. 2014, 282 pages. [cited by applicant]
Assia Y., et al., “Colored Coins Whitepaper,” 2015, available at https://docs.google.com/document/d/1AnkP_cVZTCMLIzw4DvsW6M8Q2JC0llzrTLuoWu2z1BE/, 23 pages. [cited by applicant]
Berger V., “Anomaly Detection in User Behavior of Websites Using Hierarchical Temporal Memories,” May 19, 2017, retrieved from URL http://kth.diva-portal.org/smash/get/diva2:1094877/FULLTEXT01.pdf on Sep. 5, 2017. [cited by applicant]
Billaudelle S., et al., “Porting HTM Models to the Heidelberg Neuromorphic Computing Platform,” Feb. 9, 2016, Cornell University Library, retrieved from https://arxiv.org/pdf/1505.02142.pdf, 9 pages. [cited by applicant]
Communication pursuant to Article 94(3) EPC for European Application No. 18811033.2, mailed Jan. 14, 2022, 4 pages. [cited by applicant]
Cui Y., et al., “Continuous Online Sequence Learning with an Unsupervised Neural Network Model,” Neural Computation, vol. 28, No. 11, Nov. 2016, pp. 2474-2504. [cited by applicant]
Cui Y., et al., “The HTM Spatial Pooler: A Neocortical Algorithm for Online Sparse Distributed Coding,” retrieved from https://www.biorxiv.org/content/biorxiv/early/2017/02/16/085035.full.pdf on Mar. 28, 2018, Numenta I… [cited by applicant]
Hawkins J., et al., “Hierarchical Temporal Memory Concepts, Theory, and Terminology,” Numenta Inc., 2006, 19 pages. [cited by applicant]
Hawkins J., et al., “Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex,” Frontiers in Neural Circuits, vol. 10, Article 23, Mar. 2016, 13 pages. [cited by applicant]
Hawkins J, “On Intelligence,” How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines, 2004, Times Books, Jul. 14, 2005, 174 pages. [cited by applicant]
International Preliminary Report on Patentability for Application No. PCT/EP2018/070631, mailed on Feb. 13, 2020, 9 pages. [cited by applicant]
International Preliminary Report on Patentability for Application No. PCT/EP2018/070632, mailed on Feb. 13, 2020, 10 pages. [cited by applicant]
International Search Report and Written Opinion for Application No. PCT/EP2018/070631, mailed on Aug. 17, 2018, 12 pages. [cited by applicant]
International Search Report and Written Opinion for Application No. PCT/EP2018/070632, mailed on Aug. 21, 2018, 13 pages. [cited by applicant]
International Search Report and Written Opinion For PCT Application No. PCT/EP2018/083358, mailed on Feb. 15, 2019, 11 pages. [cited by applicant]
Lavin A., et al., “Evaluating Real-Time Anomaly Detection Algorithms—The Numenta Anomaly Benchmark,” Retrieved from https://arxiv.org/ftp/arxiv/papers/1510/1510.03336.pdf, Numenta, Inc., Oct. 9, 2015, 8 pages. [cited by applicant]
Network Working Group, “Hypertext Transfer Protocol—HTTP/1.1,” Jun. 1999, 114 Pages. [cited by applicant]
Numenta, “Biological and Machine Intelligence (BAMI), A living book that documents Hierarchical Temporal Memory (HTM),” Mar. 8, 2017, 69 pages. [cited by applicant]
Numenta, “Hierarchical Temporal Memory including HTM Cortical Learning Algorithms,” Version 0.2.1, Numenta, Sep. 12, 2011, 68 pages. [cited by applicant]
Numenta, “The Science of Anomaly Detection,” How HTM Enables Anomaly Detection in Streaming Data, Dec. 31, 2015, Retrieved from URL:https://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%2… [cited by applicant]
Office Action For GB Application No. 17184578.7, mailed on Sep. 18, 2017, 11 pages. [cited by applicant]
Office Action For GB Application No. 17184580.3, mailed on Nov. 3, 2017, 8 pages. [cited by applicant]
Office Action For GB Application No. 1720174.0, mailed on May 23, 2018, 6 pages. [cited by applicant]
Olshausen B.A., et al., “Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by VI?,” Pergamon, vol. 37, No. 23, 1997, pp. 3311-3325. [cited by applicant]
Purdy S., “Encoding Data for HTM Systems,” Retrieved from https://arxiv.org/ftp/arxiv/papers/1602/1602.05925.pdf, Numenta, Inc., Feb. 2016, 11 pages. [cited by applicant]
Rosenfeld M., “Overview of Colored Coins,” https://bravenewcoin.com/assets/Whitepapers/Overview-of-Colored-Coins.pdf, Dec. 4, 2012, 13 pages. [cited by applicant]
Taylor M., “Sparse Distributed Representations,” Numenta, 2017, 3 pages. [cited by applicant]
Thorpe S.J., “Spike Arrival Times: A Highly Efficient Coding Scheme for Neural Networks,” Parallel Processing in Neural Systems and Computers, 1990, pp. 91-94. [cited by applicant]
Wang, C. et al.; “A Distributed Anomaly Detection System for In-Vehicle Network Using HTM,” 2018, vol. 6, pp. 9091-9098. [cited by applicant]
Wood G., “Ethereum: A Secure Decentralised Generalized Transaction Ledger,” EIP-150 Revision, Jun. 4, 2014, pp. 1-32. [cited by applicant]
Application and File History for U.S. Appl. No. 16/636,280, filed Feb. 3, 2020, Inventors: El Moussa et al. [cited by applicant]
Application and File History for U.S. Appl. No. 15/733,180, filed Jun. 4, 2020, Inventors: El Moussa et al. [cited by applicant]