The emergence of federated learning has alleviated the dual challenges of data silos and data privacy and security in machine learning. However, this distributed learning approach makes it more susceptible to backdoor attacks, where malicious participants can conduct adversarial attacks by injecting backdoor triggers into their local training datasets, aiming to manipulate model predictions, for example, make the classifier recognize poisoned samples (injected with specific triggers) as specific images. In order to effectively detect backdoor attacks and protect federated learning systems, we need to know how backdoor attacks are generated and developed. Currently, most backdoor attacks to federated learning use centralized attacks with static triggers, which are easily detectable by current defense methods. In this work, we propose a distributed backdoor attack method that fully leverages the distributed nature of federated learning. It starts by generating unique and independent global dynamic triggers for infected benign samples and then decomposes the global trigger into multiple sub-triggers, embedding them into the training sets of multiple participants. During the training phase, data poisoning is introduced. Through extensive experiments, we demonstrate that this attack method exhibits higher persistence and stealthiness, achieving a significantly higher success rate than standard centralized backdoor attacks. Compared to classical distributed backdoor attack (DBA) methods, it shows noticeable improvements in attack performance.
We consider cyber-physical systems equipped with sensors and actuators controlled or monitored by software and communication capabilities. Such use of communication makes sensors and actuators possibly vulnerable to cyberattacks (more briefly: attacks). We consider two approaches to study sensor and actuator attacks. The cryptographic approach is to design each sensor and actuator to be invulnerable to attacks, regardless of the system in which it is deployed. The discrete-event system (DES)-based approach is to study the detection of sensor and actuator attacks by observing the behavior of the whole system, without adding any mechanism to sensors and actuators to make them invulnerable. In this paper, we introduce and compare the two approaches and suggest an efficient way to combine them. A concrete case study is presented.
Achieving user privacy is a challenging task in the era of ubiquitous internet and mobile applications where users’ data and activities can be tracked days and nights, by the myriads of applications installed on smartphones, watches, TV, and appliances. The need for a practical, easy to use, and most of all expressive tools to express and enforce privacy rules is still insistent nowadays. Even though the law enforces in many regions of the world the user data protection as a legal principle, still mobile apps and web applications and plugins lack of a steady process that allows users to express, validate and verify their privacy rules when they are interacting with a given application. We introduce in this paper a new approach, which combines both non monotonic logics with the Linear temporal logic (LTL) to define and interpret time-constrained logical predicates used to define data sharing functions based on time such as Periodic Blocking, Time prohibition, and Data shelf life. This paper is a first step towards a time-dependent knowledge base system for privacy rule expression and enforcement.
Ransomware stands out as a particularly malicious type of cyberattack, wielding the potential to inflict severe financial, operational, and reputational harm. The insidious nature of ransomware, with their ability to encrypt or exfiltrate sensitive data, demands a paradigm shift in cybersecurity strategies. Further, the ability to predict and forecast such malware is paramount for bolstering overall cybersecurity resilience. In this paper, we provide a comprehensive overview of the evolution of ransomware, detailing key characteristics and specifications of zero-day ransomware, along with limitations in static and dynamic analyses, with the goal of identifying effective defense strategies. Despite substantial advancements, enduring challenges and notable research gaps persist, necessitating ongoing innovation in cybersecurity. Furthermore, this study serves as a valuable resource for cybersecurity professionals and researchers, shedding light on key trends, challenges, limitations, and potential avenues for future research in the realm of ransomware forecasting.
Our objective is to create a transparent authentication factor using existing hardware and information already present. Transparent authentication refers to not burdening the user with interaction, and therefore a transparent authentication factor is applicable not only at the beginning of a session but continuously during an authenticated session. We choose to utilize the WiFi environment of a user, as it is ubiquitous in terms of the presence of WiFi signals and user hardware. As we intend our contribution as an addition to stand-alone passwords or existing multifactor-authentication schemes, we decided to build on the concept of separated authentication channels used in state-of-the-art multifactor authentication. To do so, we require two devices. Measuring the WiFi environment from two points enables us to use the proximity of devices as the additional authentication claim. In this work, we demonstrate that it is feasible to use WiFi to identify the proximity of devices. We analyze two scenarios, a semi-densely populated apartment environment and a densely populated office environment in terms of WiFi access points. In the apartment scenario, we show that SPAWN provides at least as much entropy as a traditional password, while not requiring the user to retype a low-entropy token. In the office scenario, this amount of entropy can still be derived in 74% of the measures. By applying private set metrics, we investigate and demonstrate that a device’s proximity can be employed as an authentication factor without compromising privacy.
