A Preliminary Analysis of Drive-by Email Attacks in Educational Institutes
In this study we investigate malicious spam emails in the context of educational institutes. The goal of the study is two folds, first, is to explore spam types of attacks and what their malicious contents may include and secondly, to analyze if these attacks exhibit discriminative characteristics. This study offers an empirical analysis of spam emails dataset and provides a rich set of features that exist in the dataset. The features could then be used by researchers to build new intelligent systems that are capable of classifying and blocking spam emails.
Online Social Networks Security: Threats, Attacks, and Future Directions
A list of well-known Online Social Networks extend to hundreds of available sites with hundreds of thousands, millions, and even billions of registered accounts; for instance, Facebook as of April 2016 has around two billion active users. Online Social Networks made a difference in many people�s lives and helped in opening avenues that were not possible before. However, as in any success story there is a downside. Cyber-attacks that used to have a small or limited effect can now have a huge distributed effect through utilizing those social network sites. Some attacks are more apparent than others in this context; hence this chapter discusses how serious attacks are possible in online social networks and what has been done to encounter them. It will discuss privacy, Sybil attacks, social engineering, spam, malware, botnet attacks, and the trade-off between services, security, and users� rights. Social Media Shaping e-Publishing and Academia Social Media Shaping e-Publishing and Academia Look Inside Reference tools Export citation Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn.
Using IPython for Teaching Web Scraping
Web scraping constitutes an indispensable part of information gathering and data intelligence. IPython has been the de facto project for data science since 2001. In this chapter, IPython is employed to support educators in teaching the fundamentals of web scraping. The authors identify providing detailed labs as free online resources together with model answers as the main contribution of this chapter.
Spam Profile Detection in Social Networks based on Public Features
In the context of Online Social Networks, Spam profiles are not just a source of unwanted ads, but a serious security threat used by online criminals and terrorists for various malicious purposes. Recently, such criminals were able to steal a number of accounts that belong to NatWest bank's customers. Their attack vector was based on spam tweets posted by a Twitter account which looked very close to NatWest customer support account and leaded users to a link of a phishing site. In this study, we investigate the nature of spam profiles in Twitter with a goal to improve social spam detection. Based on a set of publicly available features, we develop spam profiles detection models. At this stage, a dataset of 82 Twitter's profiles are collected and analyzed. With feature engineering, we investigate ten binary and simple features that can be used to classify spam profiles. Moreover, a feature selection process is utilized to identify the most influencing features in the process of detecting spam profiles. For feture selection, two methods are used ReliefF and Information Gain. While for classification, four classification algorithms are applied and compared: Decision Trees, Multilayer Perceptron, k-Nearest neighbors and Naive Bayes. Preliminary experiments in this work show that the promising detection rates can be obtained using such features regardless of the language of the tweets.