Intrusion detection plays a critical role in cyber-security domain since malicious attacks cause irreparable damages to cyber-systems. In this work, we propose the I2SP prototype, which is a novel Information Sharing Platform, able to gather, pre-process, model, and distribute network-traffic information. Within the I2SP prototype we build several challenging deep feature learning models for network-traffic intrusion detection. The learnt representations will be utilized for classifying each new network measurement into its corresponding threat level. We evaluate our prototype's performance by conducting case studies using cyber-security data extracted from the Malware Information Sharing Platform (MISP)-API. To the best of our knowledge, we are the first that combine the MISP-API in order to construct an information sharing mechanism that supports multiple novel deep feature learning architectures for intrusion detection. Experimental results justify that the proposed deep feature learning techniques are able to predict accurately MISP threat-levels. INDEX TERMS Malware information sharing platform, network intrusion detection, anomaly detection, deep feature learning, convolutional neural networks, long-short memory neural networks, stacked-sparse autoencoders.

Our contemporary society has never been more connected and aware of vital information in real time, through the use of innovative technologies. A considerable number of applications have transitioned into the cyber-physical domain, automating and optimizing their routines and processes via the dense network of sensing devices and the immense volumes of data they collect and instantly share. In this paper, we propose an innovative architecture based on the monitoring, analysis, planning, and execution (MAPE) paradigm for network and service performance optimization. Our study confirms distinct evidence that the utilization of learning algorithms, consuming datasets enriched with the users’ empirical opinions as input during the analysis and planning phases, contributes greatly to the optimization of video streaming quality, especially by handling different packet loss rates, paving the way for the achievable provision of a resilient communications platform for calamity assessment and management.

Electrocardiogram (ECG) analysis has been established as a key element regarding the evaluation of the human health status. The computational complexity along with the strict constraints of real-time assessment of a heart beat, has made the ECG analysis flow a very challenging application for embedded medical devices. Recent advancements in cyber-physical and IoT systems are transforming medical processing towards embedded and wearable devices, thus making energy consumption a first class design objective. In this work, we focus on analysing the power, performance and energy profiles of an ECG analysis and arrhythmia detection software pipeline during its execution on a ZYNQ-based SoC. We evaluate a large set of design alternatives spanning from a pure software-only implementation to HW/SW oriented designs, in which High-Level Synthesis capabilities are utilized. Using the medically validated MIT-BIH ECG database, we examine the efficiency and the sensitivity of the design solutions in different operating frequencies and examine three Quality of Service (QoS) levels concerning the sampling rate of the ECG signal.

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