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Botnets have become one of the major threats on the Internet for serving as a vector for carrying attacks against organizations and committing cybercrimes. They are used to generate spam, carry out DDOS attacks and click-fraud, and steal sensitive information. In this paper, we propose a new approach for characterizing and detecting botnets using network traffic behaviors. Our approach focuses on detecting the bots before they launch their attack. We focus in this paper on detecting P2P bots, which represent the newest and most challenging types of botnets currently available. We study the ability of five different commonly used machine learning techniques to meet on line botnet detection requirements, namely adaptability, novelty detection, and early detection. The results of our experimental evaluation based on existing datasets show that it is possible to detect effectively botnets during the botnet Command-and Control (C&C) phase and before they launch their attacks using traffic behaviors only. However, none of the studied techniques can address all the above requirements at once.

In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user‐generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n‐gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state‐of‐the‐art methods.

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Poor ambient air quality is associated with increased morbidity and mortality, including respiratory infections. However, its effects on various host-defense mechanisms are poorly understood. This study utilized an in vitro model to study the effect of particulate matter (PM(2.5)) on one antimicrobial mechanism of host defense in the airway, beta-defensin-2 and its bovine homologue, tracheal antimicrobial peptide (TAP) induction in response to lipopolysaccharide (LPS) and IL-1beta. Our model utilized cultured primary bovine tracheal epithelial (BTE) cells and the human alveolar type II epithelial cell line, A549, treated with 0-20 microg/cm(2) residual oil fly ash (ROFA) for 6 h. The cells were then washed and stimulated for 18 h with 100 ng/ml LPS or for 6 h with 100 ng/ml IL-1beta. ROFA inhibited the LPS-induced increase in TAP mRNA and protein without inducing significant cytotoxicity. As little as 2.5 microg/cm(2) of ROFA inhibited LPS-induced TAP gene expression by 30%. The inhibitory activity was associated with the soluble fraction and not the washed particle. The activity in the leachate was attributed to vanadium, but not nickel or iron. SiO(2) and TiO(2) were utilized as controls and did not inhibit LPS induction of TAP gene expression in BTE. ROFA also inhibited the increase of IL-1beta-induced human beta-defensin-2, a homologue of TAP, in A549 cells. The results show that ROFA, V(2)O(5), and VOSO(4) inhibit the ability of airway epithelial cells to respond to inflammatory stimuli at low, physiologically relevant doses and suggest that exposure to these agents could result in an impairment of defense against airborne pathogens.

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