SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string.

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The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing.The solution consists of the following three layers: (a) data cleaning layer -improves quality of the data by smoothing it and by filling in missing states in short sequences referred to as tracks that represent a composite state of a moving object in a spatiotemporal subspace followed by one camera. (b) Data integration layer -assigns a global identity to tracks that represent the same object. (c) Persistence layermanages the metadata in a database so that it can be used for online identification and offline querying, analyzing and mining. A Kalman filter technique is used to solve (a) and a classification based on the moving object's state and its visual properties is used in (b). An object model for layer (c) is presented too.18th International Workshop on Database and Expert Systems Applications 1529-4188/07 $25.00

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