Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems

Authentication for Multi-located Parties and Wireless Ad Hoc Networks

This thesis present a new authentication protocol for multi-located parties which uses an agent based scheme that divides the message into two parts together with a key distribution center to ensure stronger authentication. It also presents a protocol for wireless ad hoc networks to combat spamming and reduce traffic overload. The appropriate number of authentication agents was calculated for a wireless ad hoc network. Simulations were run for networks of 200, 1000, 2000 and 4000 nodes and it was found that 0.075 n (n is the number of nodes in the network) authentication agents work well to distribute the load evenly amongst them.Computer Science Departmen

Towards blocking outgoing malicious impostor emails

Electronic mails (emails) have become an indispensable part of most people’s daily routines. However, they were not designed for deployment in an adversarial environment, which explains why there have been so many incidents such as spamming and phishing. Malicious impostor emails sent by sophisticated attackers are perhaps even more damaging, because their contents, except the attachments, may look perfectly legitimate while silently targeting certain critical information such as cryptographic keys and passwords. In this paper, we explore a mechanism for blocking malicious impostor emails called ContAining Malicious Emails Locally (CAMEL), which aims at blocking compromised victim user machines from further infecting others. 1

Image Understanding for Automatic Human and Machine Separation.

PhDThe research presented in this thesis aims to extend the capabilities of human interaction proofs in order to improve security in web applications and services. The research focuses on developing a more robust and efficient Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) to increase the gap between human recognition and machine recognition. Two main novel approaches are presented, each one of them targeting a different area of human and machine recognition: a character recognition test, and an image recognition test. Along with the novel approaches, a categorisation for the available CAPTCHA methods is also introduced. The character recognition CAPTCHA is based on the creation of depth perception by using shadows to represent characters. The characters are created by the imaginary shadows produced by a light source, using as a basis the gestalt principle that human beings can perceive whole forms instead of just a collection of simple lines and curves. This approach was developed in two stages: firstly, two dimensional characters, and secondly three-dimensional character models. The image recognition CAPTCHA is based on the creation of cartoons out of faces. The faces used belong to people in the entertainment business, politicians, and sportsmen. The principal basis of this approach is that face perception is a cognitive process that humans perform easily and with a high rate of success. The process involves the use of face morphing techniques to distort the faces into cartoons, allowing the resulting image to be more robust against machine recognition. Exhaustive tests on both approaches using OCR software, SIFT image recognition, and face recognition software show an improvement in human recognition rate, whilst preventing robots break through the tests

Behavioural Observation for Critical Infrastructure Security Support

Critical infrastructures include sectors such as energy resources, finance, food and water distribution, health, manufacturing and government services. In recent years, critical infrastructures have become increasingly dependent on ICT; more interconnected and are often, as a result, linked to the Internet. Consequently, this makes these systems more vulnerable and increases the threat of cyber-attack. In addition, the growing use of wireless networks means that infrastructures can be more susceptible to a direct digital attack than ever before. Traditionally, protecting against environmental threats was the main focus of critical infrastructure preservation. Now, however, with the emergence of cyber-attacks, the focus has changed and infrastructures are facing a different danger with potentially debilitating consequences. Current security techniques are struggling to keep up to date with the sheer volume of innovative and emerging attacks; therefore, considering fresh and adaptive solutions to existing computer security approaches is crucial. The research presented in this thesis, details the use of behavioural observation for critical infrastructure security support. Our observer system monitors an infrastructure’s behaviour and detects abnormalities, which are the result of a cyber-attack taking place. By observing subtle changes in system behaviours, an additional level of support for critical infrastructure security is provided through a plug-in device, which operates autonomously and has no negative impact on data flow. Behaviour is evaluated using mathematical classifications to assess the data and detect changes. The subsequent results achieved during the data classification process were high and successful. Our observer approach was able to accurately classify 98.138 % of the normal and abnormal system behaviours produced by a simulation of a critical infrastructure, using nine data classifiers