The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

Artificial intelligence is a rapidly developing field of research with many practical applications. Congruent to advances in technologies that enable big data, deep learning, and neural networks to train, learn, and predict, artificial intelligence creates new risks that are difficult to predict and manage. Such risks include economic turmoil, existential crises, and the dissolution of individual privacy. If unchecked, the capabilities of artificially intelligent systems could pose a fundamental threat to privacy in their operation or these systems may leak information under adversarial conditions. In this article, we survey the literature and provide various scenarios for the use of artificial intelligence, highlighting potential risks to privacy and offering various mitigating strategies. For the purpose of this research, a North American perspective of privacy is adopted. Impact statement—While an appreciation of the privacy risks associated with artificial intelligence is important, a thorough understanding of the assortment of different technologies that comprise artificial intelligence better prepares those implementing such systems in assessing privacy impacts. This can be achieved through the independent consideration of each constituent of an artificially intelligent system and its interactions. Under individual consideration, privacy-enhancing tools can be applied in a targeted manner to reduce the risk associated with specific components of an artificially intelligent system. A generalized North American approach to assess privacy risks in such systems is proposed that will retain applicability as the field of research evolves and can be adapted to account for various sociopolitical influences. With such an approach, privacy risks in artificial intelligent systems can be well understood, measured, and reduced.

Artificial Intelligence (AI) techniques provide effective solutions for the detection of many aberrant network traffic patterns and attack flows. However, the validation of these techniques often relies on one training dataset. Recent results show that such training may fail in the face of dynamically-changing cyberattacks. Given the increased sophistication of cyberattacks nowadays, it is imperative to examine and improve the performance of such AI models. This paper proposes a defensive AI engine combined with a twofold feature selection technique and hyperparameter optimization of the AI model. In this work, we utilize the proposed system for binary attack flow identification and the AI models are trained and validated on the CICIDS2017 dataset. The system is then evaluated using synthesized atypical attack flows to mimic real-world scenarios. We demonstrate the effectiveness of the proposed atypical attack flow detection approach using several Deep Learning and Machine Learning models including DNN, Linear-SVC, and Stacked Decision Tree Classifier (S-DTC). Simulation results demonstrate that the proposed defensive AI engine significantly improves the True Positive Rate (TPR) of AI models on multiple atypical attacks.

Agile network security is paramount in our modern world which is currently dominated by internet systems and expanding digital spaces. This rapid digital transformation has created more opportunities for cyberattackers to exploit different vulnerabilities and launch sophisticated and continuously evolving cyberattacks. Increasingly, intrusion detection systems are relying on new methods based on Machine Learning (ML) and Deep Learning (DL) techniques to detect and mitigate such cyberattacks. While such techniques normally can identify known network attack patterns with a reasonable degree of success, their ability to identify complicated atypical, polymorphic, and unknown attacks is shown to be limited. In this paper, we present a comprehensive survey of recent research for detecting unknown, atypical, and polymorphic network attacks using DL techniques. We further highlight and discuss the main challenges in this area and identify the future research directions.