Abstract: An approach is provided that trains an artificial intelligence model (AIM) using training data to produce a generalized AIM, wherein the training data comprises log-collected data corresponding to multiple application types and the generalized AIM is trained to detect one or more cross-platform cybersecurity threats. The approach identifies multiple application-specific training data sets, wherein each one of the application-specific training data sets includes labeled application logs corresponding to one of the multiple application types. The approach then fine-tunes the generalized AIM using the multiple application-specific training data sets to produce multiple dedicated AIMs, wherein each one of the dedicated AIMs is trained to detect one or more application-centric cybersecurity threats targeted at a corresponding one of the application types.

Abstract: An approach is provided that identifies a vulnerability corresponding to an initial source code. Then, the approach generates a prompt comprising the initial source code and the vulnerability. The approach inputs the prompt into an artificial intelligence model (AIM) that is trained to determine whether the initial source code comprises the vulnerability. In turn, the approach removes, using the AIM, the vulnerability from the initial source code to produce a refactored source code in response to determining that the initial source code comprises the vulnerability.

Abstract: The present disclosure provides an approach of providing, to an artificial intelligence (AI) model, a malicious script that includes a malicious behavior. The AI model is configured to modify software code of the malicious script to produce modified software code that obfuscates the malicious behavior. The approach produces, by a processing device using the AI model, an adversarial script that includes the modified software code that obfuscates the malicious behavior. In turn, the approach initiates a malware detector to test the adversarial script.

Abstract: A process tree embedding is generated corresponding to a process tree. The process tree comprises a plurality of processes. The process tree embedding is processed with a machine learning model to generate an identification of malware associated with the process tree. In some embodiments, processing the process tree embedding with the machine learning model to generate the identification of malware associated with the process tree includes: processing the process tree embedding with the machine learning model to generate a classification of the process tree as being associated with malware; and, responsive to the classification indicating that the process tree is associated with malware, generating the identification of a first process of the plurality of processes that is relevant to the classification of the process tree as being associated with malware.

Abstract: A system and method of scrubbing sensitive data from records using patterns and large language models (LLM). The method includes receiving a request to process a record comprising data including sensitive data. The method includes identifying, based on one or more regex rules, a first set of scrubbing candidates associated with the record. The method includes identifying, by a processing device and based on a large language model (LLM), a second set of scrubbing candidates associated with the record. The method includes generating, based on the first set of scrubbing candidates and the second set of scrubbing candidates, a scrubbed record by scrubbing the record to remove the sensitive data.

Abstract: Methods and systems for applying a diffusion model to adversarial purification and generating adversarial samples in malware detection are disclosed. According to an example, a malware file is inputted to a diffusion model to obtain an adversarial sample by altering content of the malware file. The adversarial sample is further tested by a malware detector. In some examples, the content of an input file may be encoded prior to be processed by the diffusion model. If the malware detector can identify the adversarial sample as a malware file, the diffusion model is updated to further alter the content until the adversarial sample successfully deceives the malware detector. According to another example, an executable file is purified using a diffusion model prior to be inputted to a malware detector. The diffusion model may remove potential malware content from the executable file, thus improving the performance of the malware detector.

Abstract: Systems and methods of authentication utilizing a large language model (LLM) are provided. The method includes accessing a knowledge base comprising user-specific data of a user device associated with a domain. In response to a request from the user device for access to a resource of the domain, the method includes generating one or more authentication challenges based on the user-specific data. The one or more authentication challenges are generated by an LLM trained on the user-specific data and contextual interactions associated with the user device. In response to determining that a response to the one or more authentication challenges matches the user-specific data of the knowledge base and the contextual interactions, the method includes providing the user device access to the resource of the domain.

Abstract: A command line anomaly detection system can generate anomaly scores associated with command line entries, such that command line entries associated with the highest anomaly scores can be identified. The command line anomaly detection system can include a transformer model trained, via unsupervised machine learning, to determine meanings of components of individual command line entries. The command line anomaly detection system can also include an anomaly detection model trained, via unsupervised machine learning, to determine anomaly scores based on the meanings of components of individual command line entries determined by the transformer model.

Abstract: This invention is a downspout used to carry water over a roof's surface from one roof plane to another gutter or downspout. The down spout is a rectangular piece of aluminum with granulated surface to match the color and contour of the roof. This invention will eliminate water stains associated with water coming down from the gutter.