Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored

Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure

Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire

New venture internationalisation and the cluster life cycle: insights from Ireland’s indigenous software industry

The internationalization of new and small firms has been a long-standing concern of researchers in international business (Coviello and McAuley, 1999; Ruzzier et al., 2006). This topic has been re-invigorated over the last decade by the burgeoning literature on so-called ‘born globals’ (BG) or ‘international new ventures’ (INV) – businesses that confound the expectations of traditional theory by being active internationally at, or soon after, inception (Aspelund et al., 2007; Bell, 1995; Rialp et al., 2005). Until quite recently, this literature had not really considered how the home regional environment of a new venture might influence its internationalization behaviour. However, a handful of recent studies have shown that being founded in a geographic industry ‘cluster’ can positively influence the likelihood of a new venture internationalizing (e.g., Fernhaber et al., 2008; Libaers and Meyer, 2011). This chapter seeks to build on these recent contributions by further probing the relationship between clusters and new venture internationalization. Specifically, taking inspiration from recent work in the thematic research stream on clusters (which spans the fields of economic geography, regional studies and industrial dynamics), the chapter explores how the emergence and internationalization of new ventures might be affected by the ‘cluster life cycle’ context within which they are founded. This issue is examined through a revelatory longitudinal case study of Ireland’s indigenous software cluster. The study investigates the origins and internationalization behaviour of ‘leading’ Irish software ventures but, in contrast to many existing studies, it seeks to understand these firms within the context of the Irish software cluster’s emergence and evolution through a number of ‘life-cycle’ stages