Non Deterministic Processing in Neural Networks : An Introduction to Multi-Threaded Neural Networks

Since McCullough and Pitts first published their work on the Binary Decision Neuron much research has been accumulated in the area of neural networks. This work has for the most part centred on network topologies and learning algorithms. The neural networks that have found their way into devices such as handheld PC’s are the fruit of NN research that has spanned 57 years. There is a simplistic beauty in the way that artificial neural networks model the biological foundations of the human thought process, but one piece of the jigsaw puzzle is still missing. We have so far been unable to match the massive parallelness of the human brain. This paper attempts to explain how multithreaded neural networks can be used as a basis for building parallel networks. By studying simple concurrent networks is hoped that significant inroads can be made into a better understanding of how neural network processing can be spread across multiple processors. The paper outlines some biological foundations and introduces some approaches that may be used to recreate software implementations of concurrent artificial neural networks

A Java Framework for Computer Vision

This paper outlines a framework implemented entirely in Java that attempts to give students exposure to computer vision systems from a practical standpoint. Various tools and technologies are introduced that will allow a student to acquire an input image through a WebCam, extract useful information from that input image and finally, attempt to make sense of the input

Image-based malware classification hybrid framework based on space-filling curves

There exists a never-ending “arms race” between malware analysts and adversarial malicious code developers as malevolent programs evolve and countermeasures are developed to detect and eradicate them. Malware has become more complex in its intent and capabilities over time, which has prompted the need for constant improvement in detection and defence methods. Of particular concern are the anti-analysis obfuscation techniques, such as packing and encryption, that are employed by malware developers to evade detection and thwart the analysis process. In such cases, malware is generally impervious to basic analysis methods and so analysts must use more invasive techniques to extract signatures for classification, which are inevitably not scalable due to their complexity. In this article, we present a hybrid framework for malware classification designed to overcome the challenges incurred by current approaches. The framework incorporates novel static and dynamic malware analysis methods, where static malware executables and dynamic process memory dumps are converted to images mapped through space-filling curves, from which visual features are extracted for classification. The framework is less invasive than traditional analysis methods in that there is no reverse engineering required, nor does it suffer from the obfuscation limitations of static analysis. On a dataset of 13,599 obfuscated and non-obfuscated malware samples from 23 families, the framework outperformed both static and dynamic standalone methods with precision, recall and accuracy scores of 97.6%, 97.6% and 97.6% respectively

Detection of DNS Based Covert Channels

Information theft or data exfiltration, whether personal or corporate, is now a lucrative mainstay of cybercrime activity. Recent security reports have suggested that while information, such as credit card data is still a prime target, other data such as corporate secrets, employee files and intellectual property are increasingly sought after on the black market. Malicious actors that are intent on exfiltrating valuable data, usually employ some form of Advanced Persistent Threat (APT) in order to exfiltrate large amounts of data over a long period of time with a high degree of covertness. Botnets are prime examples of APTs that are usually established on targeted systems through malware or exploit kits that leverage system vulnerabilities. Once established, Botnets rely on covert command and control (C&C) communications with a central server, this allows a malicious actor to keep track of compromised systems and to send out instructions for compromised systems to do their biding. Covert channels provide an ideal mechanism for data exfiltration and the exchange of command and control messages that are essential to a Botnets effectiveness. Our work focuses on one particular form of covert channel that enables communication of hidden messages over normal Domain Name Server (DNS) network traffic. Covert channels based on DNS traffic are of particular interest, as DNS requests are an essential part of most Internet traffic and as a result are rarely filtered or blocked by firewalls. As part of our work we have created a test bed system that uses a covert DNS channel to exfiltrate data from a compromised host. Using this system we have carried out network traffic analysis that uses baseline comparisons as a means to fingerprint covert DNS activity. Even though detection of covert DNS activity is relatively straightforward, there is anecdotal evidence to suggest that most organisations do not filter or pay enough attention to DNS traffic and are therefore susceptible to data exfiltration attacks once a host on their network has been compromised. Our work shows that freely available covert DNS tools have particular traffic signatures that can be detected in order to mitigate data exfiltration and C&C traffic

