Abstract: A system and method for detecting malicious hijack events in real-time is provided. The method may include receiving routing data associated with a Border Gateway Protocol (BGP) event from at least one BGP router. The method may further include generating a hijack detection model using a machine learning technique, such as Positive Unlabeled learning. The machine learning technique may include at least one data input and a probability output; wherein, the data input couples to receive a set of historically confirmed BGP hijacking data and the routing data, while the probability output transmits a probability value for the malicious event which may be calculated based upon the data input. Finally, the method may include classifying the BGP event as a malicious event or a benign event using the BGP hijack model and correcting routing tables that have been corrupted by a malicious event.

Abstract: The disclosed computer-implemented method for evaluating infection risks based on profiled user behaviors may include (1) collecting user-behavior profiles that may include labeled profiles (e.g., infected profiles and/or clean profiles) and/or unlabeled profiles, (2) training a classification model to distinguish infected profiles from clean profiles using features and labels of the user-behavior profiles, and (3) using the classification model to predict (a) a likelihood that a computing system of a user will become infected based on a profile of user behaviors of the user and/or (b) a likelihood that a user behavior in the user-behavior profiles will result in a computing-system infection. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Securing compromised network devices in a network. In one embodiment, a method may include (a) identifying a Positive Unlabeled (PU) machine learning classifier, (b) selecting labeled positive samples and unlabeled positive and negative samples as a bootstrap subset of training data from a set of training data, (c) training the PU machine learning classifier, (d) repeating (a)-(c) one or more times to create a set of trained PU machine learning classifiers, (e) predicting probabilities that a network device in a network has been compromised using each of the trained PU machine learning classifiers, (f) combining the probabilities predicted at (e) to generate a combined risk score for the network device, (g) repeating (e)-(f) one or more times to create a ranked list of combined risk scores, and (h) performing a security action on one or more of the network devices in the ranked list.

Abstract: A system and method for detecting malicious hijack events in real-time is provided. The method may include receiving routing data associated with a Border Gateway Protocol (BGP) event from at least one BGP router. The method may further include processing the routing data to generate a list of features representing ownership and various other details relating to origin and upstream equipment. The method may further include generating a hijack detection model using the routing data and the list of features, where a machine learning technique, such as Positive Unlabeled learning technique is employed. The machine learning technique may include at least one data input and a probability output; wherein, the data input couples to receive a set of historically confirmed BGP hijacking data and the routing data, while the probability output transmits a probability value for the malicious event which may be calculated based upon the data input.

Abstract: A computer-implemented method for detecting security events may include (1) identifying facets of candidate security events detected by a network security system, (2) assigning each of the facets of the candidate security events to one of multiple groups of facets to create permutations of the facets, (3) comparing, for each group of facets, the candidate security events according to a similarity algorithm that indicates similarity between the candidate security events, (4) generating, for each group of facets, a weak classifier for detecting security events based on a nearest neighbor graph, and (5) performing, by the network security system, a remedial action in response to classifying a candidate security event as a security threat by applying a combination of the weak classifiers for the groups of facets to the candidate security event. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Embodiments of the present invention disclose an image processing method and apparatus, and a computer device. The image processing method includes: receiving information sent by a central processing unit (CPU), about a scene within a preset range around a to-be-rendered target object; rendering the received scene to obtain scene depth parameters, where the scene is obtained through shooting by a camera located at a ray light source; rendering the to-be-rendered target object to obtain rendering depth parameters, where the to-be-rendered target object is obtained through shooting by a camera not located at a ray light source; calculating ambient occlusion (AO) maps of the to-be-rendered target object in directions of ray light sources according to the scene depth parameters and the rendering depth parameters; and overlaying the AO maps in the directions of the ray light sources, to obtain an output image.

Abstract: Systems and methods for a slider and suspension assembly are discussed. The slider and suspension assembly comprises a slider, a suspension, and composite fiber solder. The composite fiber solder is coupled to the suspension and coupled to the slider. The composite fiber solder comprises a fiber, wherein the fiber is configured to increase a fracture resistance of the composite fiber solder compared to a solder without the fiber.

Abstract: A method and system of controlling contamination within an HDD with a surface modified MCM 41/48 material. The method includes encasing the hard disk drive and a mesoporous material in an enclosure of the hard disk drive, wherein the mesoporous material sorbs contaminants within said enclosure.

Abstract: A method and system of controlling contamination within an HDD with a surface modified MCM 41/48 material. The method includes encasing the hard disk drive and a mesoporous material in an enclosure of the hard disk drive, wherein the mesoporous material sorbs contaminants within said enclosure.

Abstract: Systems and methods for a slider and suspension assembly are discussed. The slider and suspension assembly comprises a slider, a suspension, and composite fiber solder. The composite fiber solder is coupled to the suspension and coupled to the slider. The composite fiber solder comprises a fiber, wherein the fiber is configured to increase a fracture resistance of the composite fiber solder compared to a solder without the fiber.