A quick-response real-time stepping stone detection scheme

Stepping stone attacks are often used by network intruders to hide their identities. To detect and block stepping stone attacks, a stepping stone detection scheme should be able to correctly identify a stepping-stone in a very short time and in real-time. However, the majority of past research has failed to indicate how long or how many packets it takes for the monitor to detect a stepping stone. In this paper, we propose a novel quick-response real-time stepping stones detection scheme which is based on packet delay properties. Our experiments show that it can identify a stepping stone within 20 seconds which includes false positives and false negatives of less than 3%.

Applications of redundancy analysis for the detection of chemical response patterns to air pollution in lichen

The lichens Ramalina celastri (Spreng.) Krog & Swinsc., Punctelia microsticta (Müll. Arg.) Krog and Canomaculina pilosa (Stizenb.) Elix & Hale were transplanted simultaneously to 17 urban-industrial sites in a northwestern area of Córdoba city, Argentina. The transplantation sites were set according to different environmental conditions: traffic, industries, tree cover, building height, topographic level, position in the block and distances from the river and from the power plant. Three months later, chlorophyll a, chlorophyll b, phaeophytin a, soluble proteins, hydroperoxy conjugated dienes, malondialdehyde concentration and sulfur accumulation were determined, and a pollution index was calculated for each sampling site. Redundancy analysis was applied to detect the variation pattern of the lichen variables that can be 'best' explained by the environmental variables considered. The present study provides information about both the specific pattern response of each species to atmospheric pollution, and environmental conditions that determine it. As regards pollutants emission sources R. celastri showed a chemical response associated mainly with pollutant released by the power plant and traffic. P. microsticta and C. pilosa responded mainly to industrial sources. Regarding environmental conditions that affect the spreading of air pollutants and their incidence on the bioindicator, the topographic level and tree cover surrounding the sampling site were found to be important for R. celastri, tree cover surrounding the sampling site and the building height affected P. microsticta, while building height did so for C. pilosa.

6 million spam tweets: a large ground truth for timely Twitter spam detection

Twitter has changed the way of communication and getting news for people's daily life in recent years. Meanwhile, due to the popularity of Twitter, it also becomes a main target for spamming activities. In order to stop spammers, Twitter is using Google SafeBrowsing to detect and block spam links. Despite that blacklists can block malicious URLs embedded in tweets, their lagging time hinders the ability to protect users in real-time. Thus, researchers begin to apply different machine learning algorithms to detect Twitter spam. However, there is no comprehensive evaluation on each algorithms' performance for real-time Twitter spam detection due to the lack of large groundtruth. To carry out a thorough evaluation, we collected a large dataset of over 600 million public tweets. We further labelled around 6.5 million spam tweets and extracted 12 light-weight features, which can be used for online detection. In addition, we have conducted a number of experiments on six machine learning algorithms under various conditions to better understand their effectiveness and weakness for timely Twitter spam detection. We will make our labelled dataset for researchers who are interested in validating or extending our work.

An embedding scheme for detecting anomalous block structured graphs

Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model.