Abstract: Techniques for providing identity protection are disclosed. A system, process, and/or computer program product for providing identity protection includes monitoring a plurality of sites, extracting predetermined user information for a user from the plurality of monitored sites to generate a profile of the user, analyzing, using a model, the profile of the user to detect whether one or more security vulnerabilities exist for social engineering attacks for one or more enterprise resources associated with the user, and performing an action in response to the one or more detected security vulnerabilities based on a policy.

Abstract: A method for prevention of malware infection of a user device. A first request for a first web page is received from the user device. Transmitting, to a website associated with the requested first web page and in response to the first request a second request for the first web page. In response to the second request, receiving a first set of data associated with the first web page. Generating, based on a first set of data in the first domain format, a first set of graphical images representing respective portions of the first set of data in a second domain format. Transmitting, to the user device, the first set of graphical images with correlation data configured to enable a user to interact with the graphical images on the user device in a manner that is substantially the same as though the user device had received the first web page in the first domain format and the first web page had been rendered from the first domain format by a program operating on the user device.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, at least one processor to execute the instructions to, in response to identifying malicious data: a) in response to determining that the at least one processor is controlled by the first operating system type, block a download from being executed, and b) in response to determining a switch from the first operating system type to the second operating system type, remove, from the at least one memory, an object downloaded in the download.

Abstract: Live and legitimate user traffic is used with in depth knowledge of the business logic for an API specification to perform security testing on a set of APIs. The present system intercepts and analyzes application program interface (API) traffic, identifies user session data, and identifies traffic suitable to duplicate. The identified traffic is duplicated and modified by addition of malicious code. The modified code is then sent to its intended API destination, where it is processed as normal. The resulting response and other traffic as well as the API system and optionally other systems, such as datastore systems, are analyzed to determine if the malicious code resulted in a valid attack. Results from the modified code attack attempts are reported to a user.

Abstract: A device inputs a first source code, which is source code of the software to be monitored; builds the first source code to generate a first binary; generates a first CFG based on the first binary; embeds a tamper detection feature and tamper detection feature calling functions in a first source code based on the first CFG to generate a second source code, builds a second source code to generate a second binary; generates a second CFG based on the second binary; creates an allowed list based on the second binary and the second CFG, and outputs the second binary and the allowed list. Here, in creating the allowed list, the monitoring range for the tamper detection feature calling functions is determined based on the second CFG, and a list of hash values of the monitoring range for the tamper detection feature calling functions is created as an allowed list.

Abstract: A device configuration method for a vehicle in a fleet of vehicles comprises, at a computing device communicatively coupled to electronic devices provided in the vehicle, obtaining at least one template configuration file assigned to the computing device based on a user selection, the at least one template configuration file specific to the fleet of vehicles and comprising first configuration data indicative of a manner in which the computing device is to interface with the electronic devices, and second configuration data indicative of a desired setting for at least one configuration parameter of one or more electronic devices, automatically self-configuring for operation based on the first configuration data, and transmitting, at least in part, the second configuration data to the one or more electronic devices to cause the one or more electronic devices to adjust the at least one configuration parameter to the desired setting.

Abstract: A system for automated sensitive information discovery, monitoring, and remediation using an agent associated to a data source and including: a module detecting the occurrence of events indicative of access to data; an module identifying the events classified as potentially threatening; a module extracting data associated to each potentially threatening event; and a module performing data analysis of the extracted data and determining a sensitivity score for the data to file associated to the potentially threatening event. The system also comprises a central platform in data communication with the agent and including: a module analyzing data received from the agent and identifying a potential security risk relative to one of a user or group of users associated to the data source, the data source, a specific file or a specific data type stored on the data source; and a control module triggering remediation actions upon detection of a security risk.

Abstract: Methods, apparatus, and processor-readable storage media for detection of anomalous behavior on online platforms using machine learning techniques are provided herein. An example method includes obtaining a set of machine learning models configured to detect anomalous behavior associated with users interacting with an online platform and performing an incremental machine learning process on one or more of the machine learning models in the set. The incremental machine learning process may include obtaining data related to interactions of users with the online platform, updating at least one of the machine learning models in the set based on the obtained data, comparing the machine learning models, and selecting one of the machine learning models from the set to be used by the online platform based on the comparison. The method may further include determining, utilizing the selected machine learning model, that a given user is exhibiting anomalous behavior on the online platform.

