Biblio

From the past few years cloud services are so popular and are being used by many people from various domains for various purposes such as data storage, e-mails, backing up data and much more. While there were many options to perform such things why did people choose cloud? The answer is clouds are more flexible, convenient, reliable and efficient. Coming to security of data over cloud, it is secure to store data over cloud rather than storing data locally as there is chance of some computer breakdown or any natural disaster may also occur. There are also many threats for data security over cloud namely data breaching, lack of access-key management and much more. As the data has been processed and being stored online for various purposes, there is a clear requirement for data security. Many organizations face various challenges while storing their data over cloud such as data leakages, account hijacking, insufficient credentials and so on. So to overcome these challenges and safeguard the data, various encryption techniques were implemented. However, even though encryption is used, the data still needs to be decrypted in order to do any type of operation. As a result, we must choose a manner in which the data can be analyzed, searched for, or used in any other way without needing to be decoded. So, the objective is to introduce a technique that goes right for the above conditions mentioned and for data security over cloud.

With the spread of the Internet, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. The first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device at the end. To address these issues, fog computing, which is located in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. The main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk measures by evaluating the risk values. The proposed risk countermeasures for fog computing should help us to utilize IoT devices in a safe and secure manner.

In recent years, new cyber attacks such as targeted attacks have caused extensive damage. With the continuing development of the IoT society, various devices are now connected to the network and are being used for various purposes. The Internet of Things has the potential to link cyber risks to actual property damage, as cyberspace risks are connected to physical space. With this increase in unknown cyber risks, the demand for cyber insurance is increasing. One of the most serious emerging risks is the silent cyber risk, and it is likely to increase in the future. However, at present, security measures against silent cyber risks are insufficient. In this study, we conducted a risk management of silent cyber risk for organizations with the objective of contributing to the development of risk management methods for new cyber risks that are expected to increase in the future. Specifically, we modeled silent cyber risk by focusing on state transitions to different risks. We newly defined two types of silent cyber risk, namely, Alteration risk and Combination risk, and conducted risk assessment. Our assessment identified 23 risk factors, and after analyzing them, we found that all of them were classified as Risk Transference. We clarified that the most effective risk countermeasure for Alteration risk was insurance and for Combination risk was measures to reduce the impact of the risk factors themselves. Our evaluation showed that the silent cyber risk could be reduced by about 50%, thus demonstrating the effectiveness of the proposed countermeasures.

Due to the intrinsic properties of Solid-State Drives (SSDs), invalid data remain in SSDs before erased by a garbage collection process, which increases the risk of being attacked by adversaries. Previous studies use erase and cryptography based schemes to purposely delete target data but face extremely large overhead. In this paper, we propose a Workload-Aware Secure Deletion scheme, called WAS-Deletion, to reduce the overhead of secure deletion by three major components. First, the WAS-Deletion scheme efficiently splits invalid and valid data into different blocks based on workload characteristics. Second, the WAS-Deletion scheme uses a new encryption allocation scheme, making the encryption follow the same direction as the write on multiple blocks and vertically encrypts pages with the same key in one block. Finally, a new adaptive scheduling scheme can dynamically change the configurations of different regions to further reduce secure deletion overhead based on the current workload. The experimental results indicate that the newly proposed WAS-Deletion scheme can reduce the secure deletion cost by about 1.2x to 12.9x compared to previous studies.

The identification of transmission sections is used to improve the efficiency of monitoring the operation of the power grid. In order to test the validity of transmission sections identified, an assessment process is necessary. In addition, Transmission betweenness, an index for finding the key transmission lines in the power grid, should also be verified. In this paper, chain attack is assumed to check the weak links in the grid, thus verifying the transmission betweenness implemented for the system. Moreover, the line outage distribution factors (LODFs) are used to quantify the change of power flow when the leading line in transmission sections breaks down, so that the validity of transmission sections can be proved. Case studies based on IEEE 39 and IEEE 118 -bus system proved the effectiveness of the proposed method.

Different organizations or countries maybe adopt different PKI trust model in real applications. On a large scale, all certification authorities (CA) and end entities construct a huge mesh network. PKI trust model exhibits unstructured mesh network as a whole. However, mesh trust model worsens computational complexity in certification path processing when the number of PKI domains increases. This paper proposes an enhanced mesh trust model for PKI. Keys generation and signature are fulfilled in Trusted Platform Module (TPM) for higher security level. An algorithm is suggested to improve the performance of certification path processing in this model. This trust model is less complex but more efficient and robust than the existing PKI trust models.