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.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the Traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, Traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' Traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

Analyzing motion patterns in Traffic videos can be employed directly to generate high-level descriptions of their content. For Traffic videos captured from intersections, usually, we can easily provide additional information about Traffic phases. Such information can be obtained directly from the Traffic lights or through Traffic lights controllers. In this paper, we focus on incorporating additional information to analyze the Traffic videos more efficiently. Using side information on Traffic phases, the semantic of motion patterns from Traffic intersection scenes can be learned more effectively. The learning is performed based on optical flow features extracted from training video clips, and applying them to supervised topic models such as MedLDA and MedSTC. Based on such models, any video clip can be represented based on the learned patterns. Such representations can be further exploited in scene Analysis, rule mining, abnormal event detection, etc. Our experiments show that employing side information in intersection video Analysis leads to improvement in discovering scene pattern. Moreover, supervised topic models achieve about 4% improvement in abnormal event detection, compared to the unsupervised ones, in terms of area under ROC.

Fast and accurate network Traffic identification is becoming essential for network management, high quality of service control and early detection of network Traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a method to identify network Traffics. In this method, for cleaning and preparing data, we perform effective preprocessing approach. Then effective features are extracted using the behavioral Analysis of application. Using the effective preprocessing and feature extraction techniques, this method can effectively and accurately identify network Traffics. For this purpose, two network Traffic databases namely UNIBS and the collected database on router are analyzed. In order to evaluate the results, the accuracy of network Traffic identification using proposed method is analyzed using machine learning techniques. Experimental results show that the proposed method obtains an accuracy of 97% in network Traffic identification.

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.

Today, one of the main challenges of Traffic is affected by pedestrians, that the different behaviors of people have a direct effect on Traffic parameters. The main goal of this research is to improve and improve Traffic flow and improve Traffic for vehicles and pedestrians.  For this purpose, a case study was conducted on the behavior of pedestrians at the level intersection First of all, the Traffic statistics of pedestrian and vehicular Traffic were collected in the eight crossings of this intersection in the mentioned time periods, and by using the Traffic simulation for vehicular and pedestrian Traffic in this intersection, as well as considering and applying behavioral parameters and calibration for vehicles and for pedestrians in  considered  In order to identify the effect of pedestrian behavior and match it with reality, three scenarios were considered in the case study, in which the first scenario is related to the simulation of the intersection without considering the pedestrian Traffic, the second scenario is the simulation of the intersection with the consideration of the pedestrian Traffic and  Finally, the third scenario is the real behavior of people by simulating it in viswalk, considering the pedestrian Traffic.  The results showed that the quality of Traffic parameters in scenario 2 and 3 compared to scenario 1 has decreased by 13.5 and 25% respectively for the total speed of vehicles and increased by 21 and 38% for density, in fact the effect of Traffic interference and pedestrian behavior  Pedestrians and riders indicate Traffic flow parameters.

Corsim is microscopic simulation software in Traffic engineering with high capability in analyzing urban networks. Nowadays, the use of simulation software is a fundamental way to solve Traffic problems. Applying simulation software is very economic, yet the results are obtained in very short time without any Traffic disturbances, whereas using field tests, in addition to being expensive, disturbs Traffic flow. In this research, capacity Analysis and flow optimization of Dastghaib Street, between Ostad Moin Boulevard and Ayatollah Saidi highway, is analyzed by Corsim in Tehran. Input data was taken by municipal GIS files and field investigations. Simulation of the network was proposed in morning and afternoon peak hours. The results showed that when Dastghaib Street was made one-way, total network delays were decreased 7.3 percent and 4.9 percent in the morning and afternoon peak hours, respectively. Also, total travel time decreased from 124 hours to 120 hour, while average speed increased from 20 km/h to 22 km/h in morning peak hours.