An examination of the Asus WL-HDD 2.5 as a nepenthes malware collector

The Linksys WRT54g has been used as a host for network forensics tools for instance Snort for a long period of time. Whilst large corporations are already utilising network forensic tools, this paper demonstrates that it is quite feasible for a non-security specialist to track and capture malicious network traffic. This paper introduces the Asus Wireless Hard disk as a replacement for the popular Linksys WRT54g. Firstly, the Linksys router will be introduced detailing some of the research that was undertaken on the device over the years amongst the security community. It then briefly discusses malicious software and the impact this may have for a home user. The paper then outlines the trivial steps in setting up Nepenthes 0.1.7 (a malware collector) for the Asus WL-HDD 2.5 according to the Nepenthes and tests the feasibility of running the malware collector on the selected device. The paper then concludes on discussing the limitations of the device when attempting to execute Nepenthes

Knowledge sharing and professional online communities acceptance : an integrated model

This study aims to advance empirical research in the realm of the use of professional online communities for knowledge sharing. Use of these communities is likely to be influenced not only by social factors but also by cognitive and technological factors. Hence, drawing upon theoretical and empirical foundations and contextually relevant previous research, three theoretical frameworks were developed and applied, in which relational factors (trust), individual factors (knowledge/system self-efficacy), and technological factors (system quality and content quality) were integrated together with the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the use of professional online communities to acquire/provide knowledge among professionals. To test these theoretical models, an online web-survey was administered to 366 members of eight professional communities in Egypt.Employing covariance-based structural equation modelling (CB-SEM), the results of this study confirmed that professional online communities have emerged as an essential channel to facilitate knowledge sharing among professionals. Performance expectancy and personal outcome expectancy were found to be the strongest determinants of professional online community use. Relational capital - trust - was found to be a significant predictor of usage behaviour. However, for members who used the community for knowledge provision, trust was found to have a stronger influence than was perceived trust on using the community for knowledge acquisition. For members who used the community for knowledge acquisition, effort expectancy and social influence revealed significant effect, in contrast to members who use the community for knowledge provision. Regarding the hypotheses common to both use behaviours, the findings demonstrated some significant differences. Content quality, for example, seemed to have a clearly stronger influence on trust than system quality in all models. Content quality showed stronger effect on trust for using professional online communities for knowledge provision than using for knowledge acquisition, while system quality was found to be a stronger predictor of trust in the use for knowledge acquisition. For effort expectancy, system quality tended to have a stronger influence than system self-efficacy in all models; however, the influence of system quality on effort expectancy tended to be more important when online communities are used for knowledge acquisition.As for moderating effects, the influence of performance expectancy on use for knowledge acquisition and the influence of personal outcome expectancy on use for knowledge provision were found to be moderated by users’ gender (stronger for men) and age (stronger for younger users), while the influence of performance expectancy on use for knowledge acquisition was found to be influenced by users’ experience (stronger for less experienced users)

Detecting cyberstalking from social media platform(s) using data mining analytics

Cybercrime is an increasing activity that leads to cyberstalking whilst making the use of data mining algorithms to detect or prevent cyberstalking from social media platforms imperative for this study. The aim of this study was to determine the prevalence of cyberstalking on the social media platforms using Twitter. To achieve the objective, machine learning models that perform data mining alongside the security metrics were used to detect cyberstalking from social media platforms. The derived security metrics were used to flag up any suspicious cyberstalking content. Two datasets of detailed tweets were analysed using NVivo and R Programming. The dominant occurrence of cyberstalking was assessed with the induction of fifteen unigrams identified from the preliminary dataset such as “abuse”, “annoying”, “creep or creepy”, “fear”, “follow or followers”, “gender”, “harassment”, “messaging”, “relationships p/p”, “scared”, “stalker”, “technology”, “unwanted”, “victim”, and “violent”. Ordinal regression was used to analyse the use of the fifteen unigrams which were categorised according to degree or relationship/link towards cyberstalking on the platform Twitter. Moreover, two lightweight machine learning algorithms were used for the model performance showcasing cyberstalking indicative content. K Nearest Neighbour and K Means Clustering were both coded in R computer language for the extraction, refined, analysation and visualisation process for this research. Results showed the emotional terms like “bad”, “sad” and “hate” were attached to the unigrams being linked to cyberstalking. Each emotional term was flagged up in correspondence with one of the fifteen unigrams in tweets that correlate cyberstalking indicative content, proving one must accompany the other. K Means Clustering results showed the two terms “bad” and “sad” were shown within 100 percent of the clustering results and the term “hate” was only seen within 60 percent of the results. Results also revealed that the accuracy of the KNN algorithm was up to 40% in predicting key terms-based cyberstalking content in a real Twitter dataset consisting of 1m data points. This study emphasises the continuous relationship between the fifteen unigrams, emotional terms, and tweets within numerous datasets portrayed in this research, and reveals a general picture that cyberstalking indicative content in fact happens on Twitter at a vast rate with the corresponding links or relationships within the detection of cyberstalking