An Encrypted File Detection Algorithm

Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as entropy estimation, to identify encrypted fragments. However, modern content types use compression techniques which alter data distribution pushing it closer to the uniform distribution. The result is that current approaches exhibit unreliable encryption detection performance when compressed data appear in the dataset. Furthermore, proposed approaches are typically evaluated over few data types and fragment sizes, making it hard to assess their practical applicability. This paper compares existing statistical tests on a large, standardized dataset and shows that current approaches consistently fail to distinguish encrypted and compressed data on both small and large fragment sizes. We address these shortcomings and design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data. We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB. Our results highlight that EnCoD outperforms current approaches by a wide margin, with accuracy ranging from sim 82% for 512B fragments up to sim 92% for 8KB data fragments. Moreover, EnCoD can pinpoint the exact format of a given data fragment, rather than performing only binary classification like previous approaches.

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.

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.

Exchange of information is done in every organization. Information can be confidential and non-confidential. There are several channels through which exchange of data is done. During this process of exchange, data gets leaked. Enterprises are facing a lot of issues with data leakage and also towards recognizing the leakers. It is a very important need for the organization to exchange confidential information securely and easily. The main goal is to create the data leakage prevention and detection model which detects when the allocator’s sensitive information has been disclosed by operators and also to identify the leaker that leaked the data. Leakage of confidential information leads to a great loss to the organization. In addition to financial loss, an organization’s reputation is ruined and facing its identity stolen. These losses take a lot of time to recover. In this paper, it will focus on research papers on various techniques, which consists of classification strategies to identify sensitive and non-sensitive data, distribution strategy to monitor data, and encryption technique used.

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.

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.

Artificial intelligence (AI) evaluates networking data to detect and prevent data leakage, while enhancing security in financial cloud networks through advanced optimisation. Consumption of resources, scalability issues, concerns about privacy, false-positives and adapting to emerging threats reduce the performance of the data leakage detection (DLD) system. In this investigation, research proposed a novel model of gravitational search inspired flexible Adaboost (GS-FAB) for developing a DLD framework to improve financial cloud networking security by proactively identifying and mitigating risks. The financial cloud network security dataset was collected in this research. After the data collection, we applied a robust scaler for data preprocessing. After data preprocessing, the data labelling process is implemented and that could be divided into non-leakage and leakage data. Following the leakage and non-leakage data process, the research separated the leakage and non-leakage data into training and testing. The training process includes the GS-FAB technique. The GS-FAB method and testing process encompass the performance analysis. Finally, research employs some metrics for existing and proposed methods. Our proposed GS-FAB technique results include precision (95%), f1-score (90.5%), recall (91.4%), RMSE (49.4%), SNR (64%) and accuracy (98.1%). In comparison to both proposed and existing approaches, our suggested GS-FAB technique obtained better results. This investigation not only enhances the accuracy but also promises a better effective technique for improving financial cloud networking security by providing robust protections from unwanted data accessibility.

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.

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

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.

In today’s digital landscape, where data is a critical asset for businesses, building a resilient backup infrastructure is paramount to ensuring data availability and protection. This research investigates the synergy between three essential elements—data redundancy, encryption, and Role-Based Access Control (RBAC) to enhance backup system resilience. Data redundancy, achieved through methods like the 3-2-1 rule and cloud replication, ensures continuous availability and protection against hardware failures. Encryption secures backup data, making it unreadable to unauthorized users and safeguarding sensitive information in compliance with regulatory standards. RBAC, on the other hand, provides granular control over user access, enforcing the principle of least privilege and minimizing the risk of unauthorized data manipulation. By combining these strategies, organizations can mitigate the risks of data loss, breaches, and unauthorized access, creating a robust, secure, and efficient backup infrastructure. This paper provides a comprehensive analysis of how these technologies can be integrated, offers best practices for implementation, and discusses the future of backup systems in an increasingly complex threat environment. Keywords: Backup Infrastructure, Data Redundancy, Data Encryption, Role-Based Access Control, RBAC, Data Security, Disaster Recovery, Data Protection, Cloud Backup, Data Availability, Security Best Practices, IT Resilience, Cybersecurity, Backup Strategies.

Data leakage in an organization is a very important concern that leads to the ex-filtration of data, The work in this paper addresses a novel concept for prevention of data leakage for data in transit. The text under consideration is classified to confidential or non-confidential category based on the content and context using Machine Learning technique. Subsequent action for encryption is performed on the confidential data and then transmitted from the Intranet domain to Internet domain ensuring that the data is not compromised to unauthorized users. In addition, normal transactional data which is nonconfidential in nature is not prevented from transmitting and is easily accessible by a third party. Encryption is applied only to selected data and not the entire data in transit, ensuring that the hardware resources are efficiently utilized. An adversary can simply compose an e-mail with the organization's confidential information as the body of the mail, in such scenarios our method will classify the e-mail content and will encrypt the data so that the data is not revealed to an outsider.

In this paper, we describe the feasibility and practical study of the recently proposed idea for data leak prevention (DLP) based on end-point policy enforcement. The most reassuring way to prevent sensitive data leak is to thwart sensitive data export before it has a chance to occur. Using a System Call Interception (SCI) technique we investigate the possibility of automatically detecting and amending a non-desired, policy breaching behavior at the "intention" stage: as the corresponding system call is called by an application, but before the action has been accomplished. The SCI method is especially valuable for "black box" applications, for which source code is not available. In our system, we catalog the system calls involved in the DLP events, and reduce our SCI to the minimum necessary set of system calls associated with the sensitive, DLP-requiring tasks. We describe the system behavior for several different applications that we have studied to date.

The exposure of sensitive data in storage and transmission poses a serious threat to organizational and personal security. Data leak detection aims at scanning content (in storage or transmission) for exposed sensitive data. Because of the large content and data volume, such a screening algorithm needs to be scalable for a timely detection. Our solution uses the MapReduce framework for detecting exposed sensitive content, because it has the ability to arbitrarily scale and utilize public resources for the task, such as Amazon EC2. We design new MapReduce algorithms for computing collection intersection for data leak detection. Our prototype implemented with the Hadoop system achieves 225 Mbps analysis throughput with 24 nodes. Our algorithms support a useful privacy-preserving data transformation. This transformation enables the privacy-preserving technique to minimize the exposure of sensitive data during the detection. This transformation supports the secure outsourcing of the data leak detection to untrusted MapReduce and cloud providers.

The implementation of a smart water network (SWN) is viewed as a strategic approach to address many challenges faced by water utilities, such as pipe leak detection and main break prevention. This paper develops a convolutional neural network (CNN)–based model to classify acoustic wave files collected by the South Australian Water Corporation’s (SA Water’s) SWN over the city of Adelaide. The VGGish model (VGG refers to the team who developed the model—Visual Geometry Group) is selected as a suitable transfer learning model to extract features from wave files. The CNN model classifies an acoustic wave file as an anomaly or other background or environmental noise. Identification of a wave file as an anomaly triggers a Siamese CNN model to determine whether it is related to a regular/irregular scheduled event (for example, irrigation system near public parks or water consumption by large buildings). A field investigation is initiated if a wave file is classified as an anomaly and it is not related to a scheduled event. The developed models have been validated using data that is recorded by SWN in Adelaide. This validation data set comprises 1098 wave files, which are recorded by 34 accelerometers and are associated with 32 known leaks. The validation results shown that accuracy of alarms generated by the developed models is 92.44%. The validations confirm the developed models as an effective tool for water pipeline leak and crack detection, which, in turn, enables proactive management of the pipeline assets.