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1 |
IoT anomaly detection methods and applications: A survey
روش ها و کاربردهای تشخیص ناهنجاری اینترنت اشیا: یک مرور-2022 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.
keywords: Anomaly detection | Internet of Things | IoT | Review | Survey | Applications |
مقاله انگلیسی |
2 |
A novel machine learning pipeline to detect malicious anomalies for the Internet of Things
پایپ لاین یادگیری ماشینی جدید برای شناسایی ناهنجاری های مخرب برای اینترنت اشیا-2022 Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The
anomalies are considered as samples that do not follow a normal pattern and significantly differ
from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For
example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data
generated from IoT devices. There has been wide-scale research done on anomaly detection
problems in general, i.e., finding the samples in data that differ significantly from the expected
values. However, there has been limited work done to figure out the inherent cause of the
anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed,
the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data
manipulation by an attacker has not been explored in detail.
In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement. keywords: IoT | Anomaly detection | Ensemble learning | Predictive analytics |
مقاله انگلیسی |
3 |
Beacon Non-Transmission attack and its detection in intelligent transportation systems
حمله عدم انتقال Beacon و تشخیص آن در سیستم های حمل و نقل هوشمند-2022 Message dropping by intermediate nodes as well as RF jamming attack have been studied
widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack
in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack
in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack,
a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the
automated driving misbehavior detection protocols running in ITS, or to mount a Denial-ofService (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT
attack as a critical security threat to ITS, we propose two novel and lightweight techniques to
detect it. Our first technique bases its detection by assuming a certain distribution of the number
of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to
classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable.
Our second technique, suitable for identifying both shortish and longish BNT attacks, considers
beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF)
to determine the existence of an attack. In order to trade-off detection accuracy for equitable
use of limited computational resources, we propose a random inspection model in which the
detection algorithm is executed at random time instances and for randomly selected set of
vehicles. We have performed extensive simulations to evaluate the performance of proposed
detection algorithms under random inspection and a practical attacker model. The results
obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based
technique over simple threshold based technique in terms of higher detection accuracy as well
as smaller reaction delay.
keywords: DoS attack | Intrusion detection | Driving anomaly detection | Beacon | Autocorrelation function | Intelligent Transportation Systems |
مقاله انگلیسی |
4 |
An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety
یک تشخیص ناهنجاری مبتنی بر یادگیری عمیق در معابر پیاده برای ایمنی کاربران جاده ای آسیب پذیر-2021 Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. Therefore, this paper develops an automated deep learning based anomaly detectiontechnique in pedestrian walkways (DLADT-PW) for vulnerable road user’s safety. The goal of the DLADT-PWmodel is to detect and classify the various anomalies that exist in the pedestrian walkways such as cars, skating, jeep, etc. The DLADT-PW model involves preprocessing as the primary step, which is applied for removing the noise and raise the quality of the image. In addition, mask region convolutional neural network (Mask-RCNN) with densely connected networks (DenseNet) model is employed for the detection process. To ensure the better anomaly detection performance of the DLADT-PW technique, an extensive set of simulations were performed and the outcomes are investigated under distinct aspects. The obtained experimental values confirmed the superior characteristics of the DLADT-PW technique by achieving a maximum detection accuracy. Keywords: Anomaly detection | Pedestrian walkways | Deep learning | Safety | Mask RCNN |
مقاله انگلیسی |
5 |
Deep learning-based real-world object detection and improved anomaly detection for surveillance videos
تشخیص واقعی شیء مبتنی بر یادگیری عمیق و بهبود تشخیص ناهنجاری برای فیلم های نظارتی-2021 In this fast processing world, we need fast processing programs with maximum accuracy. This can be achieved when computer vision is connected with optimized deep learning models and neural networks. The goal of this project is to build an Artificial Intelligent system that will take live CCTV camera feed as input and detect what is happening in the video and do further analysis. Concerning current technology, there are a lot many models which use computer vision, machine learning for image and video processing. All models are different from each other, use various libraries, and are difficult to integrate or need high-end systems to process. This paper aims to use a convolutional neural network model for video processing and solve most of the important video processing features like detection of the liveliness of objects, estimating counts, and anomaly detection. And also further deploy it in such a way that it’ll be easy to integrate and easy to use with API calls.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Computer vision | Convolutional neural network | Deep learning | Estimating counts | Liveliness |
مقاله انگلیسی |
6 |
A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning
یک پایگاه داده انگور برای تشخیص زودهنگام و طبقه بندی بیماری esca در تاکستان ها از طریق یادگیری ماشین-2021 Esca is one of the most common disease that can severely
damage grapevine. This disease, if not properly treated in
time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as
well as a rising risk of propagation to the closer grapevines.
Nowadays, the detection of Esca is carried out manually
through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing,
computer vision and machine learning methods have been
widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants
that helps in preventing it to spread in the vineyards and in
minimizing the financial loss to the wine producers. In this
article, an image dataset of grapevine leaves is presented.
The dataset holds grapevine leaves images belonging to two
classes: unhealthy leaves acquired from plants affected by
Esca disease and healthy leaves. The data presented has been
collected to be used in a research project jointly developed
by the Department of Information Engineering, Polytechnic
University of Marche, Ancona, Italy and the STMicroelectronics, Italy, under the cooperation of the Umani Ronchi SPA
winery, Osimo, Ancona, Marche, Italy. The dataset could be
helpful to researchers who use machine learning and computer vision algorithms to develop applications that help
agronomists in early detection of grapevine plant diseases.
