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Full-Text Articles in Physical Sciences and Mathematics
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden
National Training Aircraft Symposium (NTAS)
An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …
Harnessing Predictive Models For Assisting Network Forensic Investigations Of Dns Tunnels, Irvin Homem, Panagiotis Papapetrou
Harnessing Predictive Models For Assisting Network Forensic Investigations Of Dns Tunnels, Irvin Homem, Panagiotis Papapetrou
Annual ADFSL Conference on Digital Forensics, Security and Law
In recent times, DNS tunneling techniques have been used for malicious purposes, however network security mechanisms struggle to detect them. Network forensic analysis has been proven effective, but is slow and effort intensive as Network Forensics Analysis Tools struggle to deal with undocumented or new network tunneling techniques. In this paper, we present a machine learning approach, based on feature subsets of network traffic evidence, to aid forensic analysis through automating the inference of protocols carried within DNS tunneling techniques. We explore four network protocols, namely, HTTP, HTTPS, FTP, and POP3. Three features are extracted from the DNS tunneled traffic: …
An Accidental Discovery Of Iot Botnets And A Method For Investigating Them With A Custom Lua Dissector, Max Gannon, Gary Warner, Arsh Arora
An Accidental Discovery Of Iot Botnets And A Method For Investigating Them With A Custom Lua Dissector, Max Gannon, Gary Warner, Arsh Arora
Annual ADFSL Conference on Digital Forensics, Security and Law
This paper presents a case study that occurred while observing peer-to-peer network communications on a botnet monitoring station and shares how tools were developed to discover what ultimately was identified as Mirai and many related IoT DDOS Botnets. The paper explains how researchers developed a customized protocol dissector in Wireshark using the Lua coding language, and how this enabled them to quickly identify new DDOS variants over a five month period of study.