Botnets have become one of the most serious threats on the Internet. On the platform of botnets, attackers conduct series of malicious activities such as distributed denial-of-service (DDoS) or virtual currencies mining. Network traffic has been widely used as the data source for the detection of botnets. However, there are two main issues on the detection of botnets with network traffic. First, many traditional filtering methods such as whitelisting are not able to process the very large amount of traffic data in real-time due to their limited computational capability. Second, many existing detection methods, based on network traffic clustering, result in high false positive rates. In this work, we are motivated to resolve the above two issues by proposing a lightweight botnet detection system called BotCapturer, based on two-layered analysis with anomaly detection in graph and network communication traffic clustering. First, we identify anomalous nodes that correspond to C&C (Control and Command) servers with anomaly scores in a graph abstracted from the network traffic. Second, we take advantage of clustering algorithms to check whether the nodes interacting with an anomalous node share similar communication pattern. In order to minimize irrelevant traffic, we propose a traffic reduction method to reduce more than 85% background traffic. The reduction is conducted by filtering the packets that are unrelated to the hosts like C&C server. We collect a very big dataset by simulating five different botnets and mixing the collected traffic with background traffic obtained from ISP. Extensive experiments are conducted and evaluation results based on our own dataset show that BotCapturer reduces more than 85% input raw packet traces and achieves a high detection rate (100%) with a low false positive rate (0.01%), demonstrating that it is very effective and efficient in detecting latest botnets.

No abstract

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the system outage probability and throughput are derived in Rician channel fading. Monte Carlo Simulation method is used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters on the system performances are discussed via simulations, which show that the system outage probability increases with the increase of the minimum transmission rate required by the users and also decreases with the increase of the energy conversion efficiency. Besides, the system throughput increaseswith the increment of the transmit power of the source node, as well as the energy conversion efficiency. Additionally, the outage performance of the system with the equal two-hop distance is better than that of the system with unequal two-hop distance.

No abstract

Background Epilepsy is a neurological disorder caused by abnormal brain discharges. In recent years, genome-wide expression studies (GES) have been conducted using human patient samples and experimental models of epilepsy. Numerous molecular targets for epileptogenesis and treatment have been identified. However, there is still an urgent need to identify new biomarkers for epilepsy. Methods We performed a meta-analysis and convergence analysis of GES available from human samples and mouse and rat models to identify differentially expressed genes. Functional and pathway enrichment analysis of differentially expressed genes was performed using Sangerbox 3.0. Protein–protein interaction networks were mapped using STRING and subsequently visualized using Cytoscape. Disease gene–drug interactions were explored using the Comparative Toxicology Database and the Drug Gene Interaction Database. Results The ten most highly differentially expressed genes were, LRRTM1, STX1A, SNAP25, SYNPR, STXBP1, NRXN1, CPLX1, SYT4, SYT13 and GABRG2, which were supported by multi-lineage genomic evidence. A functional enrichment analysis identified several important classifications, including regulation of neuronal differentiation and regulation of vesicle-mediated transport. Potential druggable genes were also identified (e.g., LRRTM1, GABRG2, CYP4X1, CHGB and TMEM130). Conclusions We performed a meta-analysis and convergence analysis of GES from human samples and multiple animal models of epileptogenesis by integrating the results of several studies. We identified the top ten candidate genes and pathways for epileptogenesis. Among them, LRRTM1 and GABRG2 have been validated to have high druggable value. The other eight genes require further study to explore their potential as therapeutic targets in medial temporal lobe epilepsy with hippocampal sclerosis.