Today unauthorized access to sensitive information and cybercrimes is rising because of increasing access to the Internet. Improvement in software and hardware technologies have made it possible to detect some attacks and anomalies effectively. In recent years, many researchers have considered flow-based approaches through machine learning algorithms and techniques to reveal anomalies. But, they have some serious defects. By way of illustration, they require a tremendous amount of data across a network to train and model network’s behaviors. This problem has been caused these methods to suffer from desirable performance in the learning phase. In this paper, a technique to disclose intrusions by Support Vector Regression (SVR) is suggested and assessed over a standard dataset. The main intension of this technique is pruning the remarkable portion of the dataset through mathematics concepts. Firstly, the input dataset is modeled as a Directed Graph (DG), then some well-known features are extracted in which these ones represent the nature of the dataset. Afterward, they are utilized to feed our model in the learning phase. The results indicate the satisfactory performance of the proposed technique in the learning phase and accuracy over the other ones.

Nowadays with improvement in computer science, distributed systems have attracted remarkable attention and increasingly becoming an indispensable factor in our life. Massive-scale data processing, weather forecasting, industrial control systems, medical science, multi-tire architectures in enterprise applications, and aerospace to name but a few are the cases in point that distributed systems play a notable role. Inter-Process Communication or in a short form, IPC is specified as the heart of all distributed systems, therefore they are not formed without IPC. Numerous methods concerning IPC have been proposed so far that are utilized in diverse circumstances. According to the physical location of communication processes in applications, IPC could be established among either multiple processes on the same computer or several computers across a network. From the communication pattern’s perspective, these IPCs can be classified into two broad groups namely, shared memory and message passing. Although, it is not true to say when processes are performed on the same computer definitely employ shared memory to communicate if processes are executed on the different systems they inevitably communicate through message passing. By way of illustration, pipes use message passing patterns to make a connection between various processes but all of the processes are carried out on the same system. The aim of this research is to depict a categorization of the some IPC methods, give a brief description of them, and assess their performance in terms of transferring rate by sending multiple files in different sizes between client and server. As we expected, socket as the basic IPC, since it does not perform extra operations on the input data to be sent had a desirable performance compared to others. Although, to achieve some of the capabilities, like eliminating platform dependencies and asynchronous communication, it needs to add additional layers that make poor performance.

Graphs are widely used to model complicated structures and link them with each other. Some of such structures are XML documents, social networks, and computer networks. Information and model extraction from graph databases is a graph mining process. Efficient query search in graph databases, known as query processing, is one of the heated debates in the field of graph mining. One of the query processing techniques is sequential search over the whole dataset and isomorphism test on all sub-graphs in the database, which is not an optimal technique as to response time and storage. This problem brought in the issues of indexing graph databases to improve query processing performance. As the method implies, part of the database where the answer is expected to be found there is pruned and the number of needed isomorphism tests decreases. It might not be easy to compare the methods and techniques of graph query techniques as different techniques have different objectives. For instance, similarity search techniques reduce query time, while they cannot compete with exact matching techniques as to accuracy and vice versa. Input data volume might be also effective on query time as with immense datasets, similarity search techniques are more preferred than exact matching techniques. The present study is a survey of graph query processing techniques with emphasis on similarity search and exact matching.

Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.