Abstract: In one embodiment, a network security service forms, for each of a plurality of malware classes, a feature vector descriptor for the malware class. The service uses the feature vector descriptors for the malware classes and a symmetric mapping function to generate a training dataset having both positively and negatively labeled feature vectors. The service trains, using the training dataset, an instant threat detector to determine whether telemetry data for a particular traffic flow is within a threshold of similarity to a feature vector descriptor for a new malware class that was not part of the plurality of malware classes.

Abstract: The invention provides a command-and-control (C&C) domain name analysis-based botnet detection method, device, apparatus and medium. The method includes an information acquisition step where DNS logs are acquired; a domain name analysis step where C&C domain names in the DNS logs are detected and the category of each C&C domain name is determined according to a pre-built domain name analyzer; a botnet determination step where whether a botnet exists is determined according to the C&C domain name and the category of C&C domain name. In the C&C domain name analysis-based botnet detection method, device, apparatus and medium provided by the present invention, by analyzing the domain name system (DNS) logs, the C&C domain name used in the attack activity is extracted for further analysis of the types of parasitic Trojans to thereby lock down the bot that the C&C server has controlled.

Abstract: Techniques and apparatuses are described to enable a strategically coordinated fictitious ecosystem of disinformation for cyber threat intelligence collection in a computing network. The ecosystem comprises fictitious profiles and supporting fictitious infrastructure information to portray in-depth, apparent authenticity of the ecosystem. Malicious communications from an adversary directed at the ecosystem are monitored, and threat intelligence about the adversary is collected to prevent future attacks.

Abstract: In some implementations, a method includes obtaining an unlabeled computer security data log and processing the unlabeled computer security data log using a machine learning model to generate a probability distribution that includes a respective probability for each of a plurality of possible log types. Each of the plurality of possible log types is associated with a corresponding parser that parses logs of the possible log type to extract structured computer security data. The method further includes selecting the possible log type having the highest probability and parsing the unlabeled computer security data log using the parser corresponding to the selected possible log type.

Abstract: Presented herein are methodologies to on-board and monitor Internet of Things (IoT) devices on a network. The methodology includes receiving at a server, from a plurality of IoT devices communicating over a network, data representative of external environmental factors being experienced by individual ones of the plurality of IoT devices at a predetermined location; generating, using machine learning, an aggregated model of the external environmental factors at the predetermined location; receiving, at the server, a communication indicative that a new IoT device seeks to join the network at the predetermined location; receiving, from the new IoT device, data representative of external environmental factors being experienced by the new IoT device; determining whether there is a discrepancy between the external environmental factors of the new IoT device and the aggregated model; and when there is such a discrepancy, prohibiting the new IoT device from joining the network.

Abstract: A system and method is provided for detecting anomalous events based on a dump of an address space of a software process in a memory of a computing device. An exemplary method includes detecting at least one event occurring in an operating system of the computing device during an execution of the software process, determining a context of the detected event, wherein the context comprises a dump of an address space of the software process containing code that was being executed at the moment of occurrence of the detected event, selecting a set of features of the dump for use in determining whether or not the event is anomalous, transforming the selected set of features of the dump into a convolution, determining a popularity of the convolution by polling a database, and determining that the detected event is an anomalous event if the determined popularity is below a threshold value.

Abstract: A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including a plurality of training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each of the at least one service, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the at least one service that is not among the discrete behaviors defined in the at least one capability for the service.

Abstract: A digital brand asset system is provided enabling a brand owner to create, distribute, maintain, manage, merchandise and analyze smart brand assets. Generally, the system enables distribution and sharing of smart brand assets across websites. The system performs the steps of presenting a web page containing code representing a smart brand asset that has a unique identifier, receiving a request for the smart brand asset from a search engine crawler which is indexing web pages of the web server, redirecting the request to a brand asset proxy server based on the unique identifier and satisfying the request by providing content of the smart brand asset. The unique identifier can include information of the location, user attributes, or the content of the smart brand asset. As a result, it is determined that the request is sufficiently satisfied to be indexed by the search engine.

