DETAILED ACTION
This action is in response to the claims filed 5/27/2025. Claims 1-20 are pending. Independent claims 1, 6, 11 and 16, and corresponding dependent claims are directed towards a method, system, non-transitory computer readable medium and apparatus for detecting malicious DNS requests using machine learning.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 6, 11 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 7 and 10 of U.S. Patent No. 12,341,786. Although the claims at issue are not identical, they are not patentably distinct from each other because of subject matter indicated below:
Application 19/219987
US Patent No. 12,341,786
Claim 1
Claim 1
Claim 6
Claim 4
Claim 11
Claim 7
Claim 16
Claim 10
Information Disclosure Statement
The information disclosure statements filed 10/30/2025 & 10/31/92025 fail to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because they fail to identify by publisher, author (if any), title, relevant pages of the publication, date, and place of publication every reference cited (see CFR 1.98 (b)(5)). Applicant has merely provided a URL for each document without any other identifying information, as such the validity of the references as prior art cannot be determined. They have been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in these information disclosure statements or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Specification
The disclosure is objected to because of the following informalities: the first recitation of the following acronyms is not expanded: [0003] IPv4 and IPv6; [0030] NXDOMAIN; [0063] IEEE; and [0065] CD-ROM, CD-RW and DVD; [0018] “analysis logic [[230]]250” for proper numbering; [0020] “communication interfaces [[230]]220” for proper numbering; [0023] “DNS request analysis logic [[240]]250” for proper numbering; [0034] “classification server [[460]]420” for proper numbering; [0035] “DNS server [[430]]410” for proper numbering; and [0039] “neural network [[510]]500” for proper numbering. Appropriate correction is required.
Claim Objections
Claims 9, 14 and 19 are objected to because of the following informalities, shown with suggested amendments: Claim 9 l.4, Claim 14 l.5 and Claim 19 l.4 “transmit, via a plugin module, DNS request information” for grammar. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 6, 8, 11, 13, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2018/0351972 A1), published Dec. 6, 2018, in view of Wang, Zheng, “Use of supervised machine learning to detect abuse of COVID-19 related domain names”, published May 2022.
As to claims 1, 6, 11 and 16, Yu substantially discloses a method (Yu [Abstract] process), system (Yu [Abstract] system; ¶14 processor & memory with instructions; Fig. 1B; ¶56 system architecture), non-transitory computer readable medium (Yu ¶14 memory with instructions) and network traffic management apparatus (Yu ¶14 apparatus, processor & memory with instructions; Fig. 1B item 152 DNS Server Policy Engine; ¶56), hereinafter referred to by the method, the method implemented by a network traffic management system comprising one or more network traffic management apparatuses, server devices, or client devices (Yu [Abstract] system; ¶14 processor & memory with instructions; Fig. 1B; ¶56 system architecture (having clients servers, firewall, etc.), the method comprising: receiving a domain name system (DNS) request (Yu ¶56 client submits DNS query); determining, using a machine learning model, that the DNS request is a malicious DNS request (Yu ¶41 CNN used to detect malicious traffic; ¶56 use of DGA detection (that uses online learning) to determine if query is malicious; ¶177 DGA detection model (classifier)), wherein the machine learning model comprises a convolutional neural network (Yu ¶41 CNN used to detect malicious traffic); and blocking the malicious DNS request based on the determining (Yu ¶21 use of DGA detection to block access to malicious domains in real-time; ¶55 potentially malicious responses are blocked; ¶178 mitigation action that includes blocking packets from malware network domain). Yu fails to explicitly disclose wherein the machine learning model comprises a convolutional neural network that comprises a two-dimensional convolutional layer, and wherein the determining comprises organizing a domain name of the DNS request as a two-dimensional matrix and providing the two-dimensional matrix as an input to the two-dimensional convolutional layer of the convolutional neural network. Wang describes the use of supervised machine learning to detect abuse of COVID-19 related domain names. With this in mind, Wang discloses wherein the machine learning model comprises a convolutional neural network that comprises a two-dimensional convolutional layer (Wang pg. 7-8 § “CoreDNS-CNN” shown having multiple 2D layers), and wherein the determining comprises organizing a domain name of the DNS request as a two-dimensional matrix (Wang pg. 7 § “CoreDNS-CNN” malicious domain name detection by transforming a 256-character input into a 16 x 16 array for processing by 2D layers of the CNN) and providing the two-dimensional matrix as an input to the two-dimensional convolutional layer of the convolutional neural network (Wang pg. 7 § “CoreDNS-CNN” malicious domain name detection by transforming a 256-character input into a 16 x 16 array for processing by 2D layers of the CNN). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the CoreDNS-CNN of Wang with the CNN-based inline malicious-domain classifier of Yu, such that the domain name is converted into a two dimensional matrix and processed using 2D layering, as it would advantageously assist in detection of threats/abnormalities towards a critical component of the internet (Wang §5.2¶1).
