Computer Forensics: Dark Net Forensic Framework and Tools Used for Digital Evidence Detection

As the development of technology increases and its use becomes increasingly more widespread, computer crimes grow. Hence, computer forensics research is becoming more crucial in developing good forensic frameworks and digital evidence detection tools to deter more cyber-attacks. In this paper, we explore the science of computer forensics, a dark web forensic framework, and digital evidence detection tools

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth

Rank and wormhole attack detection model for RPL-based Internet of Things using machine learning

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research

SCES proposed model for data protection in cloud educational system in Saudi Arabia

Education is the basis for the development of peoples and the progress of countries, so the Saudi government has made great efforts to develop education, and made this development one of the most important goals of the 2030 vision, and among the most prominent of these efforts and most in keeping with the current local and global conditions is what it provided during the Corona pandemic from the transformation to e-learning and the adoption of education platforms that enable its users to access and benefit from its services. Since the data of this systems users must be kept confidential and secure, this paper was made. This paper focuses on the security aspects of data protection in educational system in the Kingdom of Saudi Arabia in cloud computing environment. This paper involves data protection aspects by protecting data of Saudi educational systems and maintaining their integrity and confidentiality. It also proposes a secure model called SCES (Secure Cloud Educational System) based on Attribute-based encryption as an access control technique to avoid tampering with data when unauthorized people try to access it, and to protect data the proposed model applies combined encryption system using AES and RSA encryption algorithms. &nbsp

Data protection in cloud educational system in Saudi Arabia

Education is the basis for the development of peoples and the progress of countries, so the Saudi government has made great efforts to develop education, and made this development one of the most important goals of the 2030 vision, and among the most prominent of these efforts and most in keeping with the current local and global conditions is what it provided during the Corona pandemic from the transformation to e-learning and the adoption of education platforms that enable its users to access and benefit from its services. Since the data of this systems users must be kept confidential and secure, this paper was made. This paper focuses on the security aspects of data protection in educational system in the Kingdom of Saudi Arabia in cloud computing environment. This paper involves data protection aspects by protecting data of Saudi educational systems and maintaining their integrity and confidentiality. It also proposes a secure model based on Attribute-based encryption as an access control technique to avoid tampering with data when unauthorized people try to access it, and to protect data the proposed model applies combined encryption system using AES and RSA encryption algorithms

Cloud computing environments and management for big data security, and performance (CBDS) Model

Big data is a technology that is growing at a tremendous speed in many sectors and fields, and this huge growth is also accompanied by the development of cloud computing, Cloud computing provides innovative organization in order to be able to meet the services you need to deal with big data in terms of infrastructure, where big data is processed and stored in the companies and establishments concerned, and unfortunately, still, It suffers from several problems and gaps that may threaten the security of this data In order to improve data security in cloud computing, provide secure access, and improve performance. This paper will discuss several concerns that may threaten data security, access, and cloud computing performance while managing big data, and that is by proposing a model(CBDS) Cloud for Big Data Security to develop performance in cloud management that meets several needs that will contribute to maintaining data integrity and confidentiality, providing secure access to data, and will greatly improve the performance of the cloud during data processing, storage, and recovery from the cloud at the user’s desire and in complete security

Cloud Computing security: From single to multi-clouds

Abstract General Terms Securit

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time