Abstract: At least some embodiments are directed to a computer-based cyber-attack frequency tracking system that determines types and frequencies of cyber-attacks. In at least some embodiments, the method of a cyber-attack frequency tracking system may operate a processor in an enterprise computing environment for automatically conducting a process that comprises receiving, a plurality of data values that represent a plurality of cyber-attacks. Determining cyber-attack types, and then determining the frequency of attempts and contacts with assets. After that determining likelihood values. Aggregating these determinations to produce a quantifiable value of a likelihood values of each of the plurality of cyber-attack types.

Abstract: Techniques and mechanisms are disclosed that enable network security analysts and other users to efficiently conduct network security investigations and to produce useful representations of investigation results. As used herein, a network security investigation generally refers to an analysis by an analyst (or team of analysts) of one or more detected network events that may pose internal and/or external threats to a computer network under management. A network security application provides various interfaces that enable users to create investigation timelines, where the investigation timelines display a collection of events related to a particular network security investigation. A network security application further provides functionality to monitor and log user interactions with the network security application, where particular logged user interactions may also be added to one or more investigation timelines.

Abstract: Embodiments described herein provide systems, methods, and computer storage media for detecting spam using by comparing hash values of content. In embodiments, hash values are generated based on the type of content and compared to other hash values in storage buckets. The similarity of content is determined by calculating the distance between two hash values and determining whether the distance exceeds a distance index. Counter values associated with hash values in storage are incremented when the distances between hash values exceed the distance index. Spam indications are communicated when the counter values for associated with hash values exceed a count threshold.

Abstract: Examples disclosed herein relate to using an integrity manifest certificate to verify the state of a platform. A device identity of a device that has the device identity provisioned and stored in a security co-processor to retrieve an integrity proof from the security co-processor. The device includes at least one processing element, at least one memory device, and a bus including at least one bus device, and wherein the device identity is associated with a device identity certificate signed by a first authority. The integrity proof includes a representation of each of a plurality of hardware components including the at least one processing element, the at least one memory device, the at least one bus device, and a system board and a representation of plurality of firmware components included in the device. The integrity proof is provided to a certification station.

Abstract: An analysis apparatus includes a category classification unit that accesses a URL of an analysis target Web page and classifies the analysis target Web page into a category, an operation target detection unit that detects an operation target of user operation from the analysis target Web page in accordance with a detection method that is set in advance for the classified category, an operation execution unit that performs operation on the detected operation target, a function hook unit that detects an operation event that occurs after the operation has been performed on the operation target, and a log output unit that outputs log data in which communication that has occurred due to the operation, the detected operation event, and a Web browser screen that has been changed due to the operation are associated with the URL of the analysis target Web page.

Abstract: A method and system for mitigating a malware attack are disclosed herein. A malware detection module iterates over a virtual memory address space associated with a process executing on a computer system. The malware detection module identifies a region of memory likely to be vulnerable to a malware attack. Responsive to identifying the region of memory, a thread hollowing module determines a specific process thread associated with the identified region of memory. The thread hollowing module renders the specific process thread inoperable.

Abstract: A log acquirer acquires an analysis communication log and a malicious communication log. A signature generator generates a signature serving as a condition for detecting a terminal infected with malware based on a field and a value included in the malicious communication log. A malware analysis report acquirer acquires information on the malware. A malware information adder adds the information on the malware to the signature. A log analyzer analyzes the analysis communication log using the signature and detects the terminal infected with the malware. A detection result display unit displays the detection result obtained from the analysis communication log by the log analyzer and the information on the malware added to the signature used in the analysis of the analysis communication log in a manner associated with each other.

Abstract: Methods and systems for detecting malicious processes. Methods described herein gather data regarding process locations and calculate one or more inequality indicators related to the process paths based on economic principles. Instances of inequality with respect to process paths may indicate a path is uncommon and therefore the associated binary is used for malicious purposes.

Abstract: The systems and methods discussed herein provide for faster communications, particularly for high priority traffic, across a distributed network with multiple exit points to a Wide Area Network. Rather than simply routing traffic based on internal or external destination, an intelligent router may measure latency to an endpoint destination via multiple paths, both external and internal, and direct traffic accordingly. Steering high priority traffic via the internal connection to an exit point near the destination server, and then to the server via the external network, may be faster than simply forwarding the connection via the external network from the exit point closest to the source device. Additionally, to reduce bandwidth requirements of the nearby exit point and provide capability for higher priority traffic, low priority traffic may be redirected back via the internal connection and transmitted via a distant exit point.

