One of the serious threats to cyberspace is the Bot Networks or Botnets. Bots are malicious software that acts as a network and allows hackers to remotely manage and control infected computer victims. Given the fact that DNS is one of the most common protocols in the network and is essential for the proper functioning of the network, it is very useful for monitoring, detecting and reducing the activity of the Botnets. DNS queries are sent in the early stages of the life cycle of each Botnet, so infected hosts are identified before any malicious activity is performed. Because the exchange of information in the network environment and the volume of information is very high, Storing and indexing this massive data requires a large database. By using the DNS traffic analysis, we try to identify the Botnets. We used the data generated from the network traffic and information of known Botnets with the Splunk platform to conduct data analysis to quickly identify attacks and predict potential dangers that could arise. The analysis results were used in tests conducted on real network environments to determine the types of attacks. Visual IP mapping was then used to determine actions that could be taken. The proposed method is capable of recognizing known and unknown Bots.

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.

While huge investment in infrastructural plans is an urgent need for developing countries, they are not able to provide sufficient capital to fund these projects. One of the new ideas to solve this problem is to use build - operate - transfer (Bot) contracts. This method enables the private sector to participate in public, infrastructural projects without being the permanent owners thereof. The Iranian government has recently shown interest in using Bot contracts for the purpose of privatization, foreign investment attraction, access to advanced technology and technical skills, and benefiting from efficient management. As an example, paragraph 14(6) of the Economic Improvement Plan of the Islamic Republic of Iran, promulgated by the President last year, has emphasized the entry and attraction of foreign investment through Bot contracts.The paper attempts first to study the mechanism of such contracts and then to distinguish it from similar mechanisms while explaining Bot contracts characteristics in order to facilitate their application in infrastructural projects and in privatization, technology and know-how attraction and access to efficient management.

The aim of this thesis is identifying and modeling the risks of the power plant Bot projects. Main identified risks in this study are project financing risk (equity ratio risk) and the risk of revenue of project. In order to model the risks, we used the Martingale Variance Model (MVM) for the revenue risk and the Triangular distribution function for the equity ratio risk. We applicated the Monte Carlo simulations method for obtaining the probability distribution function and critical values of the decision index (Net Present Value, Internal Rate of Return, Debt Service Coverage Ratio). The one of thermal power plant projects data prepared by MAPNA, has been implemented in this study. The results of the simulation indicate that the risk of negative NPV of project is 13.41 percent and the risk of DSCR lower than 1.2 is 8.65 percent. Therefore, the sponsors suffering more risks than lenders in the studied project.

We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio Networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle Both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on Both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.

Botnets are recognized as one of the most dangerous threats to the Internet infrastructure. They are used for malicious activities such as launching distributed denial of service attacks, sending spam, and leaking personal information. Existing Botnet detection methods produce a number of good ideas, but they are far from complete yet, since most of them cannot detect Botnets in an early stage of their lifecycle; moreover, they depend on a particular command and control (C&C) protocol. In this paper, we address these issues and propose an online unsupervised method, called BotOnus, for Botnet detection that does not require a priori knowledge of Botnets. It extracts a set of flow feature vectors from the network traffic at the end of each time period, and then groups them to some flow clusters by a novel online fixed-width clustering algorithm. Flow clusters that have at least two members, and their intra-cluster similarity is above a similarity threshold, are identified as suspicious Botnet clusters, and all hosts in such clusters are identi ed as Bot infected. We demonstrate the effectiveness of BotOnus to detect various Botnets including HTTP-, IRC-, and P2P-based Botnets using a testbed network. The results of experiments show that it can successfully detect various Botnets with an average detection rate of 94: 33% and an average false alarm rate of 3: 74%.