In recent years, Cyber Warfare has been one of the most essential war scenes for intelligence and military organizations. As the volume of Data is increased, Data stream processing and Data security would be the basic requirements of military enterprises. Although, by Data encryption no one can have access to Data contents, but adversaries can delete some records from Data stream or add some counterfeit records in it without any knowledge about records contents. Such attacks are called integrity attacks. Because integrity control in DSMS is a continuous process, it may be categorized in passive defense activities. In this paper, we present a probabilistic auditing model for integrity control of received stream results via an unsecured environment. In our architecture, users and Data stream owners are trusted and channels are unsecured. The server is considered a black box and auditing process is fulfilled by contribution between Data stream owner and users. We exploit existing Data Stream Management Systems (DSMS) without any modification. Our method has no significant cost on users side and detects integrity attacks accurately and rapidly. Correctness and convergence of our probabilistic algorithm is proved and our evaluation shows very good results.

With the rapid rise of cybersecurity threats and the increasing complexity of digital security, event log Data serves as a critical source for identifying and analyzing cyberattacks and threats. This Data provide key insights into system activities, essential for detecting unauthorized intrusions, analyzing suspicious behaviors, and conducting security investigations. However, any alteration or tampering with the Data can disrupt the analysis and detection processes, leading to incorrect security decisions. Blockchain technology, with its unique features such as decentralization, immutability, and transparency, has been recognized as a reliable and secure platform for storing and protecting Data. This technology enables the storage of Data hashes in a way that any changes can be easily detected. However, directly storing the vast volume of event log Data on the blockchain faces challenges such as high costs and storage space limitations. In this research, an innovative model has been presented to automate the assurance of event log Data integrity and confidentiality using the public Ethereum blockchain and smart contracts. Instead of storing event log Data directly, only their hashes have been saved on the blockchain. This approach not only reduces storage costs but also ensures Data confidentiality. The automated Data integrity assurance process in this model occurs in two stages: Stage One: Event log Data hashes have been periodically stored on the blockchain and compared with previous hashes. Stage Two: Over longer intervals, all stored hashes have been reviewed and validated to prevent any potential tampering. In this study, the costs associated with implementing this model on the Ethereum Sepolia test network had been precisely calculated. The analysis indicates that operational costs and computational overhead have been optimized across different time intervals, demonstrating the model's feasibility for large-scale deployment. Ultimately, this research tries to introduce a novel and practical model, taking a significant step toward automating the assurance of event log Data integrity and confidentiality, providing a reliable solution for real-world applications.

The neoclassical growth model is tested by use of panel Data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel Data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

The human face of evil and criminal incidents in life is unavoidable, and mankind is always in need of understanding their environment. The use of systematic methods to identify, detect and prevent crime in society to reach a resolution mass analysis is increasing. Due to the expansion and development of information systems in organizations, In this paper we use Data mining techniques to analyze crime Data set in Databases has been. Using tools and Data mining algorithms to the complex nature of the crime, Crime and the subtle relationship s between Data models and identify crime patterns, to detect and prevent. Classification algorithms optimized model to predict future characteristic s of the crimes committed and clustering algorithms to identify the type of crime the Data sets were collected.

IoT can be used for many applications in various fields. The field of health will be one of the main applications of the Internet of Things in the future. However, if the IoT is fully implemented in the field of health, we will face many challenges, one of which is Data integrity. Data integrity assures us that any Data sent by the sender, the same Data is received by the recipient without any changes. In this paper, a integrity Data control protocol based on space and time chaos theory is proposed to be used in the smart health network. In the proposed protocol, the process of integrity control takes place in two phases of sequence sharing and Data exchange. In the sequence sharing phase, the parties to the secure exchange of identity information using a space-time chaos model generate a common sequence series, according to this sequence series the sending time of validation packages in the Data exchange phase will be determined. The performance of the proposed method has been evaluated from different aspects and its efficiency has been compared with the traditional Data integrity control protocol. Reducing time complexity, reducing closed overhead, and reducing energy consumption compared to the traditional Data integrity control protocol are some of the benefits of the proposed method. The results also show that the proposed method is safe against all types of attacks and can be used as an efficient integrity control system in real applications.

With the development of telecommunication and communication technologies, especially wireless communications, information cryptography is one of the communication necessities. Today, cryptographic algorithms are used to increase security and prevent DICOM medical images from unauthorized access. It should be noted that changes in DICOM medical images will cause the doctor to misdiagnose the patient's treatment process. In this paper, a type of hybrid cryptographic algorithms is designed. In the proposed algorithm, DNA encryption algorithm is used to encrypt DICOM images and patient biometric information such as fingerprint or iris image is used to make digital signature and validate DICOM medical images. The designed encryption algorithm is resistant to brute force attacks and the entropy of the encrypted DICOM images is above 7. 99.