Abstract: Embodiments of the present invention provide a system and methods to prevent poisoning attacks in machine learning systems in real time. The invention includes methods for blocking the injection of abnormal data into training data sets used to train machine learning models for the identification of malfeasant activity by blocking certain data from entering the machine learning training dataset in real time, blocking certain interactions from being completed in real time, or placing holds on certain resources or users according to patterns detected by the ensemble of machine learning models. Various thresholds may be set manually or identified through the machine learning algorithm in order to determine which interactions or users should be blocked.

Abstract: A method performed on a processor to determine a probability of success of a cyber-attack on a target network such that the defenses of the target network may be evaluated is provided. The method includes (1) calculating a probability that the cyber-attack will successfully ingress to the target network; (2) calculating a probability that the cyber-attack will successfully move laterally in the target network by performing an action; (3) calculating a probability that the cyber-attack will successfully perform an action on objective. The calculated probabilities are combined to determine a probability that the cyber-attack will be successful such that the defenses of the target network may be evaluated.

Abstract: A threat intelligence gateway (TIG) may protect TCP/IP networks from network (e.g., Internet) threats by enforcing certain policies on in-transit packets that are crossing network boundaries. The policies may be composed of packet filtering rules with packet-matching criteria derived from cyber threat intelligence (CTI) associated with Internet threats. These CTI-derived packet-filtering rules may be created offline by policy creation and management servers, which may distribute the policies to subscribing TIGs that subsequently enforce the policies on in-transit packets. Each packet filtering rule may specify a disposition that may be applied to a matching in-transit packet, such as deny/block/drop the in-transit packet or pass/allow/forward the in-transit packet, and also may specify directives that may be applied to a matching in-transit packet, such as log, capture, spoof-tcp-rst, etc.

Abstract: Systems, methods, and related technologies including media access control (MAC) address spoofing detection are described. The MAC address spoofing detection and response may include accessing a first MAC address associated with a first communication on a first port of a first network device and accessing a second MAC address associated with a second communication on a second port of a second network device. Whether the first MAC address and the second MAC address match may be determined. Information associated with a third communication associated with the first MAC address on the first port of the first network device and information associated with a fourth communication associated with the second MAC address on the second port of the second network device may be accessed. An action may be performed associated with the second port of the second network device based on the second MAC address matching the first MAC address.

Abstract: Systems and methods for providing an integrated or Smart NIC-based hardware accelerator for a network security device to facilitate identification and mitigation of DoS attacks is provided. According to one embodiment, a processor of a network security device receives an application layer protocol request from a client, directed to a domain hosted by various servers and protected by the network security device. The application layer protocol request is parsed to extract a domain name and a path string. The hardware acceleration sub-system updates rate-based counters based on the application layer protocol request by performing a longest prefix match on the domain name and the path string. When a rate threshold associated with the rate-based counters is exceeded, a challenge message is created and transmitted to the client, having embedded therein the application layer protocol request; otherwise the application layer protocol request is allowed to pass through the network security device.

Abstract: A method for detecting and defending against abnormal traffic of an in-vehicle network based on information entropy, including the following steps: step 1: setting a sliding window; step 2: setting a threshold; step 3: collecting and processing traffic; step 4: calculating information entropy in the sliding window when the window is full; and step 5: detecting traffic of a controller area network (CAN) bus and an in-vehicle Ethernet. Based on impact of abnormal traffic on the information entropy, the information entropy in the sliding window is calculated in real time and compared with the preset thresholds, to detect whether an abnormality occurs.

Abstract: An inventory of Internet-facing assets related to a username within a social media site is generated using network data gathered from network data sources. Using data sources of known threats, such as malware, phishing attempts, scam pages, blacklisted sites, and so on, a network analytic system generates analytical information about components that are owned, managed, and/or controlled by a target entity. A measure of identity threat is generated based on a classification model using the analytical information.