Abstract: Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.

Abstract: One example method includes integrating user space applications with kernel space events including primitives. The events are intercepted in kernel space and processed in user space. The events can be stored in a session cache that allows a holistic view of behavior to be determined with regard to resources of the computing system. The events in the session cache can be correlated to user or process behavior by provided a time-based view of the events.

Abstract: Disclosed is a hybridoma cell strain that secretes anti-dinitolmide monoclonal antibodies applicable to the field of food safety immunoassay methods. The hybridoma cell strain DAS3H10 that secretes anti-dinitolmide monoclonal antibodies has been deposited in Comprehensive Microbiology Center of China Microbial Culture Collection Management Committee (CGMCC), addressed in No. 1 Hospital No. 3 Institute of Microbiology of the Chinese Academy of Sciences, North Chenxi Road, Beijing Chaoyang District in Beijing. It is classified as a monoclonal cell strain. The deposit date is Nov. 28, 2019, and the deposit number is MCCC No. 19165. The monoclonal antibody secreted by the hybridoma cell strain DAS3H10 has a good affinity and high sensitivity to dinitolmide. Because of IC50 to dinitolmide up to 9.01 ng/mL, the monoclonal antibody could be used to prepare dinitolmide immunoassay kits and colloidal gold test strips, and can further provide a powerful means for detecting dinitolmide in animal-derived foods.

Abstract: Disclosed herein are systems and method for restoring files from a backup, the method including: retrieving a time indicator from a time server associated with a backup server; synchronizing time between the backup server and a computing device performing a backup, based on the time indicator; performing the backup of files from the computing device to the backup server, wherein a malicious process modifies at least one file being backed up at an incident time during the backup and performs an attempt to change a time of the computing device such that a modification timestamp of the at least one file precedes the incident time; blocking the attempt to change the time of the computing device; subsequent to completing the backup, detecting the malicious process infecting the computing device; and performing a restoration of the backup on the computing device.

Abstract: According to some embodiments of the disclosure, a method includes receiving an electronic communication directed to a data resource, determining, by a machine learning (ML) web application firewall (WAF), an attack probability of the electronic communication based on a plurality of features, wherein subsets of the plurality of features are arranged in a plurality of feature groups, adjusting the attack probability based on respective feature weights of the plurality of feature groups.

Abstract: Provided are a terminal and method for storing and parsing log data. The method includes collecting log data on the basis of a file path of the log data, storing metadata including the file path and log data paired with the metadata in a database (DB), classifying the log data on the basis of the metadata, acquiring type information of a parser related to the log data, and parsing the log data through the parser having the type information.

Abstract: There may be situations in which it is desirable to dynamically implement a rule on the firewall in response to detecting a particular pattern of user activity. However, the software code required for tracking user activity, identifying patterns of user activity, and deciding what action to take may be relatively complex. Deploying such software code on a firewall increases the complexity of the firewall. For example, the firewall can no longer be “stateless”. In some embodiments, the destination server works in combination with the firewall. The destination server monitors traffic to determine particular patterns of user activity. In response to a particular pattern of user activity being detected, an appropriate rule is established and the firewall is sent a command to implement the rule.

Abstract: Disclosed is a training data generation system for generating training data used to train machine learning models to inspect GenAI traffic to identify security and privacy concerns related to GenAI use. The training data generation system is seeded with initial prompts. The initial prompts include benign prompts, prompt injection attacks, and uploaded files. Each initial prompt is submitted to multiple GenAI applications to obtain responses. The corresponding prompts and responses are stored in a training data repository. Variations of the initial prompts are generated using, for example, one of the GenAI applications. Each variation is submitted to each of the GenAI applications as well, and the corresponding prompts and responses are stored. Another machine learning model, regex patterns, a combination, or the like may be used to label the prompts and responses in the training data repository to generate a large training data set quickly and efficiently.

Abstract: A method and a computing device for identifying malicious web resource are provided. The method comprises: obtaining a given link of a plurality of links, the given link referring to an initial malicious web resource; retrieving, from a database, simulated user parameters indicative of a simulated user environment and at least one user behavior vector including values indicative of simulated user actions with the initial malicious web resource; based on the simulated user parameters and the simulated user actions, determining at least one redirect chain, a given one of the at least one redirect chain including web resources defining a transition sequence from the initial malicious web resource to a respective target malicious web resource; generating, based on the at least one redirect chain, a redirect graph; and analyzing the redirect graph to determine a plurality of user redirect rules for further use in identifying in-use malicious web resources.

