No Arabic abstract
Security is considered one of the top ranked risks of Cloud Computing (CC) due to the outsourcing of sensitive data onto a third party. In addition, the complexity of the cloud model results in a large number of heterogeneous security controls that must be consistently managed. Hence, no matter how strongly the cloud model is secured, organizations continue suffering from lack of trust on CC and remain uncertain about its security risk consequences. Traditional risk management frameworks do not consider the impact of CC security risks on the business objectives of the organizations. In this paper, we propose a novel Cloud Security Risk Management Framework (CSRMF) that helps organizations adopting CC identify, analyze, evaluate, and mitigate security risks in their Cloud platforms. Unlike traditional risk management frameworks, CSRMF is driven by the business objectives of the organizations. It allows any organization adopting CC to be aware of cloud security risks and align their low-level management decisions according to high-level business objectives. In essence, it is designed to address impacts of cloud-specific security risks into business objectives in a given organization. Consequently, organizations are able to conduct a cost-value analysis regarding the adoption of CC technology and gain an adequate level of confidence in Cloud technology. On the other hand, Cloud Service Providers (CSP) are able to improve productivity and profitability by managing cloud-related risks. The proposed framework has been validated and evaluated through a use-case scenario.
Cloud computing as a potential paradigm offers tremendous advantages to enterprises. With the cloud computing, the markets entrance time is reduced, computing capabilities is augmented and computing power is really limitless. Usually, to use the full power of cloud computing, cloud users has to rely on external cloud service provider for managing their data. Nevertheless, the management of data and services are probably not fully trustworthy. Hence, data owners are uncomfortable to place their sensitive data outside their own system .i.e., in the cloud. Bringing transparency, trustworthiness and security in the cloud model, in order to fulfill clients requirements are still ongoing. To achieve this goal, our paper introduces two levels security framework: Cloud Service Provider (CSP) and Cloud Service User (CSU). Each level is responsible for a particular task of the security. The CSU level includes a proxy agent and a trust agent, dealing with the first verification. Then a second verification is performed at the CSP level. The framework incorporates a trust model to monitor users behaviors. The use of mobile agents will exploit their intrinsic features such as mobility, deliberate localization and secure communication channel provision. This model aims to protect users sensitive information from other internal or external users and hackers. Moreover, it can detect policy breaches, where the users are notified in order to take necessary actions when malicious access or malicious activity would occur.
Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of organizations assets that are dependent on the usage of machine learning models. While it is not easy to predict the types of new attacks that might be developed over time, it is possible to evaluate the risks connected to using machine learning models and design measures that help in minimizing these risks. In this paper, we outline a novel framework to guide the risk management process for organizations reliant on machine learning models. First, we define sets of evaluation factors (EFs) in the data domain, model domain, and security controls domain. We develop a method that takes the asset and task importance, sets the weights of EFs contribution to confidentiality, integrity, and availability, and based on implementation scores of EFs, it determines the overall security state in the organization. Based on this information, it is possible to identify weak links in the implemented security measures and find out which measures might be missing completely. We believe our framework can help in addressing the security issues related to usage of machine learning models in organizations and guide them in focusing on the adequate security measures to protect their assets.
`Anytime, Anywhere data access model has become a widespread IT policy in organizations making insider attacks even more complicated to model, predict and deter. Here, we propose Gargoyle, a network-based insider attack resilient framework against the most complex insider threats within a pervasive computing context. Compared to existing solutions, Gargoyle evaluates the trustworthiness of an access request context through a new set of contextual attributes called Network Context Attribute (NCA). NCAs are extracted from the network traffic and include information such as the users device capabilities, security-level, current and prior interactions with other devices, network connection status, and suspicious online activities. Retrieving such information from the users device and its integrated sensors are challenging in terms of device performance overheads, sensor costs, availability, reliability and trustworthiness. To address these issues, Gargoyle leverages the capabilities of Software-Defined Network (SDN) for both policy enforcement and implementation. In fact, Gargoyles SDN App can interact with the network controller to create a `defence-in-depth protection system. For instance, Gargoyle can automatically quarantine a suspicious data requestor in the enterprise network for further investigation or filter out an access request before engaging a data provider. Finally, instead of employing simplistic binary rules in access authorizations, Gargoyle incorporates Function-based Access Control (FBAC) and supports the customization of access policies into a set of functions (e.g., disabling copy, allowing print) depending on the perceived trustworthiness of the context.
In this short paper we argue that to combat APTs, organizations need a strategic level shift away from a traditional prevention centered approach to that of a response centered one. Drawing on the information warfare (IW) paradigm in military studies, and using Dynamic Capability Theory (DCT), this research examines the applicability of IW capabilities in the corporate domain. We propose a research framework to argue that conventional prevention centred response capabilities; such as incident response capabilities and IW centred security capabilities can be integrated into IW enabled dynamic response capabilities that improve enterprise security performance.
Cloud computing has pervaded through every aspect of Information technology in past decade. It has become easier to process plethora of data, generated by various devices in real time, with the advent of cloud networks. The privacy of users data is maintained by data centers around the world and hence it has become feasible to operate on that data from lightweight portable devices. But with ease of processing comes the security aspect of the data. One such security aspect is secure file transfer either internally within cloud or externally from one cloud network to another. File management is central to cloud computing and it is paramount to address the security concerns which arise out of it. This survey paper aims to elucidate the various protocols which can be used for secure file transfer and analyze the ramifications of using each protocol.