SerpinB3 and Yap Interplay Increases Myc Oncogenic Activity

SerpinB3 has been recently described as an early marker of liver carcinogenesis, but the potential mechanistic role of this serpin in tumor development is still poorly understood. Overexpression of Myc often correlates with more aggressive tumour forms, supporting its involvement in carcinogenesis. Yes-associated protein (Yap), the main effector of the Hippo pathway, is a central regulator of proliferation and it has been found up-regulated in hepatocellular carcinomas. The study has been designed to investigate and characterize the interplay and functional modulation of Myc by SerpinB3 in liver cancer. Results from this study indicate that Myc was up-regulated by SerpinB3 through calpain and Hippo-dependent molecular mechanisms in transgenic mice and hepatoma cells overexpressing human SerpinB3, and also in human hepatocellular carcinomas. Human recombinant SerpinB3 was capable to inhibit the activity of Calpain in vitro, likely reducing its ability to cleave Myc in its non oncogenic Myc-nick cytoplasmic form. SerpinB3 indirectly increased the transcription of Myc through the induction of Yap pathway. These findings provide for the first time evidence that SerpinB3 can improve the production of Myc through direct and indirect mechanisms that include the inhibition of generation of its cytoplasmic form and the activation of Yap pathway

A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability

Performance prediction of cloud applications through benchmarking and simulation

As the cloud paradigm gains widespread adoption, the performance evaluation and prediction of cloud applications remain daunting tasks, not yet fully accomplished. Nevertheless, reliable performance figures are the key to take the cloud to the next step, in which it will be possible to predict the maintenance cost of the applications and to introduce richer service level agreements between service providers and consumer. In this paper, we propose a methodology based on benchmarking and simulation that aims at predicting the performance of cloud applications developed through the mOSAIC framework. We prove the efficacy of the methodology on a real case study, showing how it is possible to predict performance indexes (throughput, message queue length,...) under a generic workload, using pre-acquired benchmark results and simple simulation models

Concurrent simulation in the cloud with the mJADES framework

In this paper, we discuss the design and implementation of mJADES, a new simulation engine that runs on top of an ad-hoc federation of cloud providers and is designed to perform multiple concurrent simulations. These features make mJADES an attractive environment for the simulation of complex systems, in which it is often desirable to perform many simulation runs, either for statistical validation or to compare the system behaviour in several different conditions. Given a set of simulation tasks, mJADES is able automatically to acquire the computing resources needed from the cloud, and to distribute the simulation runs to be executed

SLAs for cloud applications: Agreement protocol and REST-based implementation

Users with critical data are still reluctant to move apps and data to commercial clouds, showing a substantial lack of trust in providers. Possible risks linked to availability, performance and security may be mitigated by the adoption of Service Level Agreements (SLAs) established among cloud service providers and their customers. This paper presents the design of services for the automatic management of cloud-oriented SLAs by means of a REST-based API. The API is exposed by an SLA Manager component that can be easily integrated into existing cloud applications, platforms and infrastructures, in order to support SLA-based cloud services delivery. Its functionalities have been designed according to an extended version of the agreement protocol state diagram proposed by the WS-Agreement standard, which takes explicitly into account negotiation, remediation and renegotiation issues and is compliant with all the active standards on security