Masoud Nosrati

Detection of circular shapes from impulse noisy images using median and laplacian filter and Circular Hough Transform

Shapes detection from noisy backgrounds is one of the hot topics in the field of image processing and patter recognition. This paper investigates a new method which uses different filters like median and laplacian filter for reducing the effect of noise in the image and applies the canny edge detection algorithm. As the final step, a special form of Hough transform which is called Circular Hough Transform will be applied to detect the circular shapes in the image. To prove the efficiency of proposed method, results of implementation in real world use is presented. Results clearly show the good performance of it. This method is absolutely useful in the fields like: building extraction from aerial satellite images, digital filming, biometric security, astronomy, etc.

Heuristic metamorphic malware detection based on statistics of assembly instructions using classification algorithms

The competition between malware creators and those who work on malware detection, led to emergence and development of multifarious techniques for both creation and detection. In recent years, metamorphic malwares have become a serious challenge for antivirus programmers. Signature and heuristic based techniques cannot offer plenary solutions for detection of metamorphic malwares; because such those malwares can reconstruct from generation to generation without destruction in their functions. It caused difficulty in detecting them. In this research, we introduce a new technique for detecting the unknown malwares based on the counting the assembly instructions. Statistics which are obtained from analysis of different variables of a specific malware can be utilized as a signature. Also, accuracy, efficiency and fast performance must be considered as important issues. So far, lack of some of these features is seen in almost all the suggested methods. But, in the proposed method, speed is not a challenging issue, since the extraction of statistics from assembly codes is a very fast process. Experiments on several malwares and harmless programs indicated the excellence of this method rather than previous studies.

Latency Optimization for Resource Allocation in Cloud Computing System

Recent studies in different fields of science caused emergence of needs for high performance computing systems like Cloud. A critical issue in design and implementation of such systems is resource allocation which is directly affected by internal and external factors like the number of nodes, geographical distance and communication latencies. Many optimizations took place in resource allocation methods in order to achieve better performance by concentrating on computing, network and energy resources. Communication latencies as a limitation of network resources have always been playing an important role in parallel processing (especially in fine-grained programs). In this paper, we are going to have a survey on the resource allocation issue in Cloud and then do an optimization on common resource allocation method based on the latencies of communications. Due to it, we added a table to Resource Agent (entity that allocates resources to the applicants) to hold the history of previous allocations. Then, a probability matrix was constructed for allocation of resources partially based on the history of latencies. Response time was considered as a metric for evaluation of proposed method. Results indicated the better response time, especially by increasing the number of tasks. Besides, the proposed method is inherently capable for detecting the unavailable resources through measuring the communication latencies. It assists other issues in cloud systems like migration, resource replication and fault tolerance.

Metamorphic malware detection using opcode frequency rate and decision tree

Malware is defined as any type of malicious code that is the potent to harm a computer or a network. Modern malwares are accompanied with mutation characteristics, namely polymorphism and metamorphism. They let malwares to generate enormous number of variants. Rising number of metamorphic malwares entails hardship in analyzing them for signature extraction and database updates. In spite of the broad use of signature-based methods in the security products, they are not able detect the new unseen morphs of malware, and it is stemmed from changing the structure of malware as well as the signature in each infection. In this paper, a novel method is proposed in which the proportion of opcodes is used for detecting the new morphs. Decision trees are utilized for classification and detection of malware variants based on the rate of opcode frequencies. Three metrics for evaluating the proposed method are speed, efficiency and accuracy. It was observed in the course of experiments that speed and time complexity will not be challenging factors; because of the fast nature of extracting the frequencies of opcodes from source assembly file. Empirical validation reveals that the proposed method outperforms the entire commercial antivirus programs with a high level of efficiency and accuracy.

Minimization of Boolean Functions Which Include Don’t-Care Statements, Using Graph Data Structure

In this paper, we intend to introduce a heuristic algorithm to apply maximum minimization to Boolean functions with normal SOP form. To implement the proposed algorithm, we use the graph data structure and define the adjacencies. Also, we demonstrate some conditions to achieve the maximum minimization. Through this paper, the problem of shared vertices in more than one adjacency is talked, and the solution is presented. Also, don’t-care statements are considered and the way of behaving with them is explained. Karnaugh map is used to clarify the matter.

Community based replica management in distributed systems

Purpose One of the techniques for improving the performance of the distributed systems is data replication, in which the new replicas are created to provide more accessibility, fault tolerance, and lower access cost to the data. In this paper, we propose a community based solution for the management of data replication, based on the graph model of the communication latency between the computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. By this method, we aim at minimizing the latency and access cost to the data. Design/methodology/approach We utilized the Louvain algorithm for finding the best communities. In proposed algorithm, by requesting a file by the nodes of each community, the cost of accessing the file that is located out of the applicant’s community is calculated and the results are accumulated. By exceeding the accumulated costs from a specified threshold, a new replica of the file is created in the applicant’s ...