Abstract: Provided is a resource monitoring apparatus including a log generation unit for extracting a method requested from a hardware abstraction layer and generating a log; a log classification unit for classifying the generated log according to a type of an interface connected to the method; and a log determination unit for identifying a malicious activity from the classified log based on pattern information of the log set differently depending on the type of the interface.

Abstract: A method for deep packet inspection (DPI) in a software defined network (SDN). The method includes configuring a plurality of network nodes operable in the SDN with at least one probe instruction; receiving from a network node a first packet of a flow, the first packet matches the at least one probe instruction and includes a first sequence number; receiving from a network node a second packet of the flow, the second packet matches the at least one probe instruction and includes a second sequence number, the second packet is a response of the first packet; computing a mask value respective of at least the first and second sequence numbers indicating which bytes to be mirrored from subsequent packets belonging to the same flow; generating at least one mirror instruction based on at least the mask value; and configuring the plurality of network nodes with at least one mirror instruction.

Abstract: Disclosed is a cloud-based security system implemented in a reverse proxy that provides bidirectional traffic inspection to protect against privacy and security concerns related to the GenAI services. The security system intercepts requests directed to the GenAI service protected by the reverse proxy implementation of the network security system. The security system includes a GenAI request classifier trained to classify prompts submitted to the GenAI application as one of benign, prompt injection attack, or uploaded files. The security system further includes a GenAI response classifier trained to classify responses from the GenAI application as one of normal, leaked system prompt, leaked user uploaded files, or leaked training data.

Abstract: A method for symbolic analysis of a software program is described. The method comprises constructing a control flow graph (CFG), for a software program procedure, the CFG comprising nodes representing basic blocks reachable within the software program procedure, the basic blocks represented as respective functions from a first machine state on entry to a said basic block to a second machine state on exit from that basic block. The method further describes simplifying the CFG to a single node representing the software program procedure as a function from an input machine state on entry to the software program procedure to an output machine state on exit from the software program procedure, comparing said function to a rule set identifying vulnerabilities based on effects on the machine state; and determining a vulnerability within the software program procedure based on the comparing.

Abstract: A method for deep packet inspection (DPI) in a software defined network (SDN). The method includes configuring a plurality of network nodes operable in the SDN with at least one probe instruction; receiving from a network node a first packet of a flow, the first packet matches the at least one probe instruction and includes a first sequence number; receiving from a network node a second packet of the flow, the second packet matches the at least one probe instruction and includes a second sequence number, the second packet is a response of the first packet; computing a mask value respective of at least the first and second sequence numbers indicating which bytes to be mirrored from subsequent packets belonging to the same flow; generating at least one mirror instruction based on at least the mask value; and configuring the plurality of network nodes with at least one mirror instruction.

Abstract: A method and apparatus for testing a malware detection machine learning model. The method trains a malware detection model using a first dataset containing malware samples from a particular time period. The trained model is then tested using a second dataset that is a time shifted version of the first dataset.

Abstract: A method for securely starting up a container instance in one or more execution environments for one or more components of a technical installation, such an execution environment being designed to execute the container instance includes the following method steps: a) providing a configurable check function that is performed before and/or while starting up the container instance, b) logging each step for preparing at least one execution limitation required for starting up and/or executing the container instance, c) checking each logged step using at least one permissibility criterion configured in the check function, and d) completing the startup and if necessary the execution of the container instance if the at least one permissibility criterion is satisfied, or e) initiating an alerting measure or a measure that counteracts the startup if at least one of the possible permissibility criteria is not satisfied.

Abstract: In a distributed system, a first computer system may require computationally verifiable assurances of the authenticity and integrity of computations (e.g., performed as part of the execution of a program) performed by a second computer system. Methods described herein may be utilized to enforce and/or ensure the correct execution of a program. The first computer system may delegate execution of a program to a second computer system and a protocol may be employed to constrain the second computer system to perform a correct execution of the program. The protocol may include mitigation and correction routines that mitigate and/or correct the incorrect execution of a program. In various systems and methods described herein, the protocol may utilize a blockchain network such as a Bitcoin-based blockchain network.

