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Found 15 papers, 1 papers with code
A Review of Cybersecurity Incidents in the Food and Agriculture Sector
The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks.
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning
Results on a typical network configuration show that the RL agent can automatically discover resilient C2 attack paths utilizing both Tor-based and conventional communication channels, while also bypassing network firewalls.
Discovering Command and Control Channels Using Reinforcement Learning
Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks.
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data.
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach.
A Novel Approach To User Agent String Parsing For Vulnerability Analysis Using Mutli-Headed Attention
The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web.
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR).
Zero Day Threat Detection Using Metric Learning Autoencoders
The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale.
Anomaly Detection via Federated Learning
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior.
Lateral Movement Detection Using User Behavioral Analysis
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack.
Zero Day Threat Detection Using Graph and Flow Based Security Telemetry
With this paper, the authors' overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs
Reinforcement learning (RL), in conjunction with attack graphs and cyber terrain, are used to develop reward and state associated with determination of optimal paths for exfiltration of data in enterprise networks.
AI Assurance using Causal Inference: Application to Public Policy
Developing and implementing AI-based solutions help state and federal government agencies, research institutions, and commercial companies enhance decision-making processes, automate chain operations, and reduce the consumption of natural and human resources.
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs
Cyber attacks pose existential threats to nations and enterprises.
Using Cyber Terrain in Reinforcement Learning for Penetration Testing
Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain.
