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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 |
2 |
A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks
مدل تشخیص نفوذ مه-ابر دو لایه برای شبکه های اینترنت اشیا-2022 The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However,
the open deployment environment and the limited resources of IoT devices make them vulnerable
to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks
that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks
while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes
the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog
layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus
enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed
extensive experiments using two publicly available datasets to test the proposed approach. Test
results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also
justified the proposed model in terms of improved service time, lower delay, and optimal energy
utilization.
keywