Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, artificial intelligence (AI) techniques are used. Traditional machine learning/deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we utilized the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Three Hoeffding trees classifier algorithms are selected to be applied to the Resilient Information Systems Security (RISS) research group dataset. After configuration, Massive Online Analysis (MOA) software is utilized as a testing framework. The results of Hoeffding tree classifier algorithms are then assessed to choose the enhanced model with the highest accuracy and latency performance. In conclusion, the 99.41% classification accuracy was the highest result achieved by the EFDT algorithm in 66 ms.

A graph is a popular data model that has become pervasively used for modeling structural relationships between objects. In practice, in many real-world graphs, the graph vertices and edges need to be associated with descriptive attributes. Such type of graphs are referred to as attributed graphs. G-SPARQL has been proposed as an expressive language, with a centralized execution engine, for querying attributed graphs. G-SPARQL supports various types of graph querying operations including reachability, pattern matching and shortest path where any G-SPARQL query may include value-based predicates on the descriptive information (attributes) of the graph edges/vertices in addition to the structural predicates. In general, a main limitation of centralized systems is that their vertical scalability is always restricted by the physical limits of computer systems. This article describes the design, implementation in addition to the performance evaluation of DG-SPARQL, a distributed, hybrid and adaptive parallel execution engine of G-SPARQL queries. In this engine, the topology of the graph is distributed over the main memory of the underlying nodes while the graph data are maintained in a relational store which is replicated on the disk of each of the underlying nodes. DG-SPARQL evaluates parts of the query plan via SQL queries which are pushed to the underlying relational stores while other parts of the query plan, as necessary, are evaluated via indexless memory-based graph traversal algorithms. Our experimental evaluation shows the efficiency and the scalability of DG-SPARQL on querying massive attributed graph datasets in addition to its ability to outperform the performance of Apache Giraph, a popular distributed graph processing system, by orders of magnitudes.

The original version of this article unfortunately contained a mistake in the acknowledgement statement.