Abstract:
A computer-implemented method for analyzing operations of privilege changes is presented. The computer-implemented method includes inputting a program and performing source code analysis on the program by generating a privilege control flow graph (PCFG), generating a privilege data flow graph (PDFG), and generating a privilege call context graph (PCCG). The computer-implemented method further includes, based on the source code analysis results, instrumenting the program to perform inspections on execution states at privilege change operations, and performing runtime inspection and anomaly prevention.
Abstract:
Methods and systems for security analysis include determining whether a process has an origin internal to a system or external to the system using a processor based on monitored behavior events associated with the process. A security analysis is performed on only processes that have an external origin to determine if any of the processes having an external origin represent a security threat. A security action is performed if a process having an external origin is determined to represent a security threat.
Abstract:
Methods and systems for detecting security intrusions include detecting alerts in monitored system data. Temporal dependencies are determined between the alerts based on a prefix tree formed from the detected alerts. Content dependencies between the alerts are determined based on a distance between alerts in a graph representation of the detected alerts. The alerts are ranked based on an optimization problem that includes the temporal dependencies and the content dependencies. A security management action is performed based on the ranked alerts.
Abstract:
A system and computer-implemented method are provided for host level detection of malicious Domain Name System (DNS) activities in a network environment having multiple end-hosts. The system includes a set of DNS resolver agents configured to (i) gather DNS activities from each of the multiple end-hosts by recording DNS queries and DNS responses corresponding to the DNS queries, and (ii) associate the DNS activities with Program Identifiers (PIDs) that identify programs that issued the DNS queries. The system further includes a backend server configured to detect one or more of the malicious DNS activities based on the gathered DNS activities and the PIDs.
Abstract:
Methods are provided for both single modal and multimodal fault diagnosis. In a method, a fault fingerprint is constructed based on a fault event using an invariant model. A similarity matrix between the fault fingerprint and one or more historical representative fingerprints are derived using dynamic time warping and at least one convolution. A feature vector in a feature subspace for the fault fingerprint is generated. The feature vector includes at least one status of at least one system component during the fault event. A corrective action correlated to the fault fingerprint is determined. The corrective action is initiated on a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.
Abstract:
Systems and methods for identifying similarities in program binaries, including extracting program binary features from one or more input program binaries to generate corresponding hybrid features. The hybrid features include a reference feature, a resource feature, an abstract control flow feature, and a structural feature. Combinations of a plurality of pairs of binaries are generated from the extracted hybrid features, and a similarity score is determined for each of the pairs of binaries. A hybrid difference score is generated based on the similarity score for each of the binaries combined with input hybrid feature parameters. A likelihood of malware in the input program is identified based on the hybrid difference score.
Abstract:
Methods for system failure prediction include clustering log files according to structural log patterns. Feature representations of the log files are determined based on the log clusters. A likelihood of a system failure is determined based on the feature representations using a neural network. An automatic system control action is performed if the likelihood of system failure exceeds a threshold.
Abstract:
Methods and systems for process constraint include collecting system call information for a process. It is detected whether the process is idle based on the system call information and then whether the process is repeating using autocorrelation to determine whether the process issues system calls in a periodic fashion. The process is constrained if it is idle or repeating to limit an attack surface presented by the process.
Abstract:
Systems and methods are provided for optimizing system output in production systems, comprising. The method includes separating, by a processor, one or more initial input variables into a plurality of output variables, the output variables including environmental variables and system response variables. The method also includes building, using the processor, a nonparametric estimation that determines a relationship between one or more initial control variables and the system response variables, and estimating a global input-output mapping function, using the determined relationship, and a range of the environmental variables. The method further includes generating one or more optimal control variables from the initial control variables by maximizing the input-output mapping function and the range of the environmental variables. The method additionally includes incorporating one or more of the optimal control variables into a production system to increase production output of the production system.
Abstract:
The present invention enables capturing API level calls using a combination of dynamic instrumentation and library overriding. The invention allows event level tracing of API function calls and returns, and is able to generate an execution trace. The instrumentation is lightweight and relies on dynamic library/shared library linking mechanisms in most operating systems. Hence we need no source code modification or binary injection. The tool can be used to capture parameter values, and return values, which can be used to correlate traces across API function calls to generate transaction flow logic.