Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection.

In this paper, biometric methods for contactless and unrestricted access control for mobile devices are proposed. The major contribution of this paper are palmprint and knuckles feature extraction methods dedicated for the mobile contactless biometrics. We use texture mask-based features for the palmprint. For the knuckles, we use Probabilistic Hough Transform and Speeded Up Robust Features as well as the 3-step classification methodology. We prove the efficiency of the presented methods by reporting promising results.

The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an early warning system which indicates compromised devices in a quick and efficient manner. In this paper, we propose time window embedding solutions that efficiently process a massive amount of data and have a low-memory-footprint at the same time. On top of the proposed embedding vectors, we use the core anomaly detection unit. It is a classifier that is based on the transformer’s encoder component followed by a feed-forward neural network. We have compared the proposed method with other classical machine-learning algorithms. Therefore, in the paper, we formally evaluate various machine-learning schemes and discuss their effectiveness in the IoT-related context. Our proposal is supported by detailed experiments that have been conducted on the recently published Aposemat IoT-23 dataset.

The increased usage of cloud services, growing number of web applications users, changes in network infrastructure that connects devices running mobile operating systems and constantly evolving network technology cause novel challenges for cyber security. As a result, to counter arising threats, network security mechanisms, sensors and protection schemes also have to evolve, to address the needs and problems of the users. In this article, we focus on countering emerging application layer cyber attacks since those are listed as top threats and the main challenge for network and cyber security. The major contribution of the article is the proposition of machine learning approach to model normal behaviour of application and to detect cyber attacks. The model consists of patterns (in form of Perl Compatible Regular Expressions (PCRE) regular expressions) that are obtained using graph-based segmentation technique and dynamic programming. The model is based on information obtained from HTTP requests generated by client to a web server. We have evaluated our method on CSIC 2010 HTTP Dataset achieving satisfactory results.