Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

Performance losses of cluster applications can arise from various sources in the communications network of computer clusters. Typically, CPU intensive applications generate a small amount of network traffic the overall influence of the network subsystem is minimal. On the other hand, a dataintensive and network aware application generates a large amount of network traffic and the influence of the network subsystem is significantly greater. This paper presents a model that aims to improve the cluster's network performance by reducing the data transfer time, this solution having the advantage that doesn't imply modifications of the original applications or of the kernel.

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