We present Causal Active Inference with Real-time Interventions (CAIRN), a framework that models enterprise telemetry as a probabilistic causal graph and performs real-time Bayesian inference to estimate likely attack causes behind observed anomalies. When evidence is ambiguous, CAIRN triggers targeted interventions (e.g., decoy services, temporary isolation, or focused probes) to produce discriminative observations and reduce uncertainty. This closes the loop between detection and response: CAIRN tests competing hypotheses instead of flooding analysts with correlation-only alerts. In a testbed evaluation, CAIRN reduces false alarms and confirms attacks faster than a passive IDS baseline while maintaining low operational overhead.

In today’s technologically advanced society, online services are rapidly expanding, with a growing emphasis on customer satisfaction. To enhance the value of cloud services for users, it is essential to provide relevant and authentic recommendations. To address this requirement, our model DeepHaB-MMF integrates an automated recommendation system with advanced contextual and sequential embedding, designed to handle multilingual inputs. The first phase of our model is DeepHaB, which processes the user reviews by generating embedding that combine contextual information with extracted features. These embedding are then passed through a deep BiLSTM network to capture the bidirectional dependencies in the data. An attention mechanism further enhances the process by highlighting the most informative features which further help in classifying the truthful or fake reviews. These results are then provided with other extracted features to the next phase of the model, i.e., DeepMMF which modifies the traditional matrix factorization technique to provide relevant recommendations. Thus our model, DeepHaB-MMF first filters out the fake review and based on only truthful reviews it provides authentic as well as relevant recommendations. This model is evaluated on three low resource languages like Hindi, Marathi and Bengali and the results clearly shows that it out performs other state-of-the-art approaches.

Detecting potential data breaches in software systems can be effectively achieved through language-based information flow security analysis. Significant risk to confidential data, potentially leading to security breaches, is posed because of poor coding practices in database applications. To tackle the issue, our paper presents a dependency graphbased approach that identifies potential confidentiality leaks in software code. For a variety of software engineering tasks, including information flow security analysis, debugging, code optimization, enabling code reuse, and enhancing program comprehension, Dependency information plays an essential role. Existing dependency information techniques fail to generate precise results. Through the utilisation of syntaxbased dependency analysis, we enhance and improvise on existing techniques by introducing a new Dependency, which can result in indirect information leakage. A precise dependency information, which improves the accuracy of security analysis results, is provided through this refinement. We provide experimental results on benchmark codes to validate our approach. To determine illegal information flows, we use propositional formulae for the security-level truth assignments. Thorough utilisation of such an approach aids in bridging the gap between existing approaches and future applications to handle the same.

We are constantly exposed to the extensive usage of online applications in our daily lives. The web application's backend uses database technology that stores and processes sensitive data. One of the primary concerns of a web application in terms of data security is to safeguard sensitive data in the database. SQL injection attacks are one of the most serious security concerns of web applications (SQLIA). Akamai report suggests that SQLIAs accounted for more than 72% of all web application security attacks in the last five years. Therefore, SQLIA is one of the most severe attacks used against database-driven web applications, which compromises data privacy. It is a code injection type attack where an attacker injects malicious SQL queries to get unauthorised access to the database. Several research proposals have been published to address these security threats. In this paper, we first provide the current state-of-the-art on SQLIA and a comprehensive analysis of SQLIA vulnerabilities, detection and prevention strategies in the literature, as well as a complete comparison evaluation of the various existing methodologies.

Dependency information (data- and/or control-dependencies) among program variables and program statements is playing crucial roles in a wide range of software-engineering activities, e.g., program slicing, information flow security analysis, debugging, code-optimization, code-reuse, code-understanding. Most existing dependency analyzers focus on mainstream languages and they do not support database applications embedding queries and data-manipulation commands. The first extension to the languages for relational database management systems, proposed by Willmor et al. in 2004, suffers from the lack of precision in the analysis primarily due to its syntax-based computation and flow insensitivity. Since then no significant contribution is found in this research direction. This paper extends the Abstract Interpretation framework for static dependency analysis of database applications, providing a semantics-based computation tunable with respect to precision. More specifically, we instantiate dependency computation by using various relational and non-relational abstract domains, yielding to a detailed comparative analysis with respect to precision and efficiency. Finally, we present a prototype$\sf{ semDDA}$, asemantics-basedDatabaseDependencyAnalyzer integrated with various abstract domains, and we present experimental evaluation results to establish the effectiveness of our approach. We show an improvement of the precision on an average of 6 percent in the interval, 11 percent in the octagon, 21 percent in the polyhedra and 7 percent in the powerset of intervals abstract domains, as compared to their syntax-based counterpart, for the chosen set of Java Server Page (JSP)-based open-source database-driven web applications as part of the GotoCode project.