Abstract: A system and method for train control system intrusion detection that uses Machine Learning (ML) to detect attacks on traction and braking operations performed by a TCMS. Control message history, which includes previously generated operational commands and control messages sent to each train and mobility information for each train at predetermined time intervals, is received. The received input data is checked for misbehavior and detect attacks.

Abstract: A cloud communication architecture addresses shortcomings of traditional security protocols (e.g., SSL/TLS) in cloud computing, providing security for data-in-transit and authenticity of cloud users (CUs) and cloud service providers (CSPs). The architecture also protects the communication channel against attacks such as man-in-the-middle (MITM) (including eavesdropping, sniffing, identity spoofing, data tampering), sensitive information disclosure, replay, compromised-key, repudiation and session hijacking attacks. The architecture includes a high-performance cloud-focussed security protocol. The protocol efficiently utilizes the strength and speed of features such as symmetric block encryption with Galois/Counter mode (GCM), cryptographic hash, public key cryptography, and ephemeral key-exchange, and provides faster reconnection facility for supporting frequent connectivity and dealing with connection trade-offs. Embodiments have enhanced security against the above-noted attacks, and are superior to TLSv1.

Abstract: A method for a testing system to perform fuzzy testing of a software system, wherein the software system comprises a plurality of callable units and is arranged to receive input for the software system to process, the method comprising: determining, for each callable unit of the plurality of callable units, based on one or more security vulnerability metrics, a target number of times that callable unit is to be tested; initializing a ranked plurality of queues, each queue for storing one or more seeds, said initializing comprising storing one or more initial seeds in a corresponding queue of the ranked plurality of queues; performing a sequence of tests, wherein performing each test comprises: obtaining a seed from the highest ranked non-empty queue; performing a mutation process on the obtained seed to generate a test seed, wherein the mutation process is configured, at least in part, by mutation guidance information; providing the test seed as input to the software system for the software system to process;

Abstract: A system and method for train control system intrusion detection that uses Machine Learning (ML) to detect attacks on traction and braking operations performed by a TCMS. Control message history, which includes previously generated operational commands and control messages sent to each train and mobility information for each train at predetermined time intervals, is received. The received input data is checked for misbehavior and detect attacks.

Abstract: The disclosure is directed to a method, system and a computer readable medium of fuzzy testing a software system, using a grey-box fuzzy testing framework that optimizes the vulnerability exposure process while addressing security testing challenges. The grey-box fuzzy testing framework, unlike white-box testing, provides a focused and efficient assessment of a software system without analyzing each line of code. The disclosed embodiments provide a robust security mechanism that accumulates information about the system without increasing testing complexity, enabling fast and efficient security testing. The disclosed embodiments use security vulnerability metrics designed to identify vulnerable components in the software systems and ensures thorough testing of these components by assigning weights. A mutation engine may perform small data type mutations at the input's high-level design.

Abstract: A cloud communication architecture addresses shortcomings of traditional security protocols (e.g., SSL/TLS) in cloud computing, providing security for data-in-transit and authenticity of cloud users (CUs) and cloud service providers (CSPs). The architecture also protects the communication channel against attacks such as man-in-the-middle (MITM) (including eavesdropping, sniffing, identity spoofing, data tampering), sensitive information disclosure, replay, compromised-key, repudiation and session hijacking attacks. The architecture includes a high-performance cloud-focussed security protocol. The protocol efficiently utilizes the strength and speed of features such as symmetric block encryption with Galois/Counter mode (GCM), cryptographic hash, public key cryptography, and ephemeral key-exchange, and provides faster reconnection facility for supporting frequent connectivity and dealing with connection trade-offs. Embodiments have enhanced security against the above-noted attacks, and are superior to TLSv1.