Abstract: Botnets are one of the most serious and widespread
cyber threats. Today botnets have been facilitating many
cybercrimes, especially financial, top secret thefts. Botnets can be
available for lease in the market and are utilized by the
cybercriminals to launch massive attacks like DDoS, click fraud,
phishing attacks etc., Several large institutions, hospitals, banks,
government organizations and many social networks such as twitter,
facebook etc., became the target of the botmasters. Recently,
noteworthy researches have been carried out to detect bot, C&C
channels, botnet and botmasters. Using many sophisticated
technologies, botmasters made botnet a titan of the cyber world.
Innumerable challenges have been put forth by the botmasters to the
researchers in the detection of botnet. In this paper we present a
survey of different types of botnet C&C channels and also provide a
comparison of various botnet categories. Finally we hope that our
survey will create awareness for forthcoming botnet research
endeavors.
Abstract: As emails communications have no consistent
authentication procedure to ensure the authenticity, we present an
investigation analysis approach for detecting forged emails based on
Random Forests and Naïve Bays classifiers. Instead of investigating
the email headers, we use the body content to extract a unique writing
style for all the possible suspects. Our approach consists of four main
steps: (1) The cybercrime investigator extract different effective
features including structural, lexical, linguistic, and syntactic
evidence from previous emails for all the possible suspects, (2) The
extracted features vectors are normalized to increase the accuracy
rate. (3) The normalized features are then used to train the learning
engine, (4) upon receiving the anonymous email (M); we apply the
feature extraction process to produce a feature vector. Finally, using
the machine learning classifiers the email is assigned to one of the
suspects- whose writing style closely matches M. Experimental
results on real data sets show the improved performance of the
proposed method and the ability of identifying the authors with a
very limited number of features.