Abstract: Disclosed herein are compositions comprising a polypeptide with at least two domains, wherein the first domain is capable of binding CD3 and the second domain is capable of binding to a cancer cell. Also disclosed herein are methods of treating cancer in a subject, comprising: providing a composition comprising a polypeptide with at least two domains, wherein the first domain is capable of binding CD3 and the second domain is capable of binding to a cancer cell; and treating the cancer by administering a therapeutically effective dosage of the composition to the subject.

Abstract: A warehouse management system may receive a predictive analytics request associated with one or more warehouses and may, in response, input data associated with the one or more warehouses into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses, where the warehouse management model is trained via machine learning to determine the predictive analytics. The warehouse management system may perform simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine one or more warehouse actions to meet one or more operational requirements. The warehouse management system may communicate the one or more warehouse actions to one or more devices associated with the one or more warehouses to enable the one or more devices to operate according to the one or more warehouse actions to meet the one or more operational requirements.

Abstract: A method for detecting, identifying, and mitigating advanced persistent threats in a computer network having one or more computers includes a processor in the computer network: receiving a request to access a resource in the computer network; identifying the request as originating from an application executing on the computer network; executing an anomaly operation to determine a behavior of the application is one of anomalous and not anomalous; executing a privilege operation to determine the request is one of permanently allowed and not-permanently allowed; granting access to the resource for both a non-anomalous-behaving application and a permanently allowed request; and generating and displaying, on a graphical user interface of the computer network, and prompt for either an anomalous-behaving application or a not-permanently allowed request.

Abstract: Disclosed herein are embodiments for managing a virtual reality (VR) training exercise via a management server. The management server outputs a graphical dashboard including one or more skill nodes, and selects one or more software agents associated with the skill nodes. The management server provides the software agents to at least one host computing system communicatively coupled to a near-to-eye display device. The near-to-eye display device is configured to display a virtual three dimensional (3D) training environment including a plurality of interactive 3D virtual objects. The software agents are configured to collect VR observables data while the trainee performs actions within the virtual 3D training environment. Based on the VR observables data collected, the management server determines that one or more skills have been demonstrated during the training exercise, and updates the one or more skill nodes to graphically indicate the one or more skills demonstrated by the trainee.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which improves security of a system. The analytic server may monitor the system by retrieving status information from various devices within the system. The analytic server may generate an attack tree model based on a set of aggregation rules that are configured based on the monitored status information. The analytic server may detect one or more attacks by associating the status information with corresponding nodes of the attack tree model and executing a logic of the attack tree model. The analytic server may determine aggregated impact and risk metrics and calculate an impact score for each attack based on aggregated impact and risk metrics. The analytic server may generate reports comprising the one or more attacks ranked based on the impact scores. The analytic server may respond to one or more attacks by taking automated actions.

Abstract: In general, this disclosure describes methods and devices for analyzing source code to detect potential bugs in the code. Specifically, a device retrieves source code of an application. For each distinct execution of a plurality of executions of the application, the device initiates the respective execution at a particular starting point of the source code and inputs, into the source code, a unique set of inputs relative to any other execution. The device stores, into a path log, an indication of each line of source code and stores, into an output log, an indication of each output object encountered during the respective execution. Each output object includes a local variable dependent on the inputs. The device analyzes, using a machine learning model, the path and output logs to identify an abnormality indicative of a potential bug in the source code. The device outputs a graphical representation of the abnormality.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which detects and defends against malware in-flight regardless of the specific nature and methodology of the underlying attack. The analytic server learns the system's normal behavior during testing and evaluation phase and trains a machine-learning model based on the normal behavior. The analytic server monitors the system behavior during runtime comprising the runtime behavior of each sub-system of the system. The analytic server executes the machine-learning model and compares the system runtime behavior with the normal behavior to identify anomalous behavior. The analytic server executes one or more mitigation instructions to mitigate malware. Based on multiple available options for mitigating malware, the analytic server makes an intelligent decision and takes the least impactful action that have the least impact on the system to maintain mission assurance.

Abstract: A method for detecting, identifying, and mitigating advanced persistent threats in a computer network having one or more computers includes a processor in the computer network: receiving a request to access a resource in the computer network; identifying the request as originating from an application executing on the computer network; executing an anomaly operation to determine a behavior of the application is one of anomalous and not anomalous; executing a privilege operation to determine the request is one of permanently allowed and not-permanently allowed; granting access to the resource for both a non-anomalous-behaving application and a permanently allowed request; and generating and displaying, on a graphical user interface of the computer network, and prompt for either an anomalous-behaving application or a not-permanently allowed request.

Abstract: A system having a distributed node hardware and software product is disclosed. The distributed topology allows for multiple GPS receiver node positions. The multiple GPS receiver node positions enable an accurate location estimation of a GPS spoofing signal emitter source of an incoming malicious GPS signal. The system detects the presence of a GPS spoofing signal emitter with high confidence against any spoofing geometry or strategy while the GPS receiver nodes are on the move.

Abstract: In general, this disclosure describes methods and devices for analyzing source code to detect potential bugs in the code. Specifically, a device retrieves source code of an application. For each distinct execution of a plurality of executions of the application, the device initiates the respective execution at a particular starting point of the source code and inputs, into the source code, a unique set of inputs relative to any other execution. The device stores, into a path log, an indication of each line of source code and stores, into an output log, an indication of each output object encountered during the respective execution. Each output object includes a local variable dependent on the inputs. The device analyzes, using a machine learning model, the path and output logs to identify an abnormality indicative of a potential bug in the source code. The device outputs a graphical representation of the abnormality.

Abstract: Techniques for performing data analytics using anomaly detection systems and methods are disclosed. The anomaly detection system provides an incident response and monitoring solution, built for distributed processing, that streamlines cyber defense by unifying datasets, via a data translator, from sensors and tools into a uniform schema to provide real-time anomaly detection, via an anomaly detection system that may prevent malware from establishing a foothold on the network. The anomaly detection system may allow for the scalability to provide large-scale data aggregation and anomaly detection without compromising performance. The anomaly detection system may use a distributed architecture to support advanced cyber threat detection across large datasets in real-time for monitoring and rapid incident response. The anomaly detection system may leverage open protocols and interfaces to promote third-party support for development and interoperability.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which detects and defends against malware in-flight regardless of the specific nature and methodology of the underlying attack. The analytic server learns the system's normal behavior during testing and evaluation phase and trains a machine-learning model based on the normal behavior. The analytic server monitors the system behavior during runtime comprising the runtime behavior of each sub-system of the system. The analytic server executes the machine-learning model and compares the system runtime behavior with the normal behavior to identify anomalous behavior. The analytic server executes one or more mitigation instructions to mitigate malware. Based on multiple available options for mitigating malware, the analytic server makes an intelligent decision and takes the least impactful action that have the least impact on the system to maintain mission assurance.

Abstract: A method for detecting, identifying, and mitigating advanced persistent threats in a computer network having one or more computers includes a processor in the computer network: receiving a request to access a resource in the computer network; identifying the request as originating from an application executing on the computer network; executing an anomaly operation to determine a behavior of the application is one of anomalous and not anomalous; executing a privilege operation to determine the request is one of permanently allowed and not-permanently allowed; granting access to the resource for both a non-anomalous-behaving application and a permanently allowed request; and generating and displaying, on a graphical user interface of the computer network, and prompt for either an anomalous-behaving application or a not-permanently allowed request.