[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …
installed in residential buildings. If leveraged properly, that data could assist end-users …
DDoS attack detection and mitigation using SDN: methods, practices, and solutions
Distributed denial-of-service (DDoS) attacks have become a weapon of choice for hackers,
cyber extortionists, and cyber terrorists. These attacks can swiftly incapacitate a victim …
cyber extortionists, and cyber terrorists. These attacks can swiftly incapacitate a victim …
Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks
Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT
networks play an important role in the industry as well as people's activities. On the one …
networks play an important role in the industry as well as people's activities. On the one …
Jaqen: A {High-Performance}{Switch-Native} approach for detecting and mitigating volumetric {DDoS} attacks with programmable switches
The emergence of programmable switches offers a new opportunity to revisit ISP-scale
defenses for volumetric DDoS attacks. In theory, these can offer better cost vs. performance …
defenses for volumetric DDoS attacks. In theory, these can offer better cost vs. performance …
Nitrosketch: Robust and general sketch-based monitoring in software switches
Software switches are emerging as a vital measurement vantage point in many networked
systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements …
systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements …
Event labeling combining ensemble detectors and background knowledge
H Fanaee-T, J Gama - Progress in Artificial Intelligence, 2014 - Springer
Event labeling is the process of marking events in unlabeled data. Traditionally, this is done
by involving one or more human experts through an expensive and time-consuming task. In …
by involving one or more human experts through an expensive and time-consuming task. In …
An entropy-based network anomaly detection method
Data mining is an interdisciplinary subfield of computer science involving methods at the
intersection of artificial intelligence, machine learning and statistics. One of the data mining …
intersection of artificial intelligence, machine learning and statistics. One of the data mining …
Gee: A gradient-based explainable variational autoencoder for network anomaly detection
QP Nguyen, KW Lim, DM Divakaran… - … IEEE Conference on …, 2019 - ieeexplore.ieee.org
This paper looks into the problem of detecting network anomalies by analyzing NetFlow
records. While many previous works have used statistical models and machine learning …
records. While many previous works have used statistical models and machine learning …
Performance anomaly detection and bottleneck identification
O Ibidunmoye, F Hernández-Rodriguez… - ACM Computing Surveys …, 2015 - dl.acm.org
In order to meet stringent performance requirements, system administrators must effectively
detect undesirable performance behaviours, identify potential root causes, and take …
detect undesirable performance behaviours, identify potential root causes, and take …
Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking
Evaluating anomaly detectors is a crucial task in traffic monitoring made particularly difficult
due to the lack of ground truth. The goal of the present article is to assist researchers in the …
due to the lack of ground truth. The goal of the present article is to assist researchers in the …