The objectives of this study were to identify control points for bacterial contamination of bovine colostrum during the harvesting and feeding processes, and to describe the effects of refrigeration and use of potassium sorbate preservative on bacteria counts in stored fresh colostrum. For objective 1, first-milking colostrum samples were collected aseptically directly from the mammary glands of 39 cows, from the milking bucket, and from the esophageal feeder tube. For objective 2, 15-mL aliquots of colostrum were collected from the milking bucket and allocated to 1 of 4 treatment groups: 1) refrigeration, 2) ambient temperature, 3) refrigeration with potassium sorbate preservative, and 4) ambient temperature with potassium sorbate preservative. Subsamples from each treatment group were collected after 24, 48, and 96 h of storage. All samples underwent bacteriological culture for total plate count and coliform count. Bacteria counts were generally low or zero in colostrum collected directly from the gland [mean (SD) log10 cfu/mL(udder) = 1.44 (1.45)]. However, significant bacterial contamination occurred during the harvest process [mean (SD) log10 cfu/mL(bucket) = 4.99 (1.95)]. No additional bacterial contamination occurred between the bucket and the esophageal feeder tube. Storing colostrum at warm ambient temperatures resulted in the most rapid increase in bacteria counts, followed by intermediate rates of growth in nonpreserved refrigerated samples or preserved samples stored at ambient temperature. The most effective treatment studied was the use of potassium sorbate preservative in refrigerated samples, for which total plate count and total coliform counts dropped significantly and then remained constant during the 96-h storage period.

a b s t r a c tCurrent malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

a b s t r a c tDue to budgetary constraints and the high level of training required, digital forensic analysts are in short supply in police forces the world over. This inevitably leads to a prolonged time taken between an investigator sending the digital evidence for analysis and receiving the analytical report back. In an attempt to expedite this procedure, various process models have been created to place the forensic analyst in the field conducting a triage of the digital evidence. By conducting triage in the field, an investigator is able to act upon pertinent information quicker, while waiting on the full report. The work presented as part of this paper focuses on the training of front-line personnel in the field triage process, without the need of a forensic analyst attending the scene. The premise has been successfully implemented within regular/non-digital forensics, i.e., crime scene investigation. In that field, front-line members have been trained in specific tasks to supplement the trained specialists. The concept of front-line members conducting triage of digital evidence in the field is achieved through the development of a new process model providing guidance to these members. To prove the model's viability, an implementation of this new process model is presented and evaluated. The results outlined demonstrate how a tiered response involving digital evidence specialists and nonspecialists can better deal with the increasing number of investigations involving digital evidence.

The increasing prevalence of Internet of Things (IoT) devices has made it inevitable that their pertinence to digital forensic investigations will increase into the foreseeable future. These devices produced by various vendors often posses limited standard interfaces for communication, such as USB ports or WiFi/Bluetooth wireless interfaces. Meanwhile, with an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices, and IoT devices are no exception. Under these circumstances, a significant challenge is presented to digital forensic investigations where data from IoT devices needs to be analysed.This work explores the electromagnetic (EM) side-channel analysis literature for the purpose of assisting digital forensic investigations on IoT devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices. The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. The literature on various EM side-channel analysis attack techniques are discussed -selected on the basis of their applicability in IoT device investigation scenarios. The insight gained from the background study is used to identify promising future applications of the technique for digital forensic analysis on IoT devices -potentially progressing a wide variety of currently hindered digital investigations.

Abstract-In recent years, technology has become truly pervasive in everyday life. Technological advancement can be found in many facets of life, including personal computers, mobile devices, wearables, cloud services, video gaming, web-powered messaging, social media, Internet-connected devices, etc. This technological influence has resulted in these technologies being employed by criminals to conduct a range of crimes -both online and offline. Both the number of cases requiring digital forensic analysis and the sheer volume of information to be processed in each case has increased rapidly in recent years. As a result, the requirement for digital forensic investigation has ballooned, and law enforcement agencies throughout the world are scrambling to address this demand. While more and more members of law enforcement are being trained to perform the required investigations, the supply is not keeping up with the demand. Current digital forensic techniques are arduously timeconsuming and require a significant amount of man power to execute. This paper discusses a novel solution to combat the digital forensic backlog. This solution leverages a deduplicationbased paradigm to eliminate the reacquisition, redundant storage, and reanalysis of previously processed data.