Search results for "NETWORK"
showing 10 items of 7718 documents
Building Resilience Through Effective Disaster Management
2017
Existing literature argues that taking a holistic approach to disaster management is important for organizations in building resilience. Theoretical underpinnings to achieve a holistic understanding, however, is lacking. This article applies the notion of an ecosystem as a holistic lens to understand complex disaster management. The paper reports two case studies from Japan and Nepal to illustrate how an ecosystem works during a disaster. The theoretical framework of information ecology is used in analyzing the cases. Based on the findings, the study shows three interconnected mechanisms that can build resilience of an ecosystem in a disaster management context, namely (1) coevolution, (2) …
A New Intelligent Technique of Constructing Optimal Airline Seat Protection Levels for Multiple Nested Fare Classes of Single-Leg Flights
2019
A new, rigorous formulation of the optimization problem of airline seat protection levels for multiple nested fare classes is presented. A number of results useful for practical application are obtained. A numerical example is given.
HyperWall: A Hypervisor for Detection and Prevention of Malicious Communication
2020
Malicious programs vary widely in their functionality, from key-logging to disk encryption. However, most malicious programs communicate with their operators, thus revealing themselves to various security tools. The security tools incorporated within an operating system are vulnerable to attacks due to the large attack surface of the operating system kernel and modules. We present a kernel module that demonstrates how kernel-mode access can be used to bypass any security mechanism that is implemented in kernel-mode. External security tools, like firewalls, lack important information about the origin of the intercepted packets, thus their filtering policy is usually insufficient to prevent c…
Deep in the Dark: A Novel Threat Detection System using Darknet Traffic
2019
This paper proposes a threat detection system based on Machine Learning classifiers that are trained using darknet traffic. Traffic destined to Darknet is either malicious or by misconfiguration. Darknet traffic contains traces of several threats such as DDoS attacks, botnets, spoofing, probes and scanning attacks. We analyse darknet traffic by extracting network traffic features from it that help in finding patterns of these advanced threats. We collected the darknet traffic from the network sensors deployed at SURFnet and extracted several network-based features. In this study, we proposed a framework that uses supervised machine learning and a concept drift detector. Our experimental res…
On Detection of Network-Based Co-residence Verification Attacks in SDN-Driven Clouds
2017
Modern cloud environments allow users to consume computational and storage resources in the form of virtual machines. Even though machines running on the same cloud server are logically isolated from each other, a malicious customer can create various side channels to obtain sensitive information from co-located machines. In this study, we concentrate on timely detection of intentional co-residence attempts in cloud environments that utilize software-defined networking. SDN enables global visibility of the network state which allows the cloud provider to monitor and extract necessary information from each flow in every virtual network in online mode. We analyze the extracted statistics on d…
Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning
2019
An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up …
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Modelli di parenting multipli e benessere interpersonale. Una rassegna teorica sull’evoluzione del sistema motivazionale diadico verso il network del…
2016
Models of multiple parenting and interpersonal wellbeing. A theoretical review on the evolution of the dyadic motivational system toward the network of care A theoretical analysis on the evolution of the dyadic parenting model toward the multiple attachment perspective is presented. In particular, looking beyond the monotropic perspective (Bowlby, 1969), a theoretical framework about the integration model as a support of the elasticity of the Internal Working Models is highlighted. Based on these studies, the continuity in the quality of the attachment representations would remain in function also of the changes inside the care environment, in its aspects of risk and protection as well as o…
Recent advances of the multipactor RF breakdown in RF satellite microwave passive devices
2016
The main goal of this work is the review of the recent advances in the study of the multipactor RF breakdown phenomenon in RF satellite microwave passive devices for space telecommunication applications developed in the Val Space Consortium. In this work several topics related with the multipactor phenomenon will be discussed.
Gender, Coping, and Mental Health: a Bayesian Network Model Analysis
2016
We examined the relationships among gender, coping, and mental health in terms of probabilities. We selected a sample of university students (N = 131) aged between 18 and 32 years, and used the GHQ-28 and COPE instruments for analysis. The Bayesian network model that we constructed showed higher probabilities of symptoms of mental health problems for emotion-focused coping than for problem-focused coping. No differences were found regarding gender. This suggests that the use of problem-focused coping is more recommendable for both male and female university students, and it may also provide some benefits in terms of treatment of symptoms of mental health problems. However, to further verify…