Computer Engineering Commons^™

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 …

Julia Language Ephemeris And Physical Constants Reader For Solar System Bodies, Julia Mihaylov, Renee Spear

Undergraduate Research Symposium - Prescott

This presentation was the forerunner a second paper by the authors for the AAS/AIAA conference in January 2019, and is not available for download. For a description of the first presentation, see the authors' Discovery Day 2018 - Presentation.

Publicly released in 2012, the Julia language is a relatively new, open source dynamic language. One major benefit of Julia is its combined capabilities of both dynamic and static coding languages. It nearly matches the computational efficiency of static languages, such as FORTRAN or C, and exceeds that of dynamic languages, such as MATLAB or Python. Additionally, unlike Python, Julia …

Source Anonymization Of Digital Images: A Counter–Forensic Attack On Prnu Based Source Identification Techniques, Prithviraj Sengupta, Venkata Udaya Sameer, Ruchira Naskar, Ezhil Kalaimannan

Annual ADFSL Conference on Digital Forensics, Security and Law

A lot of photographers and human rights advocates need to hide their identity while sharing their images on the internet. Hence, source–anonymization of digital images has become a critical issue in the present digital age. The current literature contains a number of digital forensic techniques for “source–identification” of digital images, one of the most efficient of them being Photo–Response Non–Uniformity (PRNU) sensor noise pattern based source detection. PRNU noise pattern being unique to every digital camera, such techniques prove to be highly robust way of source–identification. In this paper, we propose a counter–forensic technique to mislead this PRNU sensor noise …

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

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.

Human Factors In The Development Of The Mobile Extreme Environment Research Station (Meers) Mission Control System (Mcs), Rebecca Demarco

Human Factors and Applied Psychology Student Conference

The developers for the MEERS MCS were required to develop a novel interface for their product. During the development of the MCS, analytical human factors methods and display principles were implemented to create the user interface. The methods and principles were also used so the MCS had a user centered design. The MCS went through several iterations of design. This presentation discusses the development process of the MEERS MCS from a human factors standpoint.