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Cloud computing represents a different paradigm in the field of distributed computing that involves more and more researchers. We can see in this context the need to know exactly where, when and how a piece of data is processed or stored. Compared with classic digital forensic, the field of cloud forensic has a lot of difficulties because data is not stored on a single place and furthermore it implies the use of virtualization technologies. In this paper we present a new method of monitoring activity in cloud computing environments and datacenters by running a secure cloud forensic framework. We talk in detail about the capabilities that such system must have and we propose an architecture for it. For testing and results we have implemented this solution to our previous developed cloud computing system.

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Interconnected computing units are used more and more in our daily lives, starting from the transportation systems and ending with gas and electricity distribution, together with tenths or hundreds of systems and sensors, called critical infrastructures. In this context, cyber protection is vital because they represent one of the most important parts of a country's economy thus making them very attractive to cyber criminals or malware attacks. Even though the detection technologies for new threats have improved over time, modern malware still manage to pass even the most secure and well organized computer networks, firewalls and intrusion detection equipments, making all systems vulnerable. This is the main reason that automatic learning is used more often than any other detection algorithms as it can learn from existing attacks and prevent newer ones.In this paper we discuss the issues threatening critical infrastructures systems and propose a framework based on machine learning algorithms and game theory decision models that can be used to protect such systems. We present the results taken after implementing it using three distinct classifiers -k nearest neighbors, decision trees and support vector machines.

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Cloud Computing is one of the most important paradigms used in today's digital environment because they offer to the user benefits such as virtual machine renting, digital information backup, ease of access to stored data and many other. Together with the increased usage of these technologies, at the datacenter level we need to know in detail the information flux between the computing nodes. More exactly, on which server the data is processed, how it is manipulated and stored at the physical or virtual level. To have a full picture of what it is going on we need to have a centralized system that can collect data regarding about the datacenters status and correlate them with known anomalies and other usage patterns and in case of a security breach to act accordingly.In this paper we present a new way to monitor running virtual machines existing at a datacenter level. We will talk about the architecture, and how we use the information collected to train our automated anomalies machine learning modules. We also present some implementation details and results taken from the experimental setup.

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