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Found 14 papers, 1 papers with code
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 Simple yet Efficient Ensemble Approach for AI-generated Text Detection
Experiments conducted on four benchmark datasets for generative text classification show performance improvements in the range of 0. 5 to 100\% compared to previous state-of-the-art approaches.
A Closer Look at Bearing Fault Classification Approaches
Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations.
Generative AI Text Classification using Ensemble LLM Approaches
For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0. 733 and 0. 649) for English and Spanish texts, respectively.
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.
