777 research outputs found
IoT Anomaly Detection Methods and Applications: A Survey
Ongoing research on anomaly detection for the Internet of Things (IoT) is a
rapidly expanding field. This growth necessitates an examination of application
trends and current gaps. The vast majority of those publications are in areas
such as network and infrastructure security, sensor monitoring, smart home, and
smart city applications and are extending into even more sectors. Recent
advancements in the field have increased the necessity to study the many IoT
anomaly detection applications. This paper begins with a summary of the
detection methods and applications, accompanied by a discussion of the
categorization of IoT anomaly detection algorithms. We then discuss the current
publications to identify distinct application domains, examining papers chosen
based on our search criteria. The survey considers 64 papers among recent
publications published between January 2019 and July 2021. In recent
publications, we observed a shortage of IoT anomaly detection methodologies,
for example, when dealing with the integration of systems with various sensors,
data and concept drifts, and data augmentation where there is a shortage of
Ground Truth data. Finally, we discuss the present such challenges and offer
new perspectives where further research is required.Comment: 22 page
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, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
From visual comparison to robust satellite techniques: 30 years of thermal infrared satellite data analyses for the study of earthquake preparation phases
This review paper reports the main contributions and results achieved after more
than 30 years of studies on the possible relationships among space-time variation of
Earth’s thermally emitted radiation, measured by satellite sensors operating in the
Thermal InfraRed (TIR) spectral range (8-14 m), and earthquake occurrence. Focus
will be given on the different existing methods/models to: 1) discriminate a possible
pre-seismic TIR anomaly from all the other TIR signal fluctuations; 2) correlate such
anomalies with space, time and magnitude of earthquakes; 3) physically justify such a
correlation
Cloud Energy Micro-Moment Data Classification: A Platform Study
Energy efficiency is a crucial factor in the well-being of our planet. In
parallel, Machine Learning (ML) plays an instrumental role in automating our
lives and creating convenient workflows for enhancing behavior. So, analyzing
energy behavior can help understand weak points and lay the path towards better
interventions. Moving towards higher performance, cloud platforms can assist
researchers in conducting classification trials that need high computational
power. Under the larger umbrella of the Consumer Engagement Towards Energy
Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation
Systems (EM)3 framework, we aim to influence consumers behavioral change via
improving their power consumption consciousness. In this paper, common cloud
artificial intelligence platforms are benchmarked and compared for micro-moment
classification. The Amazon Web Services, Google Cloud Platform, Google Colab,
and Microsoft Azure Machine Learning are employed on simulated and real energy
consumption datasets. The KNN, DNN, and SVM classifiers have been employed.
Superb performance has been observed in the selected cloud platforms, showing
relatively close performance. Yet, the nature of some algorithms limits the
training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International
Symposium on Real-time Data Processing for Cloud Computin
AnoML-IoT: An End to End Re-configurable Multi-protocol Anomaly Detection Pipeline for Internet of Things
The rapid development in ubiquitous computing has enabled the use of
microcontrollers as edge devices. These devices are used to develop truly
distributed IoT-based mechanisms where machine learning (ML) models are
utilized. However, integrating ML models to edge devices requires an
understanding of various software tools such as programming languages and
domain-specific knowledge. Anomaly detection is one of the domains where a high
level of expertise is required to achieve promising results. In this work, we
present AnoML which is an end-to-end data science pipeline that allows the
integration of multiple wireless communication protocols, anomaly detection
algorithms, deployment to the edge, fog, and cloud platforms with minimal user
interaction. We facilitate the development of IoT anomaly detection mechanisms
by reducing the barriers that are formed due to the heterogeneity of an IoT
environment. The proposed pipeline supports four main phases: (i) data
ingestion, (ii) model training, (iii) model deployment, (iv) inference and
maintaining. We evaluate the pipeline with two anomaly detection datasets while
comparing the efficiency of several machine learning algorithms within
different nodes. We also provide the source code
(https://gitlab.com/IOTGarage/anoml-iot-analytics) of the developed tools which
are the main components of the pipeline.Comment: Elsevier Internet of Things, Volume 16, 100437, December 202
DDoS: DeepDefence and Machine Learning for identifying attacks
Distributed Denial of Service (DDoS) attacks are very common type of
computer attack in the world of internet today. Automatically detecting such type of
DDoS attack packets & dropping them before passing through the network is the best
prevention method. Conventional solution only monitors and provide the feedforward
solution instead of the feedback machine-based learning. A Design of Deep neural
network has been suggested in this work and developments have been made on
proactive detection of attacks. In this approach, high level features are extracted for
representation and inference of the dataset. Experiment has been conducted based on
the ISCX dataset published in year 2017,2018 and CICDDoS2019 and program has
been developed in Matlab R17b, utilizing Wireshark for features extraction from the
datasets.
Network Intrusion attacks on critical oil and gas industrial installation become
common nowadays, which in turn bring down the giant industrial sites to standstill and
suffer financial impacts. This has made the production companies to started investing
millions of dollars revenue to protect their critical infrastructure with such attacks with
the active and passive solutions available. Our thesis constitutes a contribution to such
domain, focusing mainly on security of industrial network, impersonation and attacking
with DDoS
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