Sharif Digital Repository

Cloud and distributed storage systems can provide large-scale data storage and high data reliability by adding redundancy to data. Redundant data may get lost due to the instability of distributed systems such as hardware failures. In order to maintain data availability, it is necessary to regenerate new redundant data in another node, referred to as a newcomer and this process reffered to repair process. Repair process is expected to be finished as soon as possible, because the regeneration time can influence the data reliability and availability of distributed storage systems. In this context, the general objective is to minimize the volume of actual network traffic caused by such... 

In recent years, highly recommended systems have been used in various areas. One of the approaches of these systems is a collaborative refinement that consists of three user-based, item-based, and matrix-based parsing. Matrix degradation methods are more effective because they allow us to discover the hidden features that exist between user and item interactions and help us better predict recommendations. The low-level mapping method is designed to store and process very high volume of data. In this method, after completing computations in the author’s nodes, the data is sent to the downsizing nodes, which is referred to as ”data spoofing”. It has been observed that in many applications, the... 

Network traffic classification has been an essential element for security monitoring in the network security scope and also for quality of service purposes. Every now and then, new traffic classes are added to the available groups which are unknown to the system. In an security scope, the novelties are actually the zero-day attacks which can have huge effects on the system environment. There have been many methods developed for traffic classification which are able to distinguish known traffic using signatures or learning-based methods. In a real world scenario, The primary challenge that new traffic classifiers face, is to detect novelty and separate them from the previously known labels.... 

One of the most widely used protocols used on the Internet is the SSL protocol, which is also used in many applications to exchange information between the server and the user. Therefore, the analysis of this traffic can help decision makers in many analyses. In this thesis, we are going to present a mapping for feature vectors extracted from SSL traffic that will lead to improving the performance of machine learning algorithms.In this treatise, three methods for learning mapping are proposed, all of which are based on deep learning. The first method is to use a simple self-encoder for map learning that tries to learn a compact map from the input feature vector.The second method is the... 

The complexity and non-linearity of today’s machine learning-based systems make it difficult for both end users and experts in the field to understand the logic and reasoning behind their decisions and outputs. Explainable AI (XAI) methods have gained significant attention in real-world problems as they enhance our understanding of these models, increasing trust and improving their efficiency. By applying different explanation methods on a machine learning model, the same output is not necessarily observed, hence evaluation metrics are needed to assess and compare the quality of explanation methods based on one or more definitions of the goodness of the explanation produced by them. Several... 

Nowadays, neural networks are used as the main method in most machine learning applications. But research has shown that these models are vulnerable to adversarial attacks imperceptible changes to the input of neural networks that cause the net- work to be deceived and predict incorrectly. The importance of this issue in sensitive and security applications of neural networks, such as self-driving cars and medical diagnosis systems, becomes much higher. In recent years, many researches have been done in the field of making neural net- works robust against this threat, but in most of them, higher robustness has been provided on the basis of larger and more complex models. Few researches have... 

Almost all of the network traffic classification systems use pre-defined extracted features by the experts in computer network. These features include regular expressions, port number, information in the header of different layers and statistical feature of the flow. The main problem of the traffic analysis and anomaly detection system lies in finding appropriate features. The feature extraction is a time consuming process which needs an expert to be done. It is notable that the classification of special kinds of traffic like encrypted traffic is impossible using some subset of mentioned features.The lack of integration in feature detection and classification is also another important issue... 

As application demands for online convex optimization accelerate, the need for design-ing new methods that simultaneously cover a large class of convex functions and im-pose the lowest possible regret is highly rising. Known online optimization methods usually perform well only in specific settings, e.g., specific parameters such as the diam-eter of decision space, Lipschitz constant, and strong convexity coefficient, where their performance depends highly on the geometry of the decision space and cost functions. However, in practice, the lack of such geometric information leads to confusion in using the appropriate algorithm. To address these issues, some adaptive methods have been proposed... 

By increasing the use of Internet and sharing information in this platform, servers are equipped with more robust hardware and a wider bandwidth network. But the growth of data and services is such that a server, no matter how powerful, does not have the ability to respond all users! Several servers are used to solve this problem. Data centers, content delivery networks, and so on are emerged depending on types of service. One of the important issues in these systems is how to distribute loads between servers in a proportional manner. In this thesis, we first examine the relationship between cost and response time on user requests. Cost can be considered as a communication cost or financial... 

