Luca Caviglione

Enrico Cambiaso, Luca Caviglione

The use of Artificial Intelligence (AI) to support cybersecurity operations is now a consolidated practice, e.g., to detect malicious code or configure traffic filtering policies. The recent surge of AI, generative techniques and frameworks with efficient natural language processing capabilities dramatically magnifies the number of possible applications aimed at increasing the security of the Internet. Specifically, the ability of ChatGPT to produce textual contents while mimicking realistic human interactions can be used to mitigate the plague of emails containing scams. Therefore, this paper investigates the use of AI to engage scammers in automatized and pointless communications, with the goal of wasting both their time and resources. Preliminary results showcase that ChatGPT is able to decoy scammers, thus confirming that AI is an effective tool to counteract threats delivered via mail. In addition, we highlight the multitude of implications and open research questions to be addressed in the perspective of the ubiquitous adoption of AI.

Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk et al.

Steganography embraces several hiding techniques which spawn across multiple domains. However, the related terminology is not unified among the different domains, such as digital media steganography, text steganography, cyber-physical systems steganography, network steganography (network covert channels), local covert channels, and out-of-band covert channels. To cope with this, a prime attempt has been done in 2015, with the introduction of the so-called hiding patterns, which allow to describe hiding techniques in a more abstract manner. Despite significant enhancements, the main limitation of such a taxonomy is that it only considers the case of network steganography. Therefore, this paper reviews both the terminology and the taxonomy of hiding patterns as to make them more general. Specifically, hiding patterns are split into those that describe the embedding and the representation of hidden data within the cover object. As a first research action, we focus on embedding hiding patterns and we show how they can be applied to multiple domains of steganography instead of being limited to the network scenario. Additionally, we exemplify representation patterns using network steganography. Our pattern collection is available under https://patterns.ztt.hs-worms.de.

Andrea Ranieri, Davide Caputo, Luca Verderame et al.

Smart speakers and voice-based virtual assistants are core components for the success of the IoT paradigm. Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic. To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information. This letter showcases the inadequacy of such countermeasures against machine learning attacks with a dedicated experimental campaign on a real network dataset. Results indicate the need for a major re-engineering to guarantee the suitable protection of commercially available smart speakers.

Wojciech Mazurczyk, Luca Caviglione

Information hiding techniques are increasingly utilized by the current malware to hide its existence and communication attempts. In this paper we highlight this new trend by reviewing the most notable examples of malicious software that shows this capability.

Luca Caviglione, Jean-Francois Lalande, Wojciech Mazurczyk et al.

Smart environments integrate Information and Communication Technologies (ICT) into devices, vehicles, buildings and cities to offer an increased quality of life, energy efficiency and economical sustainability. In this perspective, the individual has a core role and so has networking, which enables such entities to cooperate. However, the huge amount of sensitive data, social aspects and the mixed set of protocols offer many opportunities to inject hazards, exfiltrate information, mass profiling of citizens, or produce a new wave of attacks. This work reviews the major risks arising from the usage of ICT-techniques for smart environments, with emphasis on networking. Its main contribution is to explain the role of different stakeholders for causing a lack of security and to envision future threats by considering human aspects.

Luca Caviglione, Wojciech Mazurczyk

The Apple operating system (iOS) has so far proved resistant to information-hiding techniques, which help attackers covertly communicate. However, Siri - a native iOS service that controls iPhones and iPads via voice commands - could change this trend.

Wojciech Mazurczyk, Luca Caviglione

By offering sophisticated services and centralizing a huge volume of personal data, modern smartphones changed the way we socialize, entertain and work. To this aim, they rely upon complex hardware/software frameworks leading to a number of vulnerabilities, attacks and hazards to profile individuals or gather sensitive information. However, the majority of works evaluating the security degree of smartphones neglects steganography, which can be mainly used to: i) exfiltrate confidential data via camouflage methods, and ii) conceal valuable or personal information into innocent looking carriers. Therefore, this paper surveys the state of the art of steganographic techniques for smartphones, with emphasis on methods developed over the period 2005 to the second quarter of 2014. The different approaches are grouped according to the portion of the device used to hide information, leading to three different covert channels, i.e., local, object and network. Also, it reviews the relevant approaches used to detect and mitigate steganographic attacks or threats. Lastly, it showcases the most popular software applications to embed secret data into carriers, as well as possible future directions.

Steffen Wendzel, Wojciech Mazurczyk, Luca Caviglione et al.

Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.