Abstract: Systems, methods, and software can be used to detect remote application profiling. In some aspects, one computer-implemented method includes receiving, over a network, a request from a network client directed to a particular application executed by an application server; determining whether the received request deviates from a communications profile associated with the particular application; in response to determining that the received request deviates from the communications profile, identifying the network client as an attacker; and in response to identifying the network client as an attacker, performing a defensive response with respect to the network client.

Abstract: A method includes, at a server in a network, detecting for a user device network incidents relating to one or more security threats in the network using a plurality of threat detectors over a predetermined time period, each of the network incidents including one or more behavior indicators; assigning the network incidents into one or more groups, wherein each group corresponds to a type of security threat; generating a graph for a particular group of the user device, wherein the graph includes a plurality of nodes each representing a behavior indicator in the particular group, and wherein generating the graph includes assigning an edge to connect two nodes of the plurality of nodes if the two nodes correspond to behavior indicators that belong to a same network incident; and displaying the graph on a graphical user interface for a user.

Abstract: A method of operating a communications network is disclosed. In order to manage a network, it is first necessary to establish the state the network is in. This is difficult in practice because the network operational data stored and transmitted in the network takes a myriad of forms owing to the variety of suppliers and types of network equipment. There is a need to distil that network operational data down to aggregate network operational data which can be taken to provide an indication of the state of the network which is of a manageable size, and to which network management apparatus can react by sending control commands to the network. The problem of generating aggregate network operational data is tackled by identifying the type of each attribute found in each network operational data item, and classifying the network operational data items in a manner which takes account of the identified types and thus provides network aggregate data which more accurately reflects the operational state of the network.

Abstract: The present disclosure relates to methods, systems, and computer program products for generating an attack kill chain for threat analysis. The method comprises receiving a first security event captured by a first security operation associated with a computing device, and receiving a second security event captured by a second security operation associated with the computing device. The first security event and the second security event are associated with an attack campaign. The method further comprises mapping the first security event to first security data in an attack repository, and mapping the second security event to second security data in the attack repository. The method also comprises determining based on the mapping, one or more attack execution operations for executing the attack campaign associated with the first security event and the second security event. Additionally, the method sequences the one or more attack execution operations to form an attack kill chain.

Abstract: The invention relates to a network segmentation effectiveness attestation system and method. The method may comprise receiving a list of internet protocol (IP) addresses for information technology (IT) assets within a defined scope, and executing a plurality of segmentation scans from outside a cardholder data environment (CDE) using a plurality of software agents. The software agents may be deployed and orchestrated across multiple network tiers. The method may also comprise receiving, automatically interpreting, and certifying results from the segmentation scan, automatically generating a report from the results of the segmentation scan, and automatically posting the report for authorized users to access.

Abstract: A real-time detection method and apparatus for DGA domain name. An original domain name is translated into a multi-dimensional numeric vector, the multi-dimensional numeric vector is input into a deep learning model pre-trained based on an ImageNet data set, to generate a domain name feature, a domain name classifier is trained based on the generated domain name feature, and a DGA domain name is classified and predicted based on the domain name classifier obtained by training. The method firstly uses a deep learning model pre-trained based on an ImageNet data set, from the field of visual image classification and detection, for real-time detection of a DGA domain name, avoiding the process of high-intensity training and parameter weight adjustment for the deep learning model in DGA domain name detection. The detection rate is higher, and detection speed is faster.

Abstract: A system and method securing an in-vehicle network in a vehicle may include a switch connected to at least two segments of the in-vehicle network and an IDPS connected to the switch. The IDPS unit may be adapted to: receive network messages from the switch; determine at least some of the network messages are related to a cyber threat and configure the switch according to the cyber threat. The IDPS unit may be included in the switch.

Abstract: Systems, computer program products, and methods are described herein for scalable encryption framework using virtualization and adaptive sampling. The present invention is configured to receive metadata associated with one or more intrusion types from an intrusion data lake; initiate an adaptive instance sampling engine on the metadata associated with the one or more intrusion types to generate a sampled intrusion data lake; initiate one or more simulations of atomic intrusion on a firewall; generate one or more prioritized combination of the one or more sampled intrusion types; initiate one or more simulations of cumulative intrusion on the firewall using the one or more prioritized combination of the one or more sampled intrusion types; determine an atomic performance metric and a cumulative performance metric of the firewall; and generate a robustness report for the firewall.

