System Call Interception Framework for Data Leak Prevention

centralized database-based Central Bank Digital Currency (CBDC) system's vulnerability to cyberattacks and data leakage is a major concern. Any data leak can lead to large financial losses, irreversible reputational harm, and a decline in user confidence. To protect user information, the Reserve Bank of India has underlined the significance of a strong Data Leakage Prevention (DLP) system. While current incidents have demonstrated that the measures were insufficient to meet the standards, DLP may not be enough to defend CBDCs on its own. Incorporating Zero-Knowledge Proofs (ZKPs) and differential privacy tools into DLP solutions can improve their robustness and effectiveness. There is no one-size-fits-all solution for preventing data leakage, different solutions may be more effective in different scenarios. It's always a good idea to assess an organization's or system's specific needs and requirements before deciding on the best solution. It is also important to remember that there is no such thing as absolute security, and the possibility of zero-day attacks is always there. It is essential to continuously monitor and enhance security measures to stay ahead of new threats. To preserve their Central Bank Digital Currency systems and data, financial institutions and central banks must continue to be proactive and vigilant.

Data leakage has become a serious problem to many organizations. To provide visibility into what data is confidential and where it's stored, many of current data leakage prevention (DLP) solutions depend on scanning file content. This approach needs the capability of parsing various file formats, but for those unsupported file formats there still exist risks of data breach. To address this issue, this study proposes an active DLP model by hooking on keyboard API to track and profile user key stroke behaviour. This has two major advantages: (1) It can discover sensitive data without parsing file formats, and (2) A data creator can be identified according to his/her key stroke behaviour. Since this model is based on key stroke profiling, it can resolve unsupported file format issue and have the capability of file creator identification.

An Improved Data Leakage Detection System is designed to mitigate the leakage of crucial and sensitive data in a cloud computing environment. Generally, leakage of data in computing system has caused a lot of irreparable damage or catastrophe to various institutions or organizations worldwide. Therefore, this research aims at detecting and preventing any intentional or non-intentional data leakages using dynamic password or key for data decryption security mechanisms. To achieve this the OOADM methodology was adopted. The new system was implemented using ASP.net MVC and Microsoft SQL Server Management Studio as the backend. And by incorporating an Audit trail/Transaction log mechanism, the new system monitors the activities within and outside the computing environment with date and time stamp. Hence, the system can be applied in any environment for the prevention and detection of any data leakage.

Sensitive Information and Data Leakage Prevention Using Secured Encryption Algorithm

Machine learning approaches for diabetes prediction face methodological challenges, including data leakage from preprocessing before data splitting, inconsistent handling of missing values, and class imbalance with varying validation methods. This study presents a systematic approach that prevents data leakage and establishes standardized benchmarks for diabetes prediction. Using the PIMA Indian Diabetes Dataset (768 patients), this study applied a preprocessing pipeline: MICE for missing values (652 missing, 9.43% of data), SMOTE for class balance (500 nondiabetic vs 268 diabetic cases), and z-score normalization for feature scaling. Two feature selection methods identified six important clinical variables: Glucose, Pregnancies, Glucose_BMI, Glucose_Age, BMI, and BloodPressure. Dual validation approaches were employed, single split (80:20) and 5-fold cross-validation, to compare five machine learning algorithms: Random Forest (RF), Multi-Layer Perceptron (MLP), XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR). Experimental results demonstrated that RF achieved the highest accuracy (79.79%) in single split testing, whereas MLP performed best in cross-validation (77.81% accuracy, 84.43% ROC-AUC). All algorithms achieved ROC-AUC scores above 0.80. Cross-validation analysis revealed that RF showed consistent performance across data splits, whereas MLP demonstrated better adaptability to different data conditions.

The current Internet threat environment is characterized by an increase in data theft, data leakage, and the creation of targeted, malicious code for the purpose of stealing confidential information that can be used for financial gain. This paper focuses on protecting sensitive files from getting accidently leaked out of a system through unauthorized USB devices. We propose a hardware architecture guaranteeing USB-based data leakage prevention. The prototype board connected to a USB host system intercepts and analyzes USB data transferred to a USB device. This approach provides flexible security enforcement policies including alerting and real-time blocking.

Contemporary businesses have fully utilized information technologies and networks to integrate all the activities of business. The data flow spanning the whole value chain involves abundant confidential and sensitive information, which could cause grave aftermaths if critical information is leaked. This study provides a comprehensive approach for data leakage prevention by taking online travelling services as sample case. Three axes for all-over data leakage prevention is concluded as (1) Maintain secure physical environment; (2) Adopt adequate data leakage technologies; (3) Establish appropriate data protection arrangements and fulfill the requirements continuously.

Despite the availability of data leak detection and prevention tools, there are currently a growing number of confidential data leaks through the fault of insiders. One of the possible data leak channels is encrypted or compressed data transfer, because the existing data leak detection tools use content data analysis methods. This article presents an algorithm of detecting encrypted and compressed data that is based on the statistical model of pseudo-random sequences and allows detecting encrypted and compressed data to an accuracy of 0.99.

