Ernesto Damiani

Khalifa University | KU · Artificial Intelligence and Intelligent Systems Institute

In recent years, malware attacks have become more and more sophisticated, reflecting a radical change in malware behavior. Attackers aim to create malware that, at each execution, generates a different number of independent and cooperating threads. Randomization of malware's division of labor among threads poses significant challenges to traditiona...

The massive deployment of Machine Learning (ML) models raises serious concerns about data protection. Privacy-enhancing technologies (PETs) offer a promising first step, but hard challenges persist in achieving confidentiality and differential privacy in distributed learning. In this paper, we describe a novel, regulation-compliant data protection...

Augmented Reality (AR) has experienced a significant resurgence in popularity and interest in recent years. Despite numerous surveys and reviews in the field, information remains scattered and challenging to consolidate for newcomers. This paper addresses this gap with a comprehensive study of AR’s state of the art. We begin with an introduction to...

Counter-drone technology plays a vital role in protecting airspace against unwanted and malicious drones. Counter-drone systems increasingly rely on unmanned traffic management services, such as remote identification and flight authorization enforcement, for the detection and mitigation of unauthorized activities on the part of Unmanned Aerial Vehi...

Cyber ranges gained great importance in cybersecurity training in the last years and it is still playing a role of paramount importance, thanks to its ability to give hands-on experience to trainees on real-world exercises. This paper presents the motivation and objective of the AERAS project, including a thorough analysis of data coming from ad-ho...

As the adoption of Internet of Things (IoT) devices increases rapidly, industrial applications of IoT devices gain further popularity. Some of these applications, such as smart grids, are considered high-risk applications. In the past few years, smart grids became the target of many cyber attacks. In this paper, we present a two-stage system for th...

Smartphone users are beyond two billion worldwide. Heavy users of the texting application rely on input prediction to reduce typing effort. In languages based on the Roman alphabet, many techniques are available. However, Japanese text is based on multiple character sets such as Kanji, Hiragana and Katakana. For its time intensive input, next word...

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R...

Machine Learning (ML) models are taking the place of conventional algorithms in a wide range of application domains. However, once ML models have been deployed in the field, they can be attacked in ways that are very different from the ones of conventional systems. This chapter reviews some of the techniques that attackers use to compromise ML-base...

IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the t...

Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detect...

Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the im...

In this paper, we improve the robustness of Machine Learning (ML) classifiers against training-time attacks by linking the risk of training data being tampered with to the redundancy in the ML model's design needed to prevent it. Our defense mechanism is directly applicable to classifiers' training data, without any knowledge of the specific ML mod...

The increasing number of Internet of Things (IoT) devices and low-cost sensors have facilitated developments in large-scale monitoring applications. However, the accuracy of low-cost sensors remains questionable. Monitoring applications, such as environmental monitoring, try to detect ’interesting’ data points or patterns, known as anomalies, that...

This work focuses on identifying the block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing t...

In this paper, the problem of distributed, multi-perspective conformance checking for Business Process Model and Notation (BPMN) is addressed. Traditionally, conformance checking has been performed centrally by a trusted entity, however that may not be applicable in the case of collaborative processes between multiple organizations. Consortium Bloc...

p>As Android smartphones continue to rise in popularity, the number of malicious programs targeting the platform has increased dramatically. Methods for efficiently detecting and preventing the spread of Android malware have become a subject of increasing urgency. The exfiltration of sensitive data from smartphones is one of the sophisticated secur...

p>As Android smartphones continue to rise in popularity, the number of malicious programs targeting the platform has increased dramatically. Methods for efficiently detecting and preventing the spread of Android malware have become a subject of increasing urgency. The exfiltration of sensitive data from smartphones is one of the sophisticated secur...

The evolution of 5G and 6G networks has enhanced the ability of massive IoT devices to provide real-time monitoring and interaction with the surrounding environment. Despite recent advances, the necessary security services, such as immediate and continuous authentication, high scalability, and cybersecurity handling of IoT cannot be achieved in a s...

Trace clustering has been extensively used to discover aspects of the data from event logs. Process Mining techniques guide the identification of sub-logs by grouping traces with similar behaviors, producing more understandable models, and improving conformance indicators. Nevertheless, little attention has been posed to the relationship among even...

Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the im...

In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN-RS embeds a CNN trained on computed labels using the message passing mechanism of the Hidden Markov Model. The CNN processes images or graphs of the environment to predi...

In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behind this research is to design an effective approach filtering out hybrid spam e-mails to avoid situa...

Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system should have the capacity to accu...

Most recent studies have shown several vulnerabilities to attacks with the potential to jeopardize the integrity of the model, opening in a few recent years a new window of opportunity in terms of cyber-security. The main interest of this paper is directed towards data poisoning attacks involving label-flipping, this kind of attacks occur during th...

Machine learning is becoming ubiquitous. From financial to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fr...

Traffic management continues to be one of the most critical challenges facing smart cities. Timely detection of incidents plays an important role in reducing fatality rates, avoiding congestion and improving traffic conditions. Currently, traditional traffic event detection approaches often rely on one source of data, such as road sensor readings o...

Analyzing event logs generated during the execution of digital processes, organizations can monitor the behavior of dysfunctional or unspecified processes. For achieving the most refined results, high-quality and up-to-date process models are required. However, the selection of the proper process discovery algorithm is often addressed by human expe...

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image...

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that co...

Information-Centric Networking is an emerging alternative to host-centric networking designed for large-scale content distribution and stricter privacy requirements. Recent research on Information-Centric Networking focused on the protection of the network from attacks targeting the content delivery protocols, while assuming genuine content can alw...

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With...

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely...

X-ray imagery systems have enabled security personnel to identify potential threats contained within the baggage and cargo since the early 1970s. However, the manual process of screening the threatening items is time-consuming and vulnerable to human error. Hence, researchers have utilized recent advancements in computer vision techniques, revoluti...

p>The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, w...

p>The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, w...

p> Data download and storage over wireless networks is a popular application for various multimedia such as images, audio, and video files. In such applications, the end-user may listen to or watch the downloaded media in real-time, and/or will playback the downloaded file multiple times in the future. Consequently, improving the quality of the sto...

p> Data download and storage over wireless networks is a popular application for various multimedia such as images, audio, and video files. In such applications, the end-user may listen to or watch the downloaded media in real-time, and/or will playback the downloaded file multiple times in the future. Consequently, improving the quality of the sto...

The rise of the Internet has enabled new types of relationships and online domains where trust must be computed rather than earned. However, classic trust and reputation techniques designed to support computational trust among Internet entities do not meet the scalability requirements and the resource constraints typical of Internet of Things (IoT)...

In this paper, we consider the problem of low-speed convergence in Reinforcement Learning (RL). As a solution, various potential-based reward shaping techniques were proposed to form the potential function. Learning a potential function is still challenging and comparable to building a value function from scratch. In this work, our main contributio...

This paper presents a counseling (ro)bot called Visual Counseling Agent (VICA) which focuses on remote mental healthcare. It is an agent system leveraging artificial intelligence (AI) to aid mentally distressed persons through speech conversation. The system terminals are connected to servers by the Internet exploiting Cloud-nativeness, so that any...

Global and local whole genome sequencing of SARS-CoV-2 enables the tracing of domestic and international transmissions. We sequenced Viral RNA from 37 sampled Covid-19 patients with RT-PCR-confirmed infections across the UAE and developed time-resolved phylogenies with 69 local and 3,894 global genome sequences. Furthermore, we investigated specifi...

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection framew...

Machine learning models have been widely adopted in several fields. However, most recent studies have shown several vulnerabilities from attacks with a potential to jeopardize the integrity of the model, presenting a new window of research opportunity in terms of cyber-security. This survey is conducted with a main intention of highlighting the mos...

Visible light communication (VLC) technology was introduced as a key enabler for the next generation of wireless networks, mainly thanks to its simple and low-cost implementation. However, several challenges prohibit the realization of the full potential of VLC, namely, limited modulation bandwidth, ambient light interference, optical diffuse refle...

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm for Mobile Crowdsensing (MCS) environments. In a given Area of Interest (AoI), the sensing process is performed based on task requirements, which usually ask for a specific quality of the sensing outcome. In this work, a two-stage Data-Driven Decision-making Mechanism using sm...

Over the last few years, the interest in blockchain platforms has fostered the implementation of a number of distributed ledger-based solutions for the exchange of information, assets and digitized goods in both the private and the public sectors. While proposing promising alternatives to the original Bitcoin protocol is an important goal that the...

Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Strea...

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision- making tools tailored to it. This is because the management of health conditions and their consequen...

This study discusses the general overview of Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol, including its properties, key setups, and improvement protocols. The discussion includes a new proposed two-level infinite $\mu $ TESLA (TLI $\mu $ TESLA) protocol that solves the authentication delay and synchronization issues. We...

Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, cryptographic hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance with...