10,130 research outputs found
Addressing the new generation of spam (Spam 2.0) through Web usage models
New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem
Deceptive Practices 2.0: Legal and Policy Responses
Reviews recent online misinformation campaigns and "cyberfraud" to suppress voting and skew elections, mainly in minority communities. Examines whether federal and state laws can sufficiently deter and punish perpetrators. Makes policy recommendations
Efficient Implementation of a Synchronous Parallel Push-Relabel Algorithm
Motivated by the observation that FIFO-based push-relabel algorithms are able
to outperform highest label-based variants on modern, large maximum flow
problem instances, we introduce an efficient implementation of the algorithm
that uses coarse-grained parallelism to avoid the problems of existing parallel
approaches. We demonstrate good relative and absolute speedups of our algorithm
on a set of large graph instances taken from real-world applications. On a
modern 40-core machine, our parallel implementation outperforms existing
sequential implementations by up to a factor of 12 and other parallel
implementations by factors of up to 3
Penalized Likelihood and Bayesian Function Selection in Regression Models
Challenging research in various fields has driven a wide range of
methodological advances in variable selection for regression models with
high-dimensional predictors. In comparison, selection of nonlinear functions in
models with additive predictors has been considered only more recently. Several
competing suggestions have been developed at about the same time and often do
not refer to each other. This article provides a state-of-the-art review on
function selection, focusing on penalized likelihood and Bayesian concepts,
relating various approaches to each other in a unified framework. In an
empirical comparison, also including boosting, we evaluate several methods
through applications to simulated and real data, thereby providing some
guidance on their performance in practice
Analyzing Social and Stylometric Features to Identify Spear phishing Emails
Spear phishing is a complex targeted attack in which, an attacker harvests
information about the victim prior to the attack. This information is then used
to create sophisticated, genuine-looking attack vectors, drawing the victim to
compromise confidential information. What makes spear phishing different, and
more powerful than normal phishing, is this contextual information about the
victim. Online social media services can be one such source for gathering vital
information about an individual. In this paper, we characterize and examine a
true positive dataset of spear phishing, spam, and normal phishing emails from
Symantec's enterprise email scanning service. We then present a model to detect
spear phishing emails sent to employees of 14 international organizations, by
using social features extracted from LinkedIn. Our dataset consists of 4,742
targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack
emails sent to 5,912 non victims; and publicly available information from their
LinkedIn profiles. We applied various machine learning algorithms to this
labeled data, and achieved an overall maximum accuracy of 97.76% in identifying
spear phishing emails. We used a combination of social features from LinkedIn
profiles, and stylometric features extracted from email subjects, bodies, and
attachments. However, we achieved a slightly better accuracy of 98.28% without
the social features. Our analysis revealed that social features extracted from
LinkedIn do not help in identifying spear phishing emails. To the best of our
knowledge, this is one of the first attempts to make use of a combination of
stylometric features extracted from emails, and social features extracted from
an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature
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