Keywords: Plant diseases recognition | Esca disease | Machine learning | Image dataset | Image classification |
مقاله انگلیسی |
7 |
Edge Concierge: Democratizing Cost-Effective and Flexible Network Operations using Network Layer AI at Private Network Edges
Edge Concierge: دموکراتیک کردن عملیات شبکه با هزینه و مقرون به صرفه و انعطاف پذیر با استفاده ازهوش مصنوعی لایه لایه در لبه های شبکه خصوصی-2020 We observe two major revolutionary trends in network
operations: democratization of cost-effective and flexible
communication means for vertical players, such as public safety,
by private mobile networking combined with edge computing,
and automatic and autonomic network operations empowered
by Artificial Intelligence (AI). Further innovations are required
for making private networking readily available for vertical
players that are reluctant to acquire expertise in complex network
operations. We propose Edge Concierge, of which concept is to
democratize cost-effective and flexible network operations using
network layer AI at private network edges. Edge Concierge assists
smart network operations for private mobile network operators
and energy saving by changing working state of AI-empowered
anomaly detection applications by network layer AI. We also
employ unsupervised machine learning using Hidden Markov
Model (HMM) for estimating contexts by solely observing network
traffic at mobile edge computing (MEC) middle boxes. In
detail, we design a system of real-time and self-learning context
estimation by a multi-level probabilistic state transition model
trained by unsupervised learning, which is implemented in a
commodity PC. In order to evaluate our proposed system, we
take public safety context of smart cities as an example use case
and show the benefits. |
مقاله انگلیسی |
8 |
AI City Challenge 2020 – Computer Vision for Smart Transportation Applications
چالش شهر هوش مصنوعی 2020 : چشم انداز رایانه ای برای برنامه های حمل و نقل هوشمند-2020 We present methods developed in our participation of
the AI City 2020 Challenge (AIC20) and report evaluation
results in this contest. With the blooming of AI computer
vision techniques, vehicle detection, tracking, identification,
and counting all have advanced significantly. However,
whether these technologies are ready for real-world
smart transportation usage is still a open question. The
goal of this work is to apply and integrate state-of-the-art
techniques for solving the challenge problems under a standardized
setup and evaluation. We participated all 4 AIC20
challenge tracks (T1 to T4). In T1 challenge, we perform vehicle
counting by associating deep features extracted from
Mask-RCNN detections and tracklets, followed by vehicle
movement zone matching. In T2 challenge, we perform vehicle
type and color classification and then rank matching
vehicles using a PGAM re-id network. In T3 challenge, we
proposed a new Multi-Camera Tracking Network (MTCN)
that takes single-camera vehicle tracking as input, and performs
multi-camera tracklet fusion and linking, by jointly
optimizing the matching of vehicle appearance and physical
features. In T4 challenge, we adopt a leading method
based on perspective detection and spatial-temporal matrix
discriminating, and improve it with background modeling
for traffic anomaly detection. We achieved top-6 and top-4
performance for T3 and T4 challenges respectively in the
AIC20 general leaderboard. |
مقاله انگلیسی |
9 |
AI and Reliability Trends in Safety-Critical Autonomous Systems on Ground and Air
روند هوش مصنوعی و قابلیت اطمینان در سیستمهای خودمختار ایمنی در زمین و هوا-2020 Safety-critical autonomous systems are
becoming more powerful and more integrated to enable
higher-level functionality. Modern multi-core SOCs are
often the computing backbone in such systems for which
safety and associated certification tasks are one of the key
challenges, which can become more costly and difficult to
achieve. Hence, modeling and assessment of these systems
can be a formidable task. In addition, Artificial Intelligence
(AI) is already being deployed in safety critical autonomous
systems and Machine Learning (ML) enables the
achievement of tasks in a cost-effective way.
Compliance to Soft Error Rate (SER) requirements is an
important element to be successful in these markets. When
considering SER performance for functional safety, we need
to focus on accurately modeling vulnerability factors for
transient analysis based on AI and Deep Learning
workloads. We also need to consider the reliability
implications due to long mission times leading to high
utilization factors for autonomous transport. The reliability
risks due to these new use cases also need to be
comprehended for modeling and mitigation and would
directly impact the safety analysis for these systems. Finally,
the need for telemetry for reliability, including capabilities
for anomaly detection and prognostics techniques to
minimize field failures is of paramount importance. Index Terms : SER | safety | AI | ML. reliability |
مقاله انگلیسی |
10 |
Policy-based reinforcement learning for time series anomaly detection
یادگیری تقویتی مبتنی بر سیاست برای تشخیص ناهنجاری سری زمانی-2020 Time series anomaly detection has become a crucial and challenging task driven by the rapid increase
of streaming data with the arrival of the Internet of Things. Existing methods are either domain-specific
or require strong assumptions that cannot be met in realistic datasets. Reinforcement learning (RL), as an
incremental self-learning approach, could avoid the two issues well. However, the current investigation is far
from comprehensive. In this paper, we propose a generic policy-based RL framework to address the time series
anomaly detection problem. The policy-based time series anomaly detector (PTAD) is progressively learned
from the interactions with time-series data in the absence of constraints. Experimental results show that it
outperforms the value-based temporal anomaly detector and other state-of-the-art detection methods whether
training and test datasets come from the same source or not. Furthermore, the tradeoff between precision and
recall is well respected by the PTAD, which is beneficial to fulfill various industrial requirements. Keywords: Time series anomaly detection | Reinforcement learning | Policy-based methods |
مقاله انگلیسی |