Classification of Abnormal Signaling SIP Dialogs through Deep Learning

POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021Due to the high utilization of the Session Initiation Protocol (SIP) in the signaling of cellular networks and voice over IP multimedia systems, the avoidance of security vulnerabilities in SIP systems is a major aspect to assure that the operators can reach satisfactory readiness levels of service. This work is focused on the detection and prediction of abnormal signaling SIP dialogs as they evolve. Abnormal dialogs include two classes: the ones observed so far and thus labeled as abnormal and already known, but also the unknown ones, i.e., specific sequences of SIP messages never observed before. Taking advantage of recent advances in deep learning, we use Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) to detect and predict dialogs already observed. Additionally, and based on the outputs of the LSTM neural network, we propose two different classifiers capable of identifying unknown SIP dialogs, given the high level of vulnerability they may represent for the SIP operation. The proposed approaches achieve higher SIP dialogs detection scores in a shorter time when compared to a reference probabilistic-based approach. Moreover, the proposed detectors of unknown SIP dialogs achieve a detection probability above 94%, indicating its capability to detect a significant number of unknown SIP dialogs in a short amount of time.publishersversionpublishe

Kinetics versus thermodynamics dichotomy and growth -mechanisms in linear self-assembly of mixed nanoblocks

Self-assembly is a low energy synthesis process, prominent in biological systems, in which smaller building blocks spontaneously associate to form highly organized structures of great complexity. Thus, it is one of the most promising strategies to engineer hierarchical functional nanostructures. Of special interest are one-dimensional arrays of nanobuilding blocks (e.g., nanoparticles, peptides, colloids, etc.), such as nanowires, nanotubes or polymer-like structures, due to their potential applications ranging from nanosensing, optoelectronics, or molecular selective transport to mechanical reinforcement in structural composites. However, despite considerable advances on the synthesis side in the past few years, there is lack of understanding of the physics underlying the self-assembly kinetics and the mixing of diverse building blocks in low-dimensional structures. Short-term kinetic mechanisms of growth (in the order of nanoseconds to milliseconds) are difficult to reach with computationally intensive molecular simulations and yet occur too rapidly to be resolved with experiments. The growth mechanisms and kinetics of systems such as peptide nanotubes remain unexplored. Regarding supramolecular organization, the effect of kinetic traps on the formation of arrested phases is not yet fully understood, particularly in systems where self-assembly is driven by enthalpic interactions and nonequilibrium configurations are prevalent. Here, we present recent results from classical and replica exchange molecular dynamics simulations that establish the mechanisms underpinning the growth kinetics of a binary mix of nanorings that form striped nanotubes via self-assembly type. We show that a step-growth coalescence model captures adequately the growth process of the nanotubes, which suggests that high-aspect ratio nanostructures can grow by obeying the universal laws of self-similar coarsening, contrary to existing belief that growth occurs exclusively through nucleation and elongation (e.g., amyloid fibrils). Notably, we found that striped patterns do not depend on specific growth mechanisms, but are governed by tempering conditions that control the likelihood of depropagation and fragmentation

Trade-off analysis and design of a Hydraulic Energy Scavenger

In the last years there has been a growing interest in intelligent, autonomous devices for household applications. In the near future this technology will be part of our society; sensing and actuating will be integrated in the environment of our houses by means of energy scavengers and wireless microsystems. These systems will be capable of monitoring the environment, communicating with people and among each other, actuating and supplying themselves independently. This concept is now possible thanks to the low power consumption of electronic devices and accurate design of energy scavengers to harvest energy from the surrounding environment. In principle, an autonomous device comprises three main subsystems: an energy scavenger, an energy storage unit and an operational stage. The energy scavenger is capable of harvesting very small amounts of energy from the surroundings and converting it into electrical energy. This energy can be stored in a small storage unit like a small battery or capacitor, thus being available as a power supply. The operational stage can perform a variety of tasks depending on the application. Inside its application range, this kind of system presents several advantages with respect to regular devices using external energy supplies. They can be simpler to apply as no external connections are needed; they are environmentally friendly and might be economically advantageous in the long term. Furthermore, their autonomous nature permits the application in locations where the local energy grid is not present and allows them to be ‘hidden' in the environment, being independent from interaction with humans. In the present paper an energy-harvesting system used to supply a hydraulic control valve of a heating system for a typical residential application is studied. The system converts the kinetic energy from the water flow inside the pipes of the heating system to power the energy scavenger. The harvesting unit is composed of a hydraulic turbine that converts the kinetic energy of the water flow into rotational motion to drive a small electric generator. The design phases comprise a trade-off analysis to define the most suitable hydraulic turbine and electric generator for the energy scavenger, and an optimization of the components to satisfy the systems specification

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented

Accomplishments and challenges in stem cell imaging in vivo.

Stem cell therapies have demonstrated promising preclinical results, but very few applications have reached the clinic owing to safety and efficacy concerns. Translation would benefit greatly if stem cell survival, distribution and function could be assessed in vivo post-transplantation, particularly in patients. Advances in molecular imaging have led to extraordinary progress, with several strategies being deployed to understand the fate of stem cells in vivo using magnetic resonance, scintigraphy, PET, ultrasound and optical imaging. Here, we review the recent advances, challenges and future perspectives and opportunities in stem cell tracking and functional assessment, as well as the advantages and challenges of each imaging approach

Comparing Human and Machine Errors in Conversational Speech Transcription

Recent work in automatic recognition of conversational telephone speech (CTS) has achieved accuracy levels comparable to human transcribers, although there is some debate how to precisely quantify human performance on this task, using the NIST 2000 CTS evaluation set. This raises the question what systematic differences, if any, may be found differentiating human from machine transcription errors. In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline. We find that the most frequent substitution, deletion and insertion error types of both outputs show a high degree of overlap. The only notable exception is that the automatic recognizer tends to confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans tend not to make this error, presumably due to the distinctive and opposing pragmatic functions attached to these words. Furthermore, we quantify the correlation between human and machine errors at the speaker level, and investigate the effect of speaker overlap between training and test data. Finally, we report on an informal "Turing test" asking humans to discriminate between automatic and human transcription error cases