20 research outputs found
FedComm: Federated Learning as a Medium for Covert Communication
Proposed as a solution to mitigate the privacy implications related to the
adoption of deep learning, Federated Learning (FL) enables large numbers of
participants to successfully train deep neural networks without having to
reveal the actual private training data. To date, a substantial amount of
research has investigated the security and privacy properties of FL, resulting
in a plethora of innovative attack and defense strategies. This paper
thoroughly investigates the communication capabilities of an FL scheme. In
particular, we show that a party involved in the FL learning process can use FL
as a covert communication medium to send an arbitrary message. We introduce
FedComm, a novel multi-system covert-communication technique that enables
robust sharing and transfer of targeted payloads within the FL framework. Our
extensive theoretical and empirical evaluations show that FedComm provides a
stealthy communication channel, with minimal disruptions to the training
process. Our experiments show that FedComm successfully delivers 100% of a
payload in the order of kilobits before the FL procedure converges. Our
evaluation also shows that FedComm is independent of the application domain and
the neural network architecture used by the underlying FL scheme.Comment: 18 page
Minerva: A File-Based Ransomware Detector
Ransomware is a rapidly evolving type of malware designed to encrypt user
files on a device, making them inaccessible in order to exact a ransom.
Ransomware attacks resulted in billions of dollars in damages in recent years
and are expected to cause hundreds of billions more in the next decade. With
current state-of-the-art process-based detectors being heavily susceptible to
evasion attacks, no comprehensive solution to this problem is available today.
This paper presents Minerva, a new approach to ransomware detection. Unlike
current methods focused on identifying ransomware based on process-level
behavioral modeling, Minerva detects ransomware by building behavioral profiles
of files based on all the operations they receive in a time window. Minerva
addresses some of the critical challenges associated with process-based
approaches, specifically their vulnerability to complex evasion attacks. Our
evaluation of Minerva demonstrates its effectiveness in detecting ransomware
attacks, including those that are able to bypass existing defenses. Our results
show that Minerva identifies ransomware activity with an average accuracy of
99.45% and an average recall of 99.66%, with 99.97% of ransomware detected
within 1 second.Comment: 19 pages, 3 figure
MaleficNet: Hiding Malware into Deep Neural Networks Using Spread-Spectrum Channel Coding
<p>The training and development of good deep learning models is often a challenging task, thus leading individuals (developers, researchers, and practitioners alike) to use third-party models residing in public repositories, fine-tuning these models to their needs usually with little-to-no effort. Despite its undeniable benefits, this practice can lead to new attack vectors. In this paper, we demonstrate the feasibility and effectiveness of one such attack, namely malware embedding in deep learning models. We push the boundaries of current state-of-the-art by introducing MaleficNet, a technique that combines spread-spectrum channel coding with error correction techniques, injecting malicious payloads in the parameters of deep neural networks, all while causing no degradation to the model's performance and successfully bypassing state-of-the-art detection and removal mechanisms. We believe this work will raise awareness against these new, dangerous, camouflaged threats, assist the research community and practitioners in evaluating the capabilities of modern machine learning architectures, and pave the way to research targeting the detection and mitigation of such threats.</p>
Evading behavioral classifiers: a comprehensive analysis on evading ransomware detection techniques
Recent progress in machine learning has led to promising results in behavioral malware detection. Behavioral modeling identifies malicious processes via features derived by their runtime behavior. Behavioral features hold great promise as they are intrinsically related to the functioning of each malware, and are therefore considered difficult to evade. Indeed, while a significant amount of results exists on evasion of static malware features, evasion of dynamic features has seen limited work. This paper examines the robustness of behavioral ransomware detectors to evasion and proposes multiple novel techniques to evade them. Ransomware behavior differs significantly from that of benign processes, making it an ideal best case for behavioral detectors, and a difficult candidate for evasion. We identify and propose a set of novel attacks that distribute the overall malware workload across a small set of independent, cooperating processes in order to avoid the generation of significant behavioral features. Our most effective attack decreases the accuracy of a state-of-the-art classifier from 98.6 to 0% using only 18 cooperating processes. Furthermore, we show our attacks to be effective against commercial ransomware detectors in a black-box setting. Finally, we evaluate a detector designed to identify our most effective attack, as well as discuss potential directions to mitigate our most advanced attack
Um robô linguista que ‘ouve’ e ‘fala’: GeolinguÃstica, pln e tabelas hash em concurso
Este estudo tem o objetivo apresentar o robô de conversação Professor Tical numa dimensão mais ampla e com os recursos de sÃntese e comandos por voz. Tical, que se tornou operacional como protótipo durante do III Congresso Internacional de Dialetologia e SociolinguÃstica na UEL em 2014, continua sendo um campo de provas para aplicações de algumas teorias que mantém entre si um caráter interdisciplinar como GeolinguÃstica (CARDOSO, 2014a, 2014b), Processamento de Linguagem Natural (RICH, 1993; SCHILDT, 1989; MANFIO; MORENO; BARBOSA, 2014a, 2014b) e Processamento de Dados (ZIVIANI, 1999). O ‘robô linguista’, mesmo dispondo de um banco de dados bastante limitado e apresentando várias falhas tÃpicas de sistemas dessa natureza e dotados desses recursos, serviu grandemente para realizar testes relativos a todas as áreas de conhecimento envolvidas e mostrou-se funcional o suficiente para suscitar a necessidade de dar continuidade ao projeto e à s pesquisas: ‘ouve’, ‘fala’, realiza buscas rápidas e ‘conhece linguÃstica’.