Data leakage is a critical threat to app developers because apps use and reserve a variety of sensitive data collected from users. While using third-party SDK is a common way to build apps, some accessed data may not be perceived by app developers. Therefore, the use of third-party SDKs may cause data leakage. This work proposes a platform named "WaRning-Awareness Platform" (WRAP). WRAP records profiles of SDKs, and reveals sensitive data accessed by SDKs. To demonstrate the data leakage caused by third-party SDKs, this work conducts a case study with various categories of Android apps. The result of our case study indicates that if app developers do not pay attention to the accessed data, the data would be leaked.

The data leaks problem is a security key issue in the worldwide connection, communication and interoperability functions among the huge number of mobile devices, especially for apps where sensitive data are exchanged. In order to tackle this dangerous problem, an innovative and powerful tool, named Android JADAL (JAva DAta Leak), has been developed based on hybrid approach which combines both static and dynamic code analysis techniques. This tool has been validated successfully by means of significant tests carried out on sensitive data leak applications. During the test, the user receives notification when sensible data is caught during the execution of apps. The design and testing activities have been conducted in cooperation with the Mathematics and Computer Science Department of Catania University.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

Code injection is a common approach which is utilized to exploit applications. We introduce some of the well-established techniques and formalisms of dynamical system theory into analysis of program behavior via system calls to detect code injections into an applications execution space. We accept a program as a blackbox dynamical system whose internals are not known, but whose output we can observe. The blackbox system observable in our model is the system calls the program makes. The collected system calls are treated as signals which are used to reconstruct the system’s phase space. Then, by using the well-established techniques from dynamical system theory, we quantify the amount of complexity of the system’s (program’s) behavior. The change in the behavior of a compromised system is detected as anomalous behavior compared with the baseline established from a clean program. We test the proposed approach against DARPA-98 dataset and a real-world exploit and present code injection experiments to show the applicability of our approach.

Matching records that refer to the same entity across data-bases is becoming an increasingly important part of many data mining projects, as often data from multiple sources needs to be matched in order to enrich data or improve its quality. Significant advances in record linkage techniques have been made in recent years. However, many new techniques are either implemented in research proof-of-concept systems only, or they are hidden within expensive 'black box' commercial software. This makes it difficult for both researchers and practitioners to experiment with new record linkage techniques, and to compare existing techniques with new ones. The Febrl (Freely Extensible Biomedical Record Linkage) system aims to fill this gap. It contains many recently developed techniques for data cleaning, deduplication and record linkage, and encapsulates them into a graphical user interface (GUI). Febrl thus allows even inexperienced users to learn and experiment with both traditional and new record linkage techniques. Because Febrl is written in Python and its source code is available, it is fairly easy to integrate new record linkage techniques into it. Therefore, Febrl can be seen as a tool that allows researchers to compare various existing record linkage techniques with their own ones, enabling the record linkage research community to conduct their work more efficiently. Additionally, Febrl is suitable as a training tool for new record linkage users, and it can also be used for practical linkage projects with data sets that contain up to several hundred thousand records.

Federated Learning (FL) has emerged as a suitable option for collaborative, user-centric machine learning while protecting sensitive user data. FL differs significantly from traditional centralized frameworks that collect raw data on a central server. FL focuses on collecting model updates generated locally on edge devices. With traditional FL approaches, data must be downloaded to a central server for model training. In contrast, training occurs on the edge and only model updates are sent. This decentralized framework, unlike traditional FL approaches, is ideal for ubiquitous applications—systems woven seamlessly into everyday surroundings like smart homes, advanced mobile devices, and IoT systems—where sensitive data is constantly and automatically generated. In these systems, user privacy is paramount, and advanced methods must be implemented to ensure privacy and system efficacy. This paper discusses the integration of FL into ubiquitous environments, addressing the relevance, advantages, and disadvantages of this approach. I’d like to focus on privacy-preserving methods, but their preservation against data leakage and adversary attacks adds significant value. There are case studies to show the challenges encountered, lessons learned, and the use of FL within privacy-centric ubiquitous systems. In closing this paper, I summarize the key points and underscore the transformational potential of FL for privacy-sensitive, innovative systems. I also promote further research in this area to support its practical relevance. This paper demonstrates that among the many approaches to privacy within pervasive computing, FL is the only one that is practical and scalable.

Malware has become a significant problem for the security of computers in this scientific era. Nowadays, machine learning techniques are applied to find anomalous activities in computers especially in virtualization environments. Identifying anomalous activities in virtual machines with virtual memory introspector and analyzing data with machine learning techniques are need of current trend. In this paper, an anomaly detection method is implemented using Natural Language Processing (NLP) based on Bags of System Calls (BoSC) for learning the behavior of applications on Windows virtual machines running on Xen hypervisor. During this process, system call traces are extracted from normal applications (benign processes) and malware affected applications (malicious processes) with the help of virtual memory introspection. Preprocessing of extracted system call sequences is done to obtain valid system call sequences through filtering and ordering of redundant system calls. Further, analysis of behavior of system call sequences is carried out with NLP based anomaly detection techniques. During this process, Cosine Similarity Algorithm (Co-Sim) is applied to identify malicious processes running on a VM. Apart from this, Point Detection Algorithm is applied to precisely locate the point of compromise in the system call sequences. The results shown in this paper indicates that both of these algorithms detect anomalies in the running processes with 99% accuracy.