Improving the Stealthiness of DNS-Based Covert Communication

At present, the recommended stance to take regarding Cyber Security is to assume a state of compromise. With the increase in Bring Your Own Device (BYOD), the Internet of Things (IOT) and Advanced Persistent Threats (ATPs), network boundaries have become porous and difficult to defend from external threats. Modern malware is complex and adept at making its presence hard to detect. Recent studies have shown that some malware variants are capable of using multiple covert communication channels for command and control (C2) and data exfiltration activities. Examples of this level of covert communication can be found in malware that targets Point of Sale (POS) systems and it has been hugely successful in exfiltrating large amounts of valuable payment information that can be sold on the black market. In the vast majority of cases, malware needs to communicate with some control mechanism or human controller in order to coordinate attacks, maintain lists of compromised machines and to exfiltrate data. There are many channels that malware can use for its communication. However, in recent times there has been an increase in malware that uses the Domain Name System (DNS) for communications in some shape or form. The work carried out in this paper explores the extent to which DNS can be used as a covert communication channel by examining a number of advanced approaches that can be used to increase the stealthy nature of DNS-based covert channels. Our work describes techniques that can be used to shadow legitimate network traffic by observing network packets leaving a host machine (piggybacking), the use of statistical modelling such as the Poisson distribution and a dynamic Poisson distribution model that can be used to further conceal malicious DNS activity within a network. The results obtained from this work show that current DNS-based C2 and data exfiltration approaches employed by malware have considerable room for improvement which suggests that DNS-based covert communication will remain a realistic threat into the future

Classifying Recaptured Identity Documents Using the Biomedical Meijering and Sato Algorithms

Recaptured identity documents are a low-cost, high-risk threat to modern eKYC systems. Bad actors can easily manipulate images and print them. Existing solutions typically demand manual review of remotely captured identity documents, this is expensive and does not scale. In 2022, the UK National Crime Agency estimated fraud cost business hundreds of billion pounds per year and document forgery is an area of investigation by Europol.https://arrow.tudublin.ie/cddpos/1002/thumbnail.jp

Pathfinding in Computer Games

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability to handle pathfinding in real-time

The Effects of Parental Incarceration on the School Behavior of Poor Urban Black Children

Children whose parents are incarcerated experience emotional traumas that are harmful to their social competence and overall well-being. When parents go to prison, children’s lives become traumatic, distressed, and unstable. Young children who are unable to articulate their emotional distress instead manifest disruptive behaviors in school. Poor black children who display disruptive behaviors in school are at especially high-risk for exclusionary discipline practices, such as suspension and expulsion. These practices have been shown to negatively impact the development of their social and emotional competence and further impede their academic achievement. The HOPE Project was a 3-year pilot project that provided school-based therapeutic services to black children with incarcerated parents. The children were enrolled in three elementary schools located in an urban, poverty-impacted community. Program evaluation findings suggest that intense age-appropriate therapy conducted in schools is a helpful intervention for reducing negative in-school behaviors and increasing the social and emotional competence of poor, urban black children to keep them engaged in school. The findings have important implications for social work practice in the school setting with children who have parents that are incarcerated

Endogenous cross-talk of fungal metabolites

Non-ribosomal peptide (NRP) synthesis in fungi requires a ready supply of proteogenic and non-proteogenic amino acids which are subsequently incorporated into the nascent NRP via a thiotemplate mechanism catalyzed by NRP synthetases. Substrate amino acids can be modified prior to or during incorporation into the NRP, or following incorporation into an early stage amino acid-containing biosynthetic intermediate. These post-incorporation modifications involve a range of additional enzymatic activities including but not exclusively, monooxygenases, methyltransferases, epimerases, oxidoreductases, and glutathione S-transferases which are essential to effect biosynthesis of the final NRP. Likewise, polyketide biosynthesis is directly by polyketide synthase megaenzymes and cluster-encoded ancillary decorating enzymes. Additionally, a suite of additional primary metabolites, for example: coenzyme A (CoA), acetyl CoA, S-adenosylmethionine, glutathione (GSH), NADPH, malonyl CoA, and molecular oxygen, amongst others are required for NRP and polyketide synthesis (PKS). Clearly these processes must involve exquisite orchestration to facilitate the simultaneous biosynthesis of different types of NRPs, polyketides, and related metabolites requiring identical or similar biosynthetic precursors or co-factors. Moreover, the near identical structures of many natural products within a given family (e.g., ergot alkaloids), along with localization to similar regions within fungi (e.g., conidia) suggests that cross-talk may exist, in terms of biosynthesis and functionality. Finally, we speculate if certain biosynthetic steps involved in NRP and PKS play a role in cellular protection or environmental adaptation, and wonder if these enzymatic reactions are of equivalent importance to the actual biosynthesis of the final metabolite