Abstract: A method of securing, by a managing node, communication in a network of member nodes including a first member node. Communication is secured using a (k, n1) secret sharing scheme during a first phase. The managing node announces a phase shift to a subsequent phase relative to the first phase. The managing node secures communication in the network during the subsequent phase using a (k+x, n2) secret sharing scheme. Each of the member nodes is previously in possession of exactly one corresponding unique share for the second key. The first member node is previously in possession of a first unique share for the second key. The managing node deletes the first member node from the subsequent phase by broadcasting the first unique share for the second key.

Abstract: A method of securing, by a managing node, communication in a network of member nodes including a first member node. Communication is secured using a (k, n1) secret sharing scheme during a first phase. The managing node announces a phase shift to a subsequent phase relative to the first phase. The managing node secures communication in the network during the subsequent phase using a (k+x, n2) secret sharing scheme. Each of the member nodes is previously in possession of exactly one corresponding unique share for the second key. The first member node is previously in possession of a first unique share for the second key. The managing node deletes the first member node from the subsequent phase by broadcasting the first unique share for the second key.

Abstract: A method of securing, by a managing node, communication in a network of member nodes including a first member node. Communication is secured using a (k, n1) secret sharing scheme during a first phase. The managing node announces a phase shift to a subsequent phase relative to the first phase. The managing node secures communication in the network during the subsequent phase using a (k+x, n2) secret sharing scheme. Each of the member nodes is previously in possession of exactly one corresponding unique share for the second key. The first member node is previously in possession of a first unique share for the second key. The managing node deletes the first member node from the subsequent phase by broadcasting the first unique share for the second key.

Abstract: A multiple vehicle cyber-attack detection system including a vehicle operations center coupled to and monitoring vehicles, where each operations center includes a cyber-attack alert module. A vehicle cyber-attack detection system is in communication with each operations center and includes a first tier security module coupled to an onboard system of each vehicle and receives system log data generated by the onboard system. The first tier security module extracts extracted features from a time series of overlapping windowed system log data generated from the system log data with natural language processing and includes a first cyber-attack anomaly detection module that identifies a first cyber-attack based on system entropy measurements. A second cyber-attack anomaly detection module includes a deep learning component that identifies a second cyber-attack based on classification of the extracted features.

Abstract: A method of securing, by a managing node, communication in a network of member nodes including a first member node. Communication is secured using a (k, n1) secret sharing scheme during a first phase. The managing node announces a phase shift to a subsequent phase relative to the first phase. The managing node secures communication in the network during the subsequent phase using a (k+x, n2) secret sharing scheme. Each of the member nodes is previously in possession of exactly one corresponding unique share for the second key. The first member node is previously in possession of a first unique share for the second key. The managing node deletes the first member node from the subsequent phase by broadcasting the first unique share for the second key.

Abstract: A multiple vehicle cyber-attack detection system including a vehicle operations center coupled to and monitoring vehicles, where each operations center includes a cyber-attack alert module. A vehicle cyber-attack detection system is in communication with each operations center and includes a first tier security module coupled to an onboard system of each vehicle and receives system log data generated by the onboard system. The first tier security module extracts extracted features from a time series of overlapping windowed system log data generated from the system log data with natural language processing and includes a first cyber-attack anomaly detection module that identifies a first cyber-attack based on system entropy measurements. A second cyber-attack anomaly detection module includes a deep learning component that identifies a second cyber-attack based on classification of the extracted features.

Abstract: A vehicle cyber-attack detection system includes at least one first tier security module coupled to at least one onboard system of a respective vehicle and being configured so as to receive system log data generated by the at least one onboard system of the respective vehicle. The first tier security module extracts extracted features from the system log data with natural language processing and includes a first cyber-attack anomaly detection module that identifies a first cyber-attack based on system entropy measurements determined from the extracted features, and a second cyber-attack anomaly detection module that includes a deep learning component that identifies a second cyber-attack based on classification of the extracted features. A cyber-attack alert module is coupled to the first tier security module for generating a notification of one or more of the first cyber-attack and the second cyber-attack to effect cyber-attack diagnostics and remediation by vehicle personnel.

Abstract: A vehicle cyber-attack detection system includes at least one first tier security module coupled to at least one onboard system of a respective vehicle and being configured so as to receive system log data generated by the at least one onboard system of the respective vehicle. The first tier security module extracts extracted features from the system log data with natural language processing and includes a first cyber-attack anomaly detection module that identifies a first cyber-attack based on system entropy measurements determined from the extracted features, and a second cyber-attack anomaly detection module that includes a deep learning component that identifies a second cyber-attack based on classification of the extracted features. A cyber-attack alert module is coupled to the first tier security module for generating a notification of one or more of the first cyber-attack and the second cyber-attack to effect cyber-attack diagnostics and remediation by vehicle personnel.

Abstract: The present invention relates to biosensors, in particular to surface-enhanced Raman biosensors for detection of in vivo and ex vivo analytes. In particular, the present invention provides compositions and methods for the in vivo detection of analytes such as glucose.

Abstract: The present invention relates to biosensors, in particular to surface-enhanced Raman biosensors for detection of in vivo and ex vivo analytes. In particular, the present invention provides compositions and methods for the in vivo detection of analytes such as glucose.

Abstract: The present invention relates to biosensors, in particular to surface-enhanced Raman biosensors for detection of in vivo and ex vivo analytes. In particular, the present invention provides compositions and methods for the in vivo detection of analytes such as glucose.