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.

Abstract: Detecting, mitigating and isolating a Signaling Storm, particularly in 5G communication networks. A Control Plane signal probe is connected at a first network node located between a Radio Access Network and a 5G Core Network, to monitor control messages originating from 5G-capable devices. A User Plane signal probe is connected at a second network node located between the 5G Core Network and remote entities to which the 5G-capable devices are sending messages, to monitor control messages passing through the second network node. An Inventory Management sub-system stores data correlating between 5G-capable devices and IMSI numbers. A Protector Unit is configured to receive (i) data collected by the Control Plane signal probe, and (ii) data collected by the User Plane signal probe, and (iii) a subset of IMSI numbers. The Protector Unit performs Machine Learning analysis, and detects and quarantines particular 5G-capable devices that are compromised or malfunctioning.

Abstract: Techniques for malicious HTTP cookies detection and clustering are disclosed. In some embodiments, a system, process, and/or computer program product for malicious HTTP cookies detection and clustering includes receiving a sample at a cloud security service; extracting a cookie from network traffic associated with the sample; determining that the cookie is associated with malware; and generating a signature based on the cookie.

Abstract: Entity group behavior profiling. An entity group is created that includes multiple entities, where each entity represents one of a user, a machine, and a service. A behavior profile is created for each one of the entities of the entity group. The behavior of each of one of the entities of the entity group is monitored to detect behavior change. An indicator of compromise is detected based on multiple ones of the entities experiencing substantially a same behavior change.

Abstract: A computer-implemented method, computer program product and computing system for: establishing connectivity with a plurality of security-relevant subsystems within a computing platform; and mapping one or more data fields of a unified platform to one or more data fields of each of the plurality of security-relevant subsystems.

Abstract: The disclosed embodiments provide a method and system for processing network data. During operation, the system obtains one or more event streams from one or more remote capture agents over one or more networks, wherein the one or more event streams include event data generated from network packets captured by the one or more remote capture agents. Next, the system applies one or more transformations to the one or more event streams to obtain transformed event data from the event data. The system then enables querying of the transformed event data.

Abstract: According to one aspect of the present invention, an information processing apparatus includes a determination unit that determines whether an identifier extracted from a one-way communication packet received from a sensor includes a first value indicating another information processing apparatus as a legitimate destination, a second value indicating a user different from a user of the information processing apparatus as the legitimate destination, or a third value indicating a sensor different from a sensor associated with the information processing apparatus as an origination, and a transmission unit that transmits, to a server, a second packet in which the first, the second, or the third value is stored, if the identifier includes the first, the second, or the third value.

Abstract: A system, method, and computer-readable medium are disclosed for performing a security operation. The security operation includes: monitoring an entity, the monitoring observing at least one electronically-observable data source; deriving an observable based upon the monitoring of the electronically-observable data source; identifying a security related activity of the entity, the security related activity being based upon the observable derived from the electronic data source, the security related activity being of analytic utility; associating the security related activity with a component of a cyber kill chain; and, performing a security operation on the security related activity via a security system, the security operation disrupting performance of the component of the cyber kill chain by affecting performance of the security related activity by the entity.

Abstract: A threat intelligence gateway (TIG) may protect TCP/IP networks from network (e.g., Internet) threats by enforcing certain policies on in-transit packets that are crossing network boundaries. The policies may be composed of packet filtering rules with packet-matching criteria derived from cyber threat intelligence (CTI) associated with Internet threats. These CTI-derived packet-filtering rules may be created offline by policy creation and management servers, which may distribute the policies to subscribing TIGs that subsequently enforce the policies on in-transit packets. Each packet filtering rule may specify a disposition that may be applied to a matching in-transit packet, such as deny/block/drop the in-transit packet or pass/allow/forward the in-transit packet, and also may specify directives that may be applied to a matching in-transit packet, such as log, capture, spoof-tcp-rst, etc.

