Intrusion detection by automatic extraction of the semantics of computer language grammars

Interactions between a user and information systems are based on an inescapable architectural pattern: user data is integrated into requests whose analysis is carried out by an interpreter that drives the system’s activity. Attacks targeting this architecture (known as injection attacks) are very frequent and particularly severe. Most often, this detection is based only on the syntax of this data (e.g. the presence of keywords or sub-strings typical of attacks), with limited knowledge of their semantics (i.e. the effects of the query on the information system). The automatic extraction of these semantics is, therefore, a major challenge, as it would significantly improve the performance of Intrusion Detection Systems (IDS). By leveraging the novel advancement in Natural Language Processing (NLP) it appears feasible to automatically and transparently infer the semantics of user inputs. This Master Thesis provides a framework centred on the instrumentalization of parsers. We focused on parsers for their pivotal role as the first layer of interaction with user inputs and their responsibility for the performed operation on an information system. Our research findings indicate the possibility of constructing an intrusion detection system based on this framework. Moreover, the focus on parser technologies demonstrates the potential for dynamically preventing the processing of malicious input (i.e. creating Intrusion Prevention Systems)