Abstract: A system and method for detecting potential lateral movement in a cloud computing environment includes detecting a private encryption key and a certificate, each of which further include a hash value of a respective public key, wherein the certificate is stored on a first resource deployed in the cloud computing environment; generating in a security graph: a private key node, a certificate node, and a resource node connected to the certificate node, wherein the security graph is a representation of the cloud computing environment; generating a connection in the security graph between the private key node and the certificate node, in response to determining a match between the hash values of the public key of the private key and the public key of the certificate; and determining that the first resource node is potentially compromised, in response to receiving an indication that an element of the public key is compromised.

Abstract: Techniques for data correlation across data sources are presented herein. In some examples, a method includes the steps of retrieving a first datagram from a first data structure, wherein the first datagram represents a first update to a first data source; determining, by a first routine, a first correlation between the first datagram and a second datagram that is stored in a second data structure; based on determining the first correlation, updating the second datagram in accordance with first datagram; determining, by a second routine, whether the second datagram satisfies a completion condition; and based on determining that the second datagram satisfies the completion condition, storing the second datagram in the first data structure.

Abstract: A system and method for detecting lateral movement based on an exposed cryptographic network protocol (CNP) key in a cloud computing environment. The method includes: inspecting a first workload for a private CNP key, the private CNP key associated with a hash of a public CNP key; detecting in a security database a representation of the public CNP key; generating a lateral movement path, the lateral movement path including an identifier of a second workload, the second workload represented by a representation connected to the representation of the public CNP key.

Abstract: A request to add a new block to a blockchain is received. Data associated with the new block is scanned to identify malware and/or an anomaly. In response to identifying the malware and/or the anomaly in the data associated with the new block, an action is taken. The action includes: rejecting the request to add the new block to the blockchain, or removing the malware/anomaly from the new block and adding the new block to the blockchain. In a second embodiment, a malware event is identified that identifies malware/an anomaly in a block in a blockchain. In response to the malware event, an action is taken. The action includes: consolidating the blockchain, bypassing the block in the blockchain, consolidating the blockchain and bypassing the block in the blockchain, and deleting an encryption key that was used to encrypt the associated data that comprises the malware and/or the anomaly.

Abstract: Using user feedback for attack path analysis in an anomaly detection framework, including: performing an attack path analysis for a cloud deployment; receiving, from a user, user feedback for an attack vector of the attack path analysis; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the attack path analysis.

Abstract: A learning apparatus includes: a first estimation unit—configured to learn normal communication data, estimate probability density of the normal communication data, and update a parameter of a model; a clustering unit configured to cluster the normal communication data according to the probability density estimated by the first estimation unit; a second estimation unit configured to perform learning for each of clusters resulting from the clustering by the clustering unit, and estimate the probability density of the normal communication data of the cluster and update a parameter of a model representing characteristics of the probability density of the normal communication data of the cluster by using, as an initial value of the parameter, a parameter having already been learned in the first estimation unit; and an integration unit configured to integrate the estimated probability density of the clusters.

Abstract: Novel tools and techniques are provided for implementing fraud or distributed denial of service (“DDoS”) protection for session initiation protocol (“SIP”)-based communication. In various embodiments, a computing system may receive, from a first router, first SIP data indicating a request to initiate a SIP-based media communication session between a calling party at a source address and a called party at a destination address. The computing system may analyze the received first SIP data to determine whether the received first SIP data comprises any abnormalities indicative of potential fraudulent or malicious actions. If so, the computing system may reroute the first SIP data to a security deep packet inspection (“DPI”) engine, which may perform a deep scan of the received first SIP data to identify any known fraudulent or malicious attack vectors contained within the received first SIP data. If so, the security DPI engine may initiate mitigation actions.

Abstract: A method including analyzing affected data known to include harmful content, and clean data known to be free of the harmful content; determining, based on analyzing the affected data and the clean data, harmful traits that appear in the affected data with a frequency that satisfies a threshold frequency, and clean traits that appear in the clean data with the frequency that satisfies the threshold frequency; mixing the harmful traits and the clean traits to determine a mixed set; analyzing the affected data based on utilizing the mixed set to determine a harmful pattern that indicates characteristics associated with the harmful traits and the clean traits; and transmitting pattern information indicating the harmful pattern to enable the user device to determine whether given data includes the harmful content is disclosed. Various other aspects are contemplated.

