An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

One of the most pertinent applications of image analysis is face recognition and one of the most common genetic disorders is Down syndrome (DS), which is caused by chromosome abnormalities in humans. It is currently a challenge in computer vision in the domain of DS face recognition to build an automated system that equals the human ability to recognize face as one of the symmetrical structures in the body. Consequently, the use of machine learning methods has facilitated the recognition of facial dysmorphic features associated with DS. This paper aims to present a concise review of DS face recognition using the currently published literature by following the generic face recognition pipeline (face detection, feature extraction, and classification) and to identify critical knowledge gaps and directions for future research. The technologies underlying facial analysis presented in recent studies have helped expert clinicians in general genetic disorders and DS prediction.

Recently, there has been a significant increase in the number of Artificial Intelligence (AI) systems, software, and components. As a result, it is crucial to evaluate their quality. Quality models for AI have in fact been proposed, but there is a lack of Systematic Mapping Studies (SMS) for quality models in AI systems, software, and components. The goal of this paper is to understand, classify, and critically evaluate existing quality models for AI systems, software, and components. This study conducts an SMS to investigate quality models proposed by various authors in the past. The study only found quality models for AI systems and software. So far, the SMS has revealed no work on AI software component quality models. Finally, the limitations of the quality models and the implications for future research and development efforts are discussed.

In this research, a new Hybrid Obfuscation Technique was proposed to prevent prohibited Reverse Engineering. The proposed hybrid technique contains three approaches; first approach is string encryption. The string encryption is about adding a mathematical equation with arrays and loops to the strings in the code to hide the meaning. Second approach is renaming system keywords to Unicode to increase the difficulty and complexity of the code. Third approach is transforming identifiers to junk code to hide the meaning and increase the complexity of the code. An experiment was conducted to evaluate the proposed Hybrid Obfuscation Technique. The experiment contains two phases; the first phase was conducting reverse engineering against java applications that do not use any protection to determine the ability of reversing tools to read the compiled code. The second phase was conducting reverse engineering against the proposed technique to evaluate the effectiveness of it. The experiment of the hybrid obfuscation technique was to test output correctness, syntax, reversed code errors, flow test, identifiers names test, methods, and classes correctness test. With these parameters, it was possible to determine the ability of the proposed technique to defend the attack. The experiment has presented good and promising results, where it was nearly impossible for the reversing tool to read the obfuscated code. Even the revealed code did not perform as well as original and obfuscated code.