Sri International

Michael E Locasto

Gabriela F Ciocarlie

Sek Meng Chai

Hassen Saidi

Scott Oberg

Karim Eldefrawy

Tancrède Lepoint

Dejan Jovanovic

Ulf E Lindqvist

Matthew Filippone

Norrathep Rattanavipanon

Cherita Corbett

Monowar Hasan

Bogdan Copos

Nicholas Vitovitch

Prashant Anantharaman

Matteo Campanelli

Chien-Ying Chen

Ioannis Agadakos

In general, this disclosure describes techniques for replacing target cryptographic primitives in executable binary files with other, potentially more secure, cryptographic primitives. In some examples, a computing system for augmenting cryptographic executables includes a locator to determine if an executable program in an executable binary file includes a target cryptographic primitive. The computing system can include a patch generator to generate patch instructions in response to a determination by the locator that the executable program includes the target cryptographic primitive. The patch instructions cause the executable program to execute a replacement cryptographic primitive instead of the target cryptographic primitive. A rewriter engine of the computing system can modify, based on the patch instructions, the executable program to generate a modified executable binary file.

Augmenting executables having cryptographic primitives

A method, apparatus and system for determining a weakness or risk for devices of an Internet-of-things (IoT) network include determining a representation of a physical environment of the IoT network and expected physical and cyber interactions between the devices of the IoT network based at least in part on operating characteristics of the devices of the IoT network, monitoring the physical environment and actual interactions between the devices to generate a network model including at least one of uncharacteristic physical or cyber interaction paths between the devices, based on the determined network model, determining at least one weakness or risk of at least one of the IoT network or of at least one of the devices, and providing a metric of security of at least one of the IoT network or of at least one of the devices based on at least one of the determined weakness or risk.

Modeling cyber-physical attack paths in the internet-of-things

Intrusion detection systems and methods monitor legal control messages in an operational control system to detect subtly malicious sequences of control messages with undesirable emergent effects on devices in the operational control system. A message provenance component may investigate system-level correlations between messages rather than detecting if individual messages are anomalous. A semantic fuzzing component may search, based on the operational effect of candidate message sequences, the space of legal messages for sequences that cause actual harm. Behavior oracles may be used to test message sequences to identify sequences that induce drift towards a failure state. The intrusion detection system is able to prevent harm and disruption arising from control messages that individually appear legitimate and benign but that, in combination with other messages, can cause undesirable outcomes.

Intrusion detection via semantic fuzzing and message provenance

A method, apparatus and system for malware characterization includes receiving data identifying a presence of at least one anomaly of a respective portion of a processing function captured by at least one of each of at least two different sensor payloads and one sensor payload at two different times, determining a correlation between the at least two anomalies identified by the data captured by the at least one sensor payloads, and determining a presence of malware in the processing function based on the determined correlation. The method, apparatus and system can further include predicting an occurrence of at least one anomaly in the network based on at least one of current sensor payload data or previously observed and stored sensor payload data, recommending and/or initiating a remediation action and reporting a result of the malware characterization to a user.

Method of malware characterization and prediction

Growing community of inventors

Lebanon, NJ, United States of America

Michael E Locasto