Abstract: A system and method for testing system vulnerabilities are provided. The method may include: training, by a processor, a machine learning model and agent to generate a payload to test vulnerabilities in the application by: selecting one or more input data from the action space to form an action data; electronically providing the action data as an input to the application; monitoring utilization of one or more system resources in response to the action data being inputted to the application; determining a score of utilization based on a result of the monitoring; determining a corresponding reward score for the action data based on the score of utilization; and identifying one or more of the action data to form a pool of candidate data for the application.

Abstract: Computer implemented methods and systems are provided for generating one or more test cases based on received one or more natural language strings. An example system comprises a natural language classification unit that utilizes a trained neural network in conjunction with a reinforcement learning model, the system receiving as inputs various natural language strings and providing as outputs mapped test actions, mapped by the neural network.

Abstract: There is provided a neural network system for detection of domain generation algorithm generated domain names, the neural network system comprising: an input receiver configured for receiving domain names from one or more input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are DGA-generated or benign domain names.

Abstract: There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.

Abstract: A system and method for testing system vulnerabilities are provided. The method may include: training, by a processor, a machine learning model and agent to generate a payload to test vulnerabilities in the application by: selecting one or more input data from the action space to form an action data; electronically providing the action data as an input to the application; monitoring utilization of one or more system resources in response to the action data being inputted to the application; determining a score of utilization based on a result of the monitoring; determining a corresponding reward score for the action data based on the score of utilization; and identifying one or more of the action data to form a pool of candidate data for the application.

Abstract: Computer implemented methods and systems are provided for generating one or more test cases based on received one or more natural language strings. An example system comprises a natural language classification unit that utilizes a trained neural network in conjunction with a reinforcement learning model, the system receiving as inputs various natural language strings and providing as outputs mapped test actions, mapped by the neural network.

Abstract: There is provided a neural network system for detection of domain generation algorithm generated domain names, the neural network system comprising: an input receiver configured for receiving domain names from one or more input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are DGA-generated or benign domain names.

Abstract: There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.