Biblio

With the advent of Industry 4.0, the Internet of Things (IoT) and Artificial Intelligence (AI), smart entities are now able to read the minds of users via extracting cognitive patterns from electroencephalogram (EEG) signals. Such brain data may include users' experiences, emotions, motivations, and other previously private mental and psychological processes. Accordingly, users' cognitive privacy may be violated and the right to cognitive privacy should protect individuals against the unconsented intrusion by third parties into the brain data as well as against the unauthorized collection of those data. This has caused a growing concern among users and industry experts that laws to protect the right to cognitive liberty, right to mental privacy, right to mental integrity, and the right to psychological continuity. In this paper, we propose an AI-enabled EEG model, namely Cognitive Privacy, that aims to protect data and classifies users and their tasks from EEG data. We present a model that protects data from disclosure using normalized correlation analysis and classifies subjects (i.e., a multi-classification problem) and their tasks (i.e., eye open and eye close as a binary classification problem) using a long-short term memory (LSTM) deep learning approach. The model has been evaluated using the EEG data set of PhysioNet BCI, and the results have revealed its high performance of classifying users and their tasks with achieving high data privacy.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based services to its customers. Nevertheless, security and privacy are the main challenges to its success due to its dynamic and distributed architecture, resulting in generating big data that should be carefully analysed for detecting network's vulnerabilities. In this paper, we propose a Collaborative Anomaly Detection Framework (CADF) for detecting cyber attacks from cloud computing environments. We provide the technical functions and deployment of the framework to illustrate its methodology of implementation and installation. The framework is evaluated on the UNSW-NB15 dataset to check its credibility while deploying it in cloud computing environments. The experimental results showed that this framework can easily handle large-scale systems as its implementation requires only estimating statistical measures from network observations. Moreover, the evaluation performance of the framework outperforms three state-of-the-art techniques in terms of false positive rate and detection rate.