Abstract: A solid state drive (SSD) enabled to process and store block addressable and byte addressable data, includes a first storage region for storing byte addressable data, a second storage region for storing block addressable data, and an SSD controller coupled to the first storage region and the second storage region by a bus. The SSD controller includes a processor and an interface for receiving data packets from a host. The SSD controller receives a data packet from the host at the interface, determines whether the data packet includes byte addressable data or block addressable data at the processor, selects either the first storage region or the second storage region based on the determination, and stores the data associated with the data packet in the selected storage region.

Abstract: Peripherals may be dynamically provisioned to containers. A peripheral arbitrator may be run in the host operating system environment to detect when containers are started. When a container is started, the peripheral arbitrator may determine which peripherals an application running in the container may use or require. The peripheral arbitrator may then identify any available peripherals matching the application's peripheral requirements and provision the peripherals to the container to thereby make the peripherals accessible to the application. In some instances, the peripheral arbitrator may use a trust score to determine whether to provision available peripherals to a container.

Abstract: A system and method for accelerating a cybersecurity event detection and remediation includes extracting corpora of feature data from a suspicious electronic communication, wherein the corpora of feature data comprise at least one corpus of text data extracted from a body of the suspicious electronic communication; computing at least one text embedding value for the suspicious electronic communication; evaluating the text embedding values of the corpus of text data against an n-dimensional mapping of adverse electronic communication vectors, the n-dimensional mapping comprising a plurality of historical electronic communication vectors derived for a plurality of historical electronic communications; identifying whether the suspicious electronic communication comprises one of an adverse electronic communication based on the evaluation of the text embedding value, and accelerating a cybersecurity event detection by routing data associated with the suspicious electronic communication to one of a plurality of dis

Abstract: Disclosed is an electronic device including: an input unit including buttons; a plurality of sensors; and a controller configured to generate at least one authentication information based on at least some of the plurality of sensors, calculate a final security level score based on a security level score corresponding to the at least one authentication information, and determine whether a target service or a target external device is accessible, depending on the final security level score, wherein the security level score is set differently based on a type of the at least one authentication information. Accordingly, it is possible to easily access the target service or the target external device through multi-factor authentication.

Abstract: A method and system for classifying malicious locators where a processor is trained on a set of known malicious locators using a non-supervised learning procedure. Once trained, the processor may classify new locators as being generated by a particular generation kit.

Abstract: This disclosure describes techniques for calculating a vulnerability score for a malicious threat based on Indicator of Compromise (IoC) metadata retrieved from a computing device or underlying network. Further, an Indicator of Compromise (IoC) Calculation (IoC-C) system is described that may monitor a client interaction on a computing device, and further identify IoC metadata that may relate to a malicious threat. The IoC-C system may further generate a vulnerability score that numerically quantifies a risk that the malicious threat poses to the computing device or underlying network. The vulnerability score may account for environmental criteria that mitigate an effect of the malicious threat. The IoC-C system may also generate a reporting data packet that includes an informational message identifying a potential risk posed by a malicious threat, or a response protocol that dynamically prevents, mitigates or quarantines an effect of the malicious threat on a computing device or underlying network.

Abstract: An anomaly service receives log data from nodes in a computing environment, which includes a sequence of information indicative of log messages produced by the nodes. The anomaly service identifies dominant patterns in the sequence of information that are representative of non-anomalous blocks of the log messages. Having identified the dominant patterns, the service is able to extract the non-anomalous blocks from the log data to reveal anomalous blocks that do not fit the dominant patterns. The service may then generate anomaly vectors based on the anomalous blocks, which can be distributed to the nodes to detect anomalies.

Abstract: Systems and methods for detection of attacks on a communication authentication layer of an in-vehicle network, including determining, by at least one network node, at least one attack attempt on the communication authentication layer of the in-vehicle network, wherein the determination is carried out by identifying anomalies in at least one of messages, data and metadata directed to the communication authentication layer, and selecting, by the at least one network node, a response corresponding to the determined attack attempt from at least one of modification of parameter values corresponding to a security protocol, a failsafe response, and rejection of messages identified as anomalies.