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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 |
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 |
7 |
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
تشخیص رفتارهای غیر عادی کشتی از داده های AIS با استفاده از GeoTrackNet: از آزمایشگاه به اقیانوس-2020 The constant growth of maritime traffic leads to the
need of automatic anomaly detection, which has been attracting
great research attention. Information provided by AIS (Automatic
Identification System) data, together with recent outstanding
progresses of deep learning, make vessel monitoring using
neural networks (NNs) a very promising approach. This paper
analyse a novel neural network we have recently introduced
—GeoTrackNet— regarding operational contexts. Especially, we
aim to evaluate (i) the relevance of the abnormal behaviours
detected by GeoTrackNet with respect to expert interpretations,
(ii) the extent to which GeoTrackNet may process AIS data
streams in real time. We report experiments showing the high
potential to meet operational level of the model. Index Terms: AIS | deep learning | neural networks | anomaly detection | real-time | maritime big data |
مقاله انگلیسی |
8 |
On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation
در مورد تأثیر روشهای تشخیص ناهنجاری به کمک هوش مصنوعی در پیمایش دریایی-2020 The automatic identification system (AIS) has become
an essential tool for maritime security. Nevertheless, how
to effectively use the static and dynamic voyage information of
the AIS data in maritime traffic situation awareness is still a
challenge. This paper presents a comparative study of artificial
intelligence (AI) techniques on their effectiveness in dealing with
various anomalies in maritime domain using the AIS data. The
AIS on-off switching (OOS) anomaly is critical in maritime
security, since AIS technology is susceptible to manipulation and
it can be switched on and off to hide illegal activities. Thus,
we try to detect and distinguish between intentional and nonintentional
AIS OOS anomalies through our AI-assisted anomaly
detection framework. We use AIS data, in particular positional
and navigational status of vessels, to study the effectiveness of
seven AI techniques, such as artificial neural network, support
vector machine, logistic regression, k-nearest neighbors, decision
tree, random forest and naive Bayes, in detecting the AIS OOS
anomalies. Our experimental results show that ANN and SVM
are the most suitable techniques in detecting the AIS OOS
anomalies with 99.9% accuracy. Interestingly, the ANN model
outperforms others when trained with a balanced (i.e., same
order of samples per class) dataset, and SVM, on the other hand,
is suitable when training dataset is unbalanced. |
مقاله انگلیسی |
9 |
Process mining-based anomaly detection of additive manufacturing process activities using a game theory modeling approach
تشخیص ناهنجاری مبتنی بر استخراج فرآیند از فعالیت های فرآیند تولید مواد افزودنی با استفاده از رویکرد مدل سازی تئوری بازی-2020 As a new production procedure Additive Manufacturing will present a time-effective production system when adopted in distributed 3D printing mode. In this case, the distributed manufacturing leads to different challenges such as control between production sites. Based on the cloud infrastructure usage for distributed production systems, the product reliability handling is vital. Moreover, AM is used to produce safety–critical systems components and this product type defines AM as an interesting attack target. This study presents a new extension of uncertain Business Process Management System (uncertain BPMS) architecture for detecting anomaly using this extension capability. This extension has a new component as event-based anomaly detector, where intrusion detection can take place through an integration of process mining and game theory techniques. The proposed component could operate based on pre-processor, conformance checker, and anomaly detection optimizer modules. These modules can intelligently control the AM process activities between expected behavior and actual behavior using distributed event logs, a hybrid of highly accurate algorithms such as Improved Particle Swarm Optimization (IPSO), firefly, and AdaBoost algorithms inside the game theory modeling approach. In this case, the game theory technique as an optimizer provides optimal selection strategies for the proposed component to detect untrusted behaviors. The results of the new extension execution on a case study and its evaluation using Nash Equilibrium (NE) solution indicate that the proposed anomaly detector component is highly accurate in anomaly detection for AM process activities and can detect more attacks successfully through guidance of the game theory framework in the system. Keywords: Event-based anomaly detection | Additive manufacturing | Business process management system | Process mining technique | Game theory modeling | Distributed production system |
مقاله انگلیسی |
10 |
Behavioral Model Anomaly Detection in Automatic Identification Systems (AIS)
تشخیص ناهنجاری مدل رفتاری در سیستم های شناسایی خودکار (AIS)-2020 Over 90% of all goods in the world, at some point in
their life, are carried on a vessel at sea. Currently, the maritime
industry relies on the Automatic Identification System (AIS) for
collision avoidance, vessel tracking, and vessel awareness while
operating at sea. AIS is a plaintext, unencrypted, unauthenticated
protocol and, as such, is vulnerable to various types of attacks.
Malicious actors can alter the AIS location of a vessel by
spoofing a vessel or alter the channel the AIS receiver is using
to send nefarious information to a vessel privately. With the
advent of the Ocean of Things (OoT), vessels are sharing more
information than vessel location alone at sea. As this information
becomes critical for safe and efficient operation at sea, we in this
work present a novel approach of applying machine learning
to build behavior models for vessels at sea. These models allow
vessels to detect anomalous communication from vessels nearby,
thus enable vessels to determine the quality of the messages
shared between each other and, more critically, identify malicious
vessels’ behaviors. Keywords: Automatic Identification System | machine learning | behavioral model | anomaly detection | Ocean of Things |
مقاله انگلیسی |