Today, botnets have become a serious threat to enterprise networks. By creation of network of bots, they launch several attacks, distributed denial of service attacks (DDoS) on networks is a sample of such attacks. Such attacks with the occupation of system resources, have proven to be an effective method of denying network services. Botnets that launch HTTP packet FLOOD attacks against Web servers are one of the newest and most troublesome threats in networks. In this paper, we present a system called HF-Blocker that detects and prevents the HTTP FLOOD attacks. The proposed system, by checking at the HTTP request in three stages, a Java-based test, check cookies and then check the user agent, detects legitimate source of communication from malicios source, such as botnets. If it is proved the source of connection to be bot, HF-Blocker blocks the request and denies it to access to resources of the web server and thereby prevent a denial of service attack. Performance analysis showed that HF-Blocker, detects and prevents the HTTP-based attacks of botnets with high probability.

Bot networks are a serious threat to cyber security, whose destructive behavior affects network performance directly. Detecting of infected HTTP communications is a big challenge because infected HTTP connections are clearly merged with other types of HTTP traffic. Cybercriminals prefer to use the web as a communication environment to launch application layer attacks and secretly engage in forbidden activities, while TLS (Transport Layer Security) protocols allow encrypted communication between client and server in the context of Internet provides. Methods of analyzing traffic behavior do not depend on payloads. This means that they can work with encrypted network communication protocols. Traffic behavior analysis methods do not depend on package shipments, which means they can work with encrypted network communication protocols. Hence, the analysis of TLS and HTTP traffic behavior has been considered for detecting malicious activities. Because of the exchange of information in the network context is very high and the volume of information is very large, storing and indexing of this massive data require a Big data platform.

Paleoclimatic investigation of the Quaternary period reflects fluctuation in climatic conditions during geological periods. Abrupt climatic changes, viewed as problem causing global incidents bring about adverse side effects in sensitive climatic zones such as Iran. Droughts and unpredictable FLOODs that damage natural resources as well as human life become common yearly occurrences. Apart from influences of natural physical factors, human activities, if not properly controlled, add to the destructive power of the FLOODs too. This happened in the Nekaroud basin mainly due to deforestation and damaging changes in landuse during the last century. The huge magnitude FLOOD of summer 1999 which caused irreparable downstream damage and death of citizens is a sad incident of such environmental changes. The present study is an investigation into some characteristics of Nekaroud basin, an analysis of the reasons behind the occurrence of the catastrophic FLOOD, ways to predict and prevent similar disastrous incidents in other catchments basins in Mazandaran as well as in other regions with similar conditions in Iran.

This study aimed to investigate the entrepreneurial intention among students of Bu-Ali Sina University in Hamedan and PNU surveys have been conducted within the overall framework. The sample consisted of 370 male and female students in agricultural disciplines in undergraduate students formed the College of Agriculture. To collect information, a questionnaire and a simple random sampling was used to select the sample. Cronbach's alpha coefficient to determine the reliability coefficient was calculated to vary from 850 students' entrepreneurial intentions are good. To analyze the data, descriptive and inferential statistical methods were used. The results showed that in general students of entrepreneurship spirit and intent of the high entrepreneurial intentions are more male students. The results of the analysis showed a correlation between gender, employment situation, risk appetite and economic barriers in interest income and a significant positive correlation with their entrepreneurial intentions can be seen. As well as the entrepreneurial intentions Mnadray difference was observed between the universities.

Among the various natural hazards, FLOODs may be considered as the most devastating factor that inflicts great damage on human societies. Therefore, the importance of estimating FLOOD damage and its scope in planning to reduce damages and determine points with high risk is very important. The aim of this study is to determine the extent of FLOOD hazard using OLI satellite data. For this reason, a window of OLI satellite images of Landsat 8 was acquired before and after the Dezful FLOOD of April 25, 2016. First, preprocessing operations include radiometric and atmospheric corrections of images were done, and the principal component analysis was then used to reduce the correlation of the data. Data processing was performed using a Support Vector Machine algorithm with linear and polynomial kernels. In order to train the Support Vector Machine algorithm, training samples for each class (agricultural land, FLOOD extent, water resources, settlement areas, and recreational areas along the river boundary) were harvested at the user level. In order to evaluate the similarity of the classes and the degree of correlation between the samples, the quantitative assessment method of the Jeffries Matusita was performed. The results showed that the FLOOD area was 11593. 26 ha, the highest damage was due to agricultural land with a destruction of 8467. 45 ha and recreational and tourist areas along the riverbank with a destruction of 2659. 14 ha.

Natural disasters threatening and endangering human communities has resulted in the study and research of such disasters through the related sciences and present methods of forecasting their behavior with time and place and also from a qualification and quantity viewpoint. To this end, numerous methods for the determination of the maximum FLOOD in various return periods has been made available which can be refered to as FLOOD frequency analysis methods. One of these methods is the regional FLOOD frequency analysis in which instead of using the data from a single station, it considers the data and characteristics of a group of similar stations. In the case under the research this method uses L-Moments and Index FLOOD in North, Razavi and South Khorasan water basins and MATLAB software. Maximum annual FLOOD statistics were used from 68 Hydrometric stations with minimum and maximum statistical periods of 6 and 39 years. Using Cluster analysis the region under study was divided to 7 partitions. Discordance test has conducted and only one station in region C was found as discordance station. Because of knowing the homogeneity of the regions, the parameter of Kappa distribution were estimated and with using the simulation method of Monte Carlo with 500 times, the homogeneity measure was tested in 7 regions. Using homogeneity test all regions was found homogen. Using goodness-of-fit measure z and Kolmogrove-Smirnov the Log normal 3 parameters distribution were selected for two regions of A and B, GEV for C, Generalized Pareto for D and E, Generalized logistic for F and Pearson III for G. Besides, GEV distribution was found appropriate for all of the regions, only their parameters are different in any regions. For estimating of index FLOOD a logarithmic model has found for each region with 4 variables of area, height, average slop and form factor. Using of these models, the index FLOOD can be estimated in each region and it can be used for standardize the statistics of maximum FLOOD values.