In network science, link prediction is one of the essential tasks that has been neglected. One important application of link prediction in telecommunication networks is analyzing the user's consumption pattern to provide better service. This project aims to predict future links with applications to call data using the users' call history. In previous research, there are two main approaches: 1) heuristic-based approach, and 2) deep-learning-based approach, such as graph neural networks. These methods are mainly used for processing static graphs, and therefore, we cannot generalize them to dynamic graphs. But there are many graphs which are dynamic in nature. For instance, call data records... 

Today, we live in an information age where all people can access the vast amount of data in the world by connecting to the Internet.Since the Internet has expanded significantly to share information, some individuals and organizations seek to be able to prevent the possible sabotage of some people by monitoring network users. Analysis of computer network traffic is one of the importance issues that many activities have been done in this area. One of the most important questions in traffic analysis is to identify the main content of traffic on the encrypted network. Numerous studies have shown that the traffic of websites visited through the Tor network, including Specific information that... 

The rapid growth of computer networks has increased the importance of analytics and traffic analysis tools for these networks, and the increasing importance of these networks has increased the importance of security of these networks and the intrusion detection in these networks. Many studies aimed at providing a powerful way to quickly and accurately detect computer network intrusions, each of which has addressed this issue.The common point of all these methods is their reliance on the features extracted from network traffic by an expert. This strong dependence has prevented these methods from being flexible against new attacks and methods of intrusion or changes in the current normal... 

The representation of data influences the explanation factors of data variations. Thus,the success of learner algorithms depends on the data representation. Our main contribution in this thesis is learning of high level and abstract representation using deep structure. One of the fundamental examples of representation learning is the AutoEncoders. The auto-encoder is a rigid framework that doesn’t consider explanation factors in terms of statistical concepts. So, the auto-encoders can be re-interpreted by seeing the decoder as the statistical model of interest. The role of encoder is a mechanism for inference in the model described by the decoder. Our purpose is to design such model with... 

We live in the cyber era in which network-based technologies have become omnipresent. Meanwhile, threats and attacks are rapidly growing in cyberspace. Nowadays, some signature-based intrusion detection systems try to detect these malicious traffics. However, as new vulnerabilities and new zero-day attacks appear, there is a growing risk of bypassing the current intrusion detection systems. Many research studies have worked on machine learning algorithms for intrusion detection applications. Their major weakness is to consider the different aspects of network security concurrently. For example, continuous concept drift in normal and abnormal traffic, the permanent appearance of zero-day... 

Hi-C is a new technology invented to record the amount of interaction between all chromatin fragments. Chromatin interactions are manifestations of chromatin's 3D structure. It has been proved that different 3D structures of different cell types, despite having the same DNA, causes in different functionalities of cells. Therefore knowing chromatin structure, the factors affecting it and the way it affects cells' behaviors such as gene co-expression, sheds more light on the knowledge about cells and the methods that can change their fates. Finding the causes of diseases, designing more efficient drugs and progressing the conversion of different cell types into each other used for amending... 

Autism spectrum disorder (ASD) is a strong genetic perturbation that encompasses a wide range of clinical symptoms, including functional at different regions of the brain, repetitive behaviors, and interests, weaknesses in social relationships, some sensitivities to environmental factors and etc. Genetic complexity and the impact of environmental factors put the disease in the category of Level 1 complex developmental disorders.We proposed a pilot, combined, and highly effective structure to identify biomarkers in the autism spectrum disorder that could be extended to other diseases that have a similar genetic architecture with autism. We also develop a Gene-tissue interaction network to... 

With the rise of the concept of “Big Data”, both data volumes and data processing time increased, imposing the need for new methods of processing and computation of said data.Analytical and computational methods in Machine Learning are some of the most important applications of Big Data processing. There exist many methods of data analysis in the Machine Learning field, each requiring extensive processing on Big Data. One of the methods for working with Big Data is Distributed Systems. MapReduce is one of the most popular methods distributed computation by increasing the ease and speed of distributed processing of big data. But a number of bottlenecks have been discovered in MapReduce which...