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.

With the widespread use of Android smartphones, the Android platform has become an attractive target for cybersecurity attackers and malware authors. Meanwhile, the growing emergence of zero-day malware has long been a major concern for cybersecurity researchers. This is because malware that has not been seen before often exhibits new or unknown behaviors, and there is no documented defense against it. In recent years, deep learning has become the dominant machine learning technique for malware detection and could achieve outstanding achievements. Currently, most deep malware detection techniques are supervised in nature and require training on large datasets of benign and malicious samples. However, supervised techniques usually do not perform well against zero-day malware. Semi-supervised and unsupervised deep malware detection techniques have more potential to detect previously unseen malware. In this paper, we present MalGAE, a novel end-to-end deep malware detection technique that leverages one-class Graph neural networks to detect Android malware in a semi-supervised manner. MalGAE represents each Android application with an Attributed Function Call Graph (AFCG) to benefit the ability of Graphs to model complex relationships between data. It builds a deep one-class classifier by training a stacked Graph autoencoder with Graph convolutional layers on benign AFCGs. Experimental results show that MalGAE can achieve good detection performance in terms of different evaluation measures.

Despite several studies and attempts, in time-memory trade-off attacks on cryptoGraphic algorithms, the coverage of Hellman tables and similar methods are practiCally much less than half and their probability of success is low. In fact, Hellman chains are paths with given starting and end vertices on a Functional Graph. In this paper, behavior of these chains is investigated with this approach. In the beginning of the paper, parameters of the Functional Graph for a random mapping are defined and based on these parameters, Hellman chains are analyzed. Our results show that the coverage of such tables can’t be high, for the following reasons: First, there exist some remarkable terminal vertices (37%) on the Functional Graph such that the possible occurrence of these vertices on chains (except in the starting vertices) is zero. Secondly, appropriate parameters for constructing chains exist in Graph for about half of all hidden states of cipher Function. Thirdly, for construction of noncyclic chains and collision of chains, we must pay attention to the obtained probabilities in this note. PractiCally, above reasons show that after some point the coverage of a Hellman table tends to zero quickly, and so construction of them will be ineffective. Our results are implemented on mAES algorithm where validate our theatrical results.

Researchers have always been interested in Graph nodes clustering based on content or structure. But less attention has been paid to clustering based on both structure and content. But a content-structural clustering is needed in information networks like social networks. In this paper, the ICS-Cluster algorithm is proposed which takes into consideration both the structure and content aspects of the nodes. The purpose of this approach is to gain a coherent internal structure (structural aspect) and homogeneous attribute values (content aspect) in the Graph. In this approach firstly the Graph is converted into a content-structural Graph which edges' weight show similarity between the connected nodes. Incremental clustering is done based on edges’ weight in this process the edges with the most weight is considered as clusters then the weight of connected edge to the cluster is aggregated and they’ ll be one edge, the process is repeated until the algorithm reaches the number of clusters that indicated by the user. ICS-Cluster algorithm number of cluster is indicated by the user. Comparing ICS-Cluster with other content structural algorithm based on six criteria for measuring cluster quality shows that ICS-Cluster has good performance. These criteria contain structural criteria (Modularity, Error Link, and Density), content criterion (Average Similarity), content-structural criterion (CS-Measure) and the run time.

Relevance feedback approaches is used to improve the performance of content-based image retrieval systems. In this paper, a novel relevance feedback approach based on similarity measure modification in an X-ray image retrieval system based on fuzzy representation using fuzzy Attributed relational Graph (FARG) is presented. In this approach, optimum weight of each feature in feature vector is calculated using similarity rate between query image and relevant and irrelevant images in user feedback. The calculated weight is used to tune fuzzy Graph matching algorithm as a modifier parameter in similarity measure. The standard deviation of the retrieved image features is applied to calculate the optimum weight. The proposed image retrieval system uses a FARG for representation of images, a fuzzy matching Graph algorithm as similarity measure and a semantic classifier based on merging scheme for determination of the search space in image database. To evaluate relevance feedback approach in the proposed system, a standard X-ray image database consisting of 10000 images in 57 classes is used. The improvement of the evaluation parameters shows proficiency and efficiency of the proposed system.