The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.

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The usage of cloud systems is at an all-time high, and with more organizations reaching for Big Data the forensic implications must be analyzed. The Hadoop Distributed File System is widely used both as a cloud service and with organizations implementing it themselves. This paper analyzes the forensic viability of a RAM analysis method for Hadoop based investigations and compared it against targeted process data dumping through the Java heap information. The RAM analysis has been done through string searching and the use of the RAM analysis tool Volatility. This work found that RAM analysis can be a valuable tool for discovering artefacts of deleted resources from a Hadoop cluster but was unable to discover further information such as the block to node mapping. The targeted process analysis managed to provide some partial information about deleted resources and also produce important metadata on the current state of the file system.

Modern Intrusion Detection Systems are able to identify and check all traffic crossing the network segments that they are only set to monitor. Traditional network infrastructures use static detection mechanisms that check and monitor specific types of malicious traffic. To mitigate this potential waste of resources and improve scalability across an entire network, we propose a methodology which deploys distributed IDS in a Software Defined Network allowing them to be used for specific types of traffic as and when it appears on a network. The core of our work is the creation of an SDN application that takes input from a Snort IDS instances, thus working as a classifier for incoming network traffic with a static ruleset for those classifications. Our application has been tested on a virtualised platform where it performed as planned holding its position for limited use on static and controlled test environments.