This survey explores the transformative role of Generative Artificial Intelligence (GenAI) in enhancing the trustworthiness, reliability, and security of autonomous systems such as Unmanned Aerial Vehicles (UAVs), self-driving cars, and robotic arms. As edge robots become increasingly integrated into daily life and critical infrastructure, the complexity and connectivity of these systems introduce formidable challenges in ensuring security, resilience, and safety. GenAI advances from mere data interpretation to autonomously generating new data, proving critical in complex, context-aware environments like edge robotics. Our survey delves into the impact of GenAI technologies—including Generative Adversarial Networkss (GANs), Variational Autoencoderss (VAEs), Transformer-based models, and Large Language Models (LLMs)—on cybersecurity, decision-making, and the development of resilient architectures. We categorize existing research to highlight how these technologies address operational challenges and innovate predictive maintenance, anomaly detection, and adaptive threat response. Our comprehensive analysis distinguishes this work from existing reviews by mapping out the applications, challenges, and technological advancements of GenAI and their impact on creating secure frameworks for autonomous systems. We discuss significant challenges and future directions for integrating these technologies within security frameworks to address the evolving landscape of cyber-physical threats, underscoring the potential of GenAI to make autonomous systems more adaptive, secure, and efficient.

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

Distributed stream processing platforms is a new class of real-time monitoring systems that analyze and extracts knowledge from large continuous streams of data. This type of systems is crucial for providing high throughput and low latency required by Big Data or Internet of Things monitoring applications. This paper describes and analyzes three main open-source distributed stream- processing platforms: Storm Flink, and Spark Streaming. We analyze the system architectures and we compare their main features. We carry out two experiments concerning anomaly detection on network traffic to evaluate the throughput efficiency and the resilience to node failures. Results show that the performance of native stream processing systems, Storm and Flink, is up to 15 times higher than the micro-batch processing system, Spark Streaming. On the other hand, Spark Streaming is more robust to node failures and provides recovery without losses.

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based IDS play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional Autoencoder for unsupervised network anomaly DEtection). With a convolutional AE, ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network. ARCADE is trained exclusively on normal traffic. An adversarial training strategy is proposed to regularize and decrease the AE’s capabilities to reconstruct network flows that are out-of-the-normal distribution, thereby improving its anomaly detection capabilities. The proposed approach is more effective than state-of-the-art deep learning approaches for network anomaly detection. Even when examining only two initial packets of a network flow, ARCADE can effectively detect malware infection and network attacks. ARCADE presents 20 times fewer parameters than baselines, achieving significantly faster detection speed and reaction time.

In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.