There is a rapid increase in number and variety of malware. In particular, hundreds of thousands of new malware are observed on a daily basis. This amplifies the need for automatic analysis and detection of malware. Recently, techniques based on System Call Dependency Graphs have emerged due to their promising detection rate and ease of implementation. In this paper, a new approach is proposed for malware detection. The approach is based on analysis of System Call Dependency Graphs. Dependency frequencies are considered as feature vectors to represent malware and benign behavior. Given a train set of System Call Dependency Graphs from various benign and malware families, machine learning algorithms are used to construct classification models. We try algorithms such as support vector machines, random forests and gradient boosted decision trees and train various classification models. The evaluation results demonstrate that most of these models, in comparison with other related work, have a high degree of detection rate and low false positive rate.

Most malware producers use obfuscation techniques to bypass signature-based detections. In order to provide proactive and real-time protection, the researchers have begun to develop strategies for behavior-based detection. While behavior-based detection techniques are promising solutions to this growing problem of malwares varieties, but they still suffer from high false positive rate in detecting unknown malware detection. To overcome this problem, we shall seek for identifying patterns, representing malicious intent in all instances of a malware family. In this paper, we propose a new technique, based on discriminative subGraph mining technique to identify discriminative behavioral patterns. The malicious behavioral patterns discovered by our technique from a known malware set allows the detector to reach an 94% detection rate over unknown malware with no false positives. This is a significant improvement over the 55% detection rate observed from commercial antivirus, and the 86% rate reported by the best behavior-based detector.

The knowledge Graph plays an important role in the Semantic Web and Natural Language Processing (NLP) tools. There are many knowledge bases in different languages, however lack of Farsi-specific knowledge base appears some defects in research and industrial applications. In this study, the most comprehensive knowledge base in Farsi language is presented, which consists of more than 500K of entities and 7 million relations, which is accessible in an open source repository. Data is supplied from four sources: Farsi Wikipedia and its structured data such as infoboxes, web tables, Wiki tables, and a relation extraction module. A variety of challenges of triple extraction from web tables, especially wiki tables, is addressed and some solutions to tackle these challenges are offered. According to the semantic web, RDF data model and OWL2 ontology employed to implement the Farsi Knowledge Graph (FKG). Resources and their relations are stored in triple format, therefor access to the knowledge Graph is provided by a SPARQL endpoint. The FKG consists of several main parts including triple extraction from raw text, triple extraction from structured data, knowledge base creation, a search System on the knowledge base, and an entity linking module. In this paper, overall architecture of these parts is discussed in detail. One of the major contribution of this work is mapping of the ontology to the FarsNet, the Persian WordNet, for research purposes. In this Graph, there are a large amount of information on a variety of topics including famous people, important places, organizations and companies, literary and art works, physiology, biology, events, species, astronomy, etc. For evaluation purposes, a small part of triples were randomly collected to build a test dataset for manually inspection. Experimental results demonstrate that more than 94% of triples were obtained correctly through the process of extraction, conversion, mapping, transformation and store. Future of internet according to the semantic web will be a complex and huge global knowledge base, therefor the FKG can play a significant role in developing this emerging technology.