DOLOS: A Novel Architecture for Moving Target Defense
Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asymmetric disadvantage for the attacker by using deception and randomization techniques to create a dynamic attack surface. Moving Target Defense (MTD) typically relies on system randomization and diversification, while Cyber Deception is based on decoy nodes and fake systems to deceive attackers. However, current Moving Target Defense techniques are complex to manage and can introduce high overheads, while Cyber Deception nodes are easily recognized and avoided by adversaries. This paper presents DOLOS, a novel architecture that unifies Cyber Deception and Moving Target Defense approaches. DOLOS is motivated by the insight that deceptive techniques are much more powerful when integrated into production systems rather than deployed alongside them. DOLOS combines typical Moving Target Defense techniques, such as randomization, diversity, and redundancy, with cyber deception and seamlessly integrates them into production systems through multiple layers of isolation. We extensively evaluate DOLOS against a wide range of attackers, ranging from automated malware to professional penetration testers, and show that DOLOS is effective in slowing down attacks and protecting the integrity of production systems. We also provide valuable insights and considerations for the future development of MTD techniques based on our findings
Clinical outcomes of bioresorbable versus durable polymercoated everolimus-eluting stents in real-world complex patients
AIMS: The aim of this study was to evaluate the safety and efficacy profile of new-generation, SYNERGY everolimus-eluting stents (S-EES) as compared to XIENCE everolimus-eluting stents (X-EES) with a durable polymer coating in "complex patients".
METHODS AND RESULTS: We included 2,001 consecutive patients treated with S-EES (n=400) or X-EES (n=1,601) at two Italian centres between May 2013 and May 2015. We used propensity score matching to obtain two cohorts of patients with similar baseline risk profiles. Patients were stratified according to baseline complexity based on the EVOLVE II trial exclusion criteria. The primary outcome was major adverse cardiac events (MACE), defined as the composite of all-cause death, myocardial infarction (MI), and target lesion revascularisation (TLR), at one year. Among 391 matched pairs of patients treated with S-EES or X-EES, we identified 253 (63%) as complex. At one-year follow-up, among "complex" patients, MACE rates did not differ between the S-EES and X-EES groups (9.9% vs. 9.5%, p=0.830, HR 1.04, CI: 0.72-1.48). Similarly, death, MI, and TLR, stratified for complexity, were comparable between S-EES and X-EES treated patients at one year. Of note, no definite ST was observed in either the S-EES or the X-EES cohort.
CONCLUSIONS: New-generation S-EES with a bioresorbable polymer coating appear to be safe and effective irrespective of patient complexity as compared to X-EES
NATURA CHE M'ISPIRI.ALCUNI PERCORSI LETTERARI, LINGUISTICI, ARCHEOLOGICI, GEOGRAFICI
Pensare la Natura è il tema portante del volume, una miscellanea di contributi , che da diverse propsettive offrono un'analisi ampia, nuova e accattivante . Il carattere multidisciplinare dell'opera offre una lettura che spazia dall'archeologia alla papirologia, dalle letterature classiche a quelle moderne e contemporanee, nonché alle discipline propriamente geografiche