Modern digitalized cyber domains are extremely complex ensemble. Cyber attacks or incidents against system may affect capricious effects for another system or even for physical devices. For understanding and training to encounter those effects requires an effective and complex simulation capability. Cyber Security Exercises are an effective expedient for training and learning measures and operations […]
This study aims to find a list of methods to fool artificial neural networks used in medical imaging. We collected a short list of publications related to machine learning model fooling to see if these methods have been used in the medical imaging domain. Specifically, we focused our interest to pathological whole slide images used […]
In the modern society almost all services are based on data-networks and networked systems. Especially through the growing digitalization an increasing number of services is connected to data-networks. One example of a highly digitalized domain is the healthcare sector, where a cyber-attack could cause extreme circumstances. Decision making requires knowledge about the current situation. Particularly, […]
Training neural networks with captured real-world network data may fail to ascertain whether or not the network architecture is capable of learning the types of correlations expected to be present in real data. In this paper we outline a statistical model aimed at assessing the learning capability of neural network-based intrusion detection system. We explore […]
JAMK has two Data Analytics projects funded by European Regional Development Fund (ERDF): New Business Innovations from Data Analytics and Information Secure R&D-environment for Data Analytics. The focus of the Information Secure R&D environment for Data Analytics project is on implementing the needed investments in order to plan, build and deploy an integrated, information secure […]
The need for skillful cyber security workforce has increased dramatically during the last ten years. The contents of the degree programmes have not been able to respond to this need adequately and the curriculum contents have not always met the industry’s knowledge needs. In this paper, we describe a model for designing a degree programme […]
Increasing amount of attacks and intrusions against networked systems and data networks requires sensor capability. Data in modern networks, including the Internet, is often encrypted, making classical traffic analysis complicated. In this study, we detect anomalies from encrypted network traffic by developing an anomaly based network intrusion detection system applying neural networks based on the […]
Cyber security exercises (CSE) are complex learning experiences aimed at developing expert knowledge and competence through simulation. In this paper we examine pedagogical issues relating to CES, from exercise design to training results and evaluation. In addition, we present a Deliberate Practice -oriented view on expert and competence development for CSEs. We use data gathered […]
In the cyber domain, situational awareness of the critical assets is extremely important. For achieving comprehensive situational awareness, accurate sensor information is required. An important branch of sensors are Intrusion Detection Systems (IDS), especially anomaly based intrusion detection systems applying artificial intelligence or machine learning for anomaly detection. This millennium has seen the transformation of […]
The number of intrusions and attacks against data networks and networked systems increases constantly, while encryption has made it more difficult to inspect network traffic and classify it as malicious. In this paper, an anomaly-based intrusion detection system using Haar wavelet transforms in combination with an adversarial autoencoder was developed for detecting malicious TLS-encrypted Internet traffic. […]
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JAMK University of Applied Sciences, Institute of Information Technology
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40100 Jyväskylä, Finland
jyvsectec@jamk.fi