As to claims 3, 8, 13 and 18, Yu and Wang disclose the invention as claimed as described in claims 1, 6, 11 and 16, respectively, including further comprising: classifying the DNS request as malicious using a classification server (Yu ¶21 DGA detection in a DNS server; ¶23 deployed DGA detection model includes a classifier; ¶55-56 query sent to inline DGA detection which indicates malicious query); generating a classification result record associated with the classification of the DNS request (Yu ¶56 query is determined malicious and IP address is sent to firewall to place on blacklist – entry in blacklist as record); and storing the classification result record in a database (Yu ¶56 query is determined malicious and IP address is sent to firewall to place on blacklist – blacklist constitutes a database).
Claims 2, 7, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2018/0351972 A1), published Dec. 6, 2018, in view of Wang, Zheng, “Use of supervised machine learning to detect abuse of COVID-19 related domain names”, published May 2022, in view of St. Pierre (US 2021/0160283 A1), published May 27, 2021.
As to claims 2, 7, 12 and 17, Yu and Wang disclose the invention as claimed as described in claims 1, 6, 11 and 16, respectively, failing, however, to explicitly disclose transmitting a honeypot Internet Protocol address in response to determining the DNS request is a malicious DNS request, wherein the honeypot Internet Protocol address is associated with a computing device configured to monitor activities associated with the honeypot Internet Protocol address. St. Pierre describes the management of botnet attacks to a computer network. With this in mind, St. Pierre discloses transmitting a honeypot Internet Protocol address in response to determining the DNS request is a malicious DNS request (St. Pierre ¶27confirm DNS name is malicious and respond with address of traffic inspector; ¶25 monitoring via honeypot), wherein the honeypot Internet Protocol address is associated with a computing device configured to monitor activities associated with the honeypot Internet Protocol address (St. Pierre ¶27 traffic sent to traffic inspector is inspected and logged). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the honeypot/traffic inspector of St. Pierre with the malicious domain request classification of Yu and Wang, such that upon determination that the DNS request is malicious an address of a traffic inspector is returned instead, as it would advantageously allow for analyzing of attacks and possible derivation of new patterns for use in detection and mitigation (St. Pierre ¶4).
Claims 4-5, 9-10, 14-15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2018/0351972 A1), published Dec. 6, 2018, in view of Wang, Zheng, “Use of supervised machine learning to detect abuse of COVID-19 related domain names”, published May 2022, in view of Chanakya Ekbote, “CoreDNS Machine Learning Plugin”, published Oct. 13, 2020, hereinafter referred to as Ekbote.
As to claims 4, 9, 14, and 19, Yu and Wang disclose the invention as claimed as described in claims 3, 8, 13 and 18, respectively, failing, however, to explicitly disclose extracting, via a plugin module, DNS request information from the DNS request; and transmitting, via the plugin module, the DNS request information to the classification server. Ekbote describes a CoreDNS Machine Learning Plugin. With this in mind, Ekbote discloses extracting, via a plugin module, DNS request information from the DNS request (Ekbote § “General Overview” plugin intercepts requests and forwards them to middleware for further processing); and transmitting, via the plugin module, the DNS request information to the classification server (Ekbote § “General Overview” flask server (middleware) receives request and infers whether request is malicious). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the CoreDNS plugin of Ekbote with the malicious domain request classification of Yu and Wang, such that requests are intercepted and forwarded by the plugin, as it would advantageously provide malicious-domain classification without embedding the classifier directly in the DNS-server process.
As to claims 5, 10, 15 and 20, Yu and Wang disclose the invention as claimed as described in claims 1, 6, 11 and 16, respectively, failing, however, to explicitly disclose generating a graphical user interface (GUI) comprising an alert associated with the DNS request; and transmitting the GUI to a computing device. Ekbote discloses generating a graphical user interface (GUI) comprising an alert associated with the DNS request (Ekbote § “General Overview” visualization dashboard to visualize and analyze the results and to manually vet domain names; § “Overview” ¶2 plugin alerts sysadmin for manual vetting); and transmitting the GUI to a computing device (Ekbote § “Dash Application” providing user with interface to manually vet). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the CoreDNS plugin of Ekbote having its visualization dashboard, with the malicious domain request classification of Yu and Wang, such that malicious classifications are presented to an administrator for manual vetting, as it would advantageously allow an administrator to identify any false positives (Ekbote § “Overview” ¶1-2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bui et al. (US 2021/0377303 A1) is related to machine learning to determine domain reputation.
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/Eric W Shepperd/Primary Examiner, Art Unit 2492
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