Abstract: An artificial intelligence (AI) based advanced malware detection tool (AIMaD), which uses a combination of both static and dynamic malware analysis in a machine learning (ML) framework. It uses reverse engineering and feature extraction technique at DLL, function call, and assembly levels; these multi-level features are then processed with N-gram (i.e., Natural Language Processing, NLP), association rule mining to feed in different machine learning classifiers. The AIMaD is able to detect malware/ransomware with high accuracy and low false-positive rate.

Abstract: A system and related methods for providing greater security and control over access to classified files and documents and other forms of sensitive information based upon a multi-user permission strategy centering on organizational structure. Based on the sensitivity or classification of the information being requested by a user, approvers are selected dynamically based on the work environment (e.g., mobility, use of the computing device seeking access, access policy, and the like). The selected sets of approvers are non-repetitive in nature, making it very hard to guess which approvers will be selected for a particular request.

Abstract: A Dual-Filtering (DF) system to provide a robust Machine Learning (ML) platform against adversarial attacks. It employs different filtering mechanisms (one at the input and the other at the output/decision end of the learning system) to thwart adversarial attacks. The developed dual-filter software can be used as a wrapper to any existing ML-based decision support system to prevent a wide variety of adversarial evasion attacks. The DF framework utilizes two filters based on positive (input filter) and negative (output filter) verification strategies that can communicate with each other for higher robustness.

Abstract: Systems and related methods for providing greater security and control over access to protected or classified resources, files and documents and other forms of sensitive information based upon an initial adaptive selection of multiple modalities for authentication in different operating environments, with subsequent multi-user permission strategy centering on organizational structure. The system calculates trustworthiness values of different authentication factors under various environmental settings, and combines a trust-based adaptive, robust and scalable software-hardware framework for the selection of authentication factors for continuous and triggered authentication with optimal algorithms to determine the security parameters of each of the authentication factors. A subset of authentication factors thus are determined for application at triggering events on-the-fly, thereby leaving no exploitable a priori pattern or clue for hackers to exploit.

Abstract: A system and related methods for providing greater security and control over access to classified files and documents and other forms of sensitive information based upon a multi-user, multi-modality permission strategy centering on organizational structure, thereby making authentication strategy unpredictable so to significantly reduce the risk of exploitation. Based on the sensitivity or classification of the information being requested by a user, approvers are selected dynamically based on the work environment, e.g., mobility, use of the computing device seeking access, authentication factors under applicable environmental settings, access policy, and the like.

Abstract: Systems and related methods for providing greater security and control over access to protected or classified resources, files and documents and other forms of sensitive information based upon an initial adaptive selection of multiple modalities for authentication in different operating environments, with subsequent multi-user permission strategy centering on organizational structure. The system calculates trustworthiness values of different authentication factors under various environmental settings, and combines a trust-based adaptive, robust and scalable software-hardware framework for the selection of authentication factors for continuous and triggered authentication with optimal algorithms to determine the security parameters of each of the authentication factors. A subset of authentication factors thus are determined for application at triggering events on-the-fly, thereby leaving no exploitable a priori pattern or clue for hackers to exploit.

Abstract: A system and related methods for providing greater security and control over access to classified files and documents and other forms of sensitive information based upon a multi-user permission strategy centering on organizational structure. Based on the sensitivity or classification of the information being requested by a user, approvers are selected dynamically based on the work environment (e.g., mobility, use of the computing device seeking access, access policy, and the like). The selected sets of approvers are non-repetitive in nature, making it very hard to guess which approvers will be selected for a particular request.

Abstract: A system and related methods for providing greater security and control over access to classified files and documents and other forms of sensitive information based upon a multi-user, multi-modality permission strategy centering on organizational structure, thereby making authentication strategy unpredictable so to significantly reduce the risk of exploitation. Based on the sensitivity or classification of the information being requested by a user, approvers are selected dynamically based on the work environment, e.g., mobility, use of the computing device seeking access, authentication factors under applicable environmental settings, access policy, and the like.

Abstract: A system and methodology for adaptive selection of multiple modalities for authentication in different operating environments, thereby making authentication strategy unpredictable so to significantly reduce the risk of exploitation by authentication-guessing attacks. The system calculates trustworthiness values of different authentication factors under various environmental settings, and combines a trust-based adaptive, robust and scalable software-hardware framework for the selection of authentication factors for continuous and triggered authentication with optimal algorithms to determine the security parameters of each of the authentication factors. A subset of authentication factors thus are determined for application at triggering events on-the-fly, thereby leaving no exploitable a priori pattern or clue for hackers to exploit.

Abstract: A system and methodology for adaptive selection of multiple modalities for authentication in different operating environments, thereby making authentication strategy unpredictable so to significantly reduce the risk of exploitation by authentication-guessing attacks. The system calculates trustworthiness values of different authentication factors under various environmental settings, and combines a trust-based adaptive, robust and scalable software-hardware framework for the selection of authentication factors for continuous and triggered authentication with optimal algorithms to determine the security parameters of each of the authentication factors. A subset of authentication factors thus are determined for application at triggering events on-the-fly, thereby leaving no exploitable a priori pattern or clue for hackers to exploit.

Abstract: The disclosed invention is a method for screening access to a computer system using a negative authentication system. Input login requests are compared against a set of detectors comprising anti-passwords and only allowed further access if they do not match any of the anti-passwords. A method of generating a set of detectors comprising anti-passwords is also disclosed.