53,924 research outputs found
An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks
Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91%), recall (99.92%) and F1 score (99.91%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model
Design and validation of a meter band rate in OpenFlow and OpenDaylight for optimizing QoS
Technological developments in the Internet and communications have created a vastly complex and dynamic context with diverse heterogeneous networks and fast growth of mobile devices and multimedia. As the Internet becomes the primary mode of communication for many organisations there is requirement to enhance quality of service (QoS) from heterogeneous systems and networks. Traditional networks such as TETRA have become increasingly incapable of addressing the demand for media rich, bandwidth intensive traffic flows and applications. Mission-critical multimedia over new generation mobile networks face QoS constraints. This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks. A Meter Band Rate Evaluation (MBE) mechanism is advanced that improves the native QoS capability of OpenFlow and OpenDaylight. The MBE is a physical component added to the OpenFlow meter table to evaluate and dynamically adjust traffic rates and allows the traffic volume to be specified relative to other traffic in the network. Its design and development are presented and the mechanism is verified through a simulated experiment in an SDN testbed. The results identified that QoS performance experienced a significant percentage increase when the MBE was active. These findings contribute a novel Meter Band Rate Evaluation mechanism that extends the native capability of OpenFlow and OpenDaylight to enhance the efficiency of QoS provision
Discussion on Event Horizon and Quantum Ergosphere of Evaporating Black Holes in a Tunnelling Framework
In this paper, with the Parikh-Wilczek tunnelling framework the positions of
the event horizon of the Vaidya black hole and the Vaidya-Bonner black hole are
calculated respectively. We find that the event horizon and the apparent
horizon of these two black holes correspond respectively to the two turning
points of the Hawking radiation tunnelling barrier. That is, the quantum
ergosphere coincides with the tunnelling barrier. Our calculation also implies
that the Hawking radiation comes from the apparent horizon.Comment: 8 page
Diversity and Adaptation in Large Population Games
We consider a version of large population games whose players compete for
resources using strategies with adaptable preferences. The system efficiency is
measured by the variance of the decisions. In the regime where the system can
be plagued by the maladaptive behavior of the players, we find that diversity
among the players improves the system efficiency, though it slows the
convergence to the steady state. Diversity causes a mild spread of resources at
the transient state, but reduces the uneven distribution of resources in the
steady state.Comment: 8 pages, 3 figure
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