Model checking is among the most effective techniques for automatic verification of hardware and software Systems’ properties. Generally, in this method, a model of the desired System is generated and all possible states are explored in the space state Graph to find errors and undesirable patterns. In models of large and complex Systems, if the size of the generated state space is too extensive so that not all available states can be explored due to computational restrictions, the problem of state space explosion occurs. In fact, this problem confines the validation process in model verification Systems. To use the model checking technique, the System must be described in a formal language. Graphs are very beneficial and intuitive tools for describing and modeling software Systems. Correspondingly, Graph transformation System provides a proper tool for formal description of software System features as well as their automatic verification. Various techniques have been investigated in the researches to reduce the effect of state space explosion problem in the model checking process. Some of these methods try to reduce the required memory by reducing the number of cases explored. Among others are symbolic model checking, partial-order reduction, symmetry reduction, and scenario-driven model checking. In a complex System, these algorithms, along with conventional methods such as DFS or BFS search algorithms may not afford any complete answer due to the explosion of state space. Hence, the use of intelligent methods such as knowledge-based techniques, datamining, machine learning, and meta-heuristic algorithms which do not entail full state space exploration could be advantageous. Recent researches attest that exploring the state space using intelligent methods could be a promising idea. Therefore, an intelligent method is used in this research to explore the state space of large and complex Systems. Accordingly in this paper, first a model of the desired System is created using Graph conversion System. Then a portion of the state space of the model is generated. Afterwards, using the conditional probability table, the dependencies between the rules in the paths toward the goal state are discovered. Finally, by means of the discovered dependencies, the rest of the model state space is intelligently explored. In other words, only promising paths, i. e. those who match the detected dependencies are explored to reach the goal state. It is worth noting that the first goal of the proposed approach is to find a goal state, i. e., one in which either the safety property is violated or the reachability property is satisfied in the shortest possible time. The second less important goal is to reduce the number of explored states in the Graph of the state space until reaching the goal state. This paper provides a way to check the availability feature in complex and large software Systems modeled in the official Graph transformation language. The suggested method is implemented in GROOVE which is an open source toolset for designing and model checking Graph transformation Systems. The results of experimental tests indicate that the proposed approach is faster than the previous methods and produces a shorter counterexample/witness.

In this paper, optimization of periodic inspection interval for a two-component System with failure Dependency is presented. Failure of the first component is soft, namely, it does not cause the System stop, but it increases the System operating costs. The second component’s failure is hard, i.e. as soon as it occurs, the System stops operating. Any failure of the second component increases the first component’s failure rate. Failure of the first component is only detected if inspection is performed. Thus, the first component is periodiCally inspected and if found failed, it is perfectly repaired and it is restored to as good as new. Failure of the second component is detected as soon as it occurs. Since this failure causes the System stop, it is immediately replaced. It is assumed that the time for replacement or repaired is negligible. We model the first component’s failure as a non-homogeneous Poisson process (NHPP) with increasing failure rate and the second component’s failure as a homogeneous Poisson process (HPP) with constant failure rate. The objective is to find the optimal inspection interval for the first component such that the expected total cost per unit time is minimized. A simplified numerical example along with sensitivity analysis on cost parameters is given.

The high and unreasonable demand for fuel in Iran, and also the excessive and irrational consumption of fuel in various sectors, including transportation along with cheap fuel for power generation plants, the lack of proper standards for fuel consumption in different sectors, has brought the high Dependency on fossil fuels energy supply for Iran. This problem is a serious threat to the country's energy security, in light of the increasing trend in energy demand and in particular the energy intensity of the country's energy supply. Understanding the System structure of this Dependency is the main objective of the present research. The research methodology is based on the System dynamics approach. A stock and flow model of energy Dependency was developed and four scenarios have tested on it. From the comparison of the four scenarios, it can be said that the current energy Dependency on is continuing to increase. Based on the results, it is recommended that the demand-side management policy and the development of renewable energy capacities reduce energy Dependency and increase energy security together.

Today, positioning has attracted special attention as one of the fundamental aspects of various Systems. Over time, the navigation System's error, especially in the Inertial Navigation System (INS), has significantly increased. This error increase can lead to serious issues in navigation processes, especially on long routes. To ensure the high accuracy and stability of navigation on long routes, the use of advanced and effective auxiliary Systems is necessary. The goal of this article is to present an innovative method for positioning by combining camera and inertial sensor data to enhance accuracy in this vital process. This method is particularly important in self-driving vehicles, domestic robots, and search and rescue robots operating in enclosed environments or areas without active GPS coverage. Consequently, there is a crucial need for a real-time positioning System for these Systems. In this article, we have implemented a real-time Visual-Inertial Odometry (VIO) navigation System on a PC platform, which performs significantly better than the INS System alone. In this System, cameras are used to correct the accumulated INS error, and the fusion of cameras and inertial data is done using a Graph. To validate and evaluate the System, practical data collected by the System are used, demonstrating that the proposed VIO System exhibits more than a 90% improvement compared to INS.