Abstract: The invention relates to a method for improving the security in an electronic communication network, in which lures and decoys are distributed in the communication network. The aim of the invention is that of providing a systemisation for the selection and positioning of lures and decoys, by means of which the lures and decoys are distributed as optimally as possible in the communication network.

Abstract: Testing software applications often requires a balancing of thoroughness versus the time and computing resources available to perform such tests. Certain data handling operations may potentially expose data to unauthorized parties. However, not all data is equal; some data requires a greater degree of protection than other data, which may be based on a security context (e.g., rule, law, policy, etc.). By generating rules determined by a particular context, extraneous tests on data outside of the context, may be omitted. Unnecessary tests may be omitted and the results of each analysis process correlated to identify actual vulnerabilities and omit false positives, such as vulnerabilities to data that does not require the same degree of care to avoid unauthorized exposure.

Abstract: Endpoints in a network execute a sensor module that intercepts commands. The sensor module compares a source of commands to a sanctioned list of applications received from a management server. If the source does not match a sanctioned application and the command is a write or delete command, the command is ignored and a simulated acknowledgment is sent. If the command is a read command, deception data is returned instead. In some embodiments, certain data is protected such that commands will be ignored or modified to refer to deception data where the source is not a sanctioned application. The source may be verified to be a sanctioned application by evaluating a certificate, hash, or path of the source. Responses from an active directory server may be intercepted and modified to reference a decoy server when not addressed to a sanctioned application.

Abstract: A program processing device (100) includes: an entry address masking unit (110) to generate a fake entry address, which is obtained by masking an entry address with the use of a save value copied from a setting value stored in a storage, and to replace an entry address used by an application program with the fake entry address; a memory monitoring unit (111) to determine whether the fake entry address is loaded during execution of the application program; and an entry address unmasking unit (112) to generate a new entry address, which is obtained by loading the setting value from the storage and unmasking the fake entry address with the use of the setting value, and to replace the fake entry address with the new entry address, when the fake entry address is loaded.

Abstract: A comparison means compares a first risk analysis result with a second risk analysis result. The first risk analysis result includes a first risk evaluation value. The second risk analysis result includes a second risk evaluation value. Based on the result of the comparison, a display means displays the first risk evaluation value in such a manner that a first risk evaluation value for which there is a second risk evaluation value, in the second risk analysis result, for an attack step of which an attack destination coincides with an asset included in the first risk analysis result and an attack method coincides with an attack method included in the first risk analysis result can be distinguished from a first risk evaluation value for which there is no such second risk evaluation value.

Abstract: Examples of the present disclosure describe systems and methods for exploit detection via induced exceptions. One embodiment of a method can include generating an inspection point, the inspection point causing an exception when a set of software instructions encounters the inspection point during an execution of the set of software instructions by a processor, registering an exception handler to handle the exception associated with by the inspection point; receiving, in response to the set of software instructions encountering the inspection point, an indication of an exception, accessing a context record associated with the execution of the set of software instructions, evaluating the context record to determine if an exploit is present using the first reputation information, and based on a determination that an exploit is present, performing a corrective action for the exploit.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a request for a communication service from a user equipment, based on the obtaining of the request, assigning a first identifier to the user equipment as part of the communication service, obtaining first data from the user equipment as part of the communication service, analyzing at least the first data in accordance with at least one algorithm to determine that the user equipment is likely misconfigured in an amount that is greater than a threshold, resulting in a determination, based on the determination, mapping the first identifier to a second identifier associated with the user equipment, generating a message based on the mapping, and transmitting the message. Other embodiments are disclosed.

Abstract: The invention provides a device with cryptographic function, which includes: a hardware unit, exhibiting hardware-intrinsic properties; a key generating unit, generating a private key according to the hardware-intrinsic properties, and generating a public key according to the private key, for exchanging public keys with an outside device to convert communication payload information into first encrypted information based on the received public key; and a session operational unit, establishing a session key configured to encrypt the first encrypted information into second encrypted information to be transmitted between the cryptographic device with cryptographic function and the outside device. The key generating unit further optionally generates a secret key according to the hardware-intrinsic properties for securing data at rest in the cryptographic device.

Abstract: In some aspects, a computing system may use a machine learning model to determine whether a computer security policy should be modified to reduce the likelihood of a cyber security incident. Through the use of a machine learning model, unsafe combinations of access grants or permissions may be identified and modified to prevent cyber security incidents from occurring. The computing system may input a representation of a computer security policy into a machine learning model, which has been trained on a dataset that includes representations of computer security policies. The computing system may generate output indicating a likelihood that the first computing system will be involved in a cyber security incident. Based on the output satisfying a first threshold, the computing system may generate a recommendation to modify the first computer security policy. The computing system may modify the first computer security policy based on the recommendation.

Abstract: Data is received that characterizes artefacts associated with each of a plurality of layers of a first machine learning model. Fingerprints are then generated for each of the artefacts in the layers of the first machine learning model. These generated fingerprints collectively form a model indicator for the first machine learning model. It is then determined whether the first machine learning model is derived from another machine learning model by performing a similarity analysis between the model indicator for the first machine learning model and model indicators generated for each of a plurality of reference machine learning models each comprising a respective set of fingerprints. Data characterizing the determination can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Abstract: The disclosed computer-implemented method for enforcing strict network connectivity and storage access during online payments may include (i) determining that a webpage in a tab of a browser application executing on the computing device includes a payment page for an e-commerce website, (ii) based on determining that the webpage includes a payment page, providing formjacking attack protection by monitoring network connectivity and storage access by the browser tab, (iii) based on the formjacking attack protection, identifying a potentially malicious attempt to hijack information entered into at least one web form included in the payment page, and (iv) in response to identifying the potentially malicious attempt, preventing the potentially malicious attempt from hijacking the information entered into the at least one web form included in the payment page. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Various embodiments include systems and methods of implementing automated assessment scheduling. A set of scheduling parameters may be received, including at least a frequency corresponding to how often assessments are to be completed via a particular automated assessment and a type of assessment to perform in the particular automated assessment. Based at least in part on the set of scheduling parameters, an assessment configuration may be generated. The assessment configuration includes a set of attributes defining how the particular automated assessment is to be performed. At least one scan engine resource of a set of scan engine resources may be identified for utilization in the particular automated assessment. Based at least in part on the assessment configuration and using the at least one scan engine resource, the particular automated assessment may be automatically initiated.

Abstract: An integrated circuit (IC) applicable to performing system protection through dynamic voltage change may include a monitoring circuit, at least one power voltage generation circuit and a voltage adjustment circuit. The monitoring circuit monitors at least one security checking result of a security engine to determine whether at least one security event occurs. The at least one power voltage generation circuit generates at least one internal power voltage within the IC according to at least one input voltage received from outside of the IC, to provide the internal power voltage to at least one internal component of the IC. In response to occurrence of the at least one security event, the voltage adjustment circuit controls the at least one power voltage generation circuit to dynamically adjust the at least one internal power voltage, to control the internal power voltage randomly exceed predetermined voltage range thereof, thereby performing the system protection.

Abstract: A method and a system for performing a search in a dataset in a mainframe session are disclosed. The method includes receiving at least one keyword associated with at least one query. The method includes identifying at least one from among at least one step containing the at least one keyword and at least one paragraph containing the at least one keyword based on the at least one query. The method includes tagging at least one identifier to at least one from among the at least one identified step and the at least one identified paragraph. Next, the method includes verifying the at least one tagged identifier. Thereafter, the method includes displaying at least one from among the at least one identified step and the at least one identified paragraph.

Abstract: In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.

Abstract: Providing policy check functionality to file uploads is disclosed. An attempted file upload is detected at a browser isolation system. A user of a client is prompted to provide a credential associated with the file and usable to access contents of the file. A policy is applied to the file upload.

Abstract: A system and computer-implemented method to detect particular Domain Name System (DNS) misuse, wherein the method includes obtaining monitored network data. The monitored network data includes respective instances of request traffic. The request traffic is associated with DNS requests that request resolution of a name that belongs to at least one identified domain. Each DNS request is sent from a source address of one or more stub resolver; the source address of the stub resolver may be spoofed. Each instance of request traffic includes the source address, the name for which DNS resolution is requested to be resolved, and the at least one identified domain associated with a corresponding DNS request. The method further includes tracking over time, using a probabilistic algorithm, an approximation of a first cardinality of names belonging to a selected domain of the at least one identified domain included in the instances of request traffic.