Abstract. We identified 480 persons with positive thick smears for asexual Plasmodium falciparum parasites, of whom 454 had positive rapid diagnostic tests (RDTs) for the histidine-rich protein 2 (HRP2) product of the hrp2 gene and 26 had negative tests. Polymerase chain reaction (PCR) amplification for the histidine-rich repeat region of that gene was negative in one-half (10/22) of false-negative specimens available, consistent with spontaneous deletion. False-negative RDTs were found only in persons with asymptomatic infections, and multiplicities of infection (MOIs) were lower in persons with false-negative RDTs (both P < 0.001). These results show that parasites that fail to produce HRP2 can cause patent bloodstream infections and false-negative RDT results. The importance of these observations is likely to increase as malaria control improves, because lower MOIs are associated with false-negative RDTs and false-negative RDTs are more frequent in persons with asymptomatic infections. These findings suggest that the use of HRP2-based RDTs should be reconsidered.

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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.

-How to find and detect novel o r unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on network is presented and four genetic operators namely reproduction, mutation, crossover and dropping condition operators a r e used to evolve new rules. New rules are used to detect novel o r known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that the rule generated by GP has a low false positive rate (FPR), a low false negative rate (FNR) and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate (DR) with low false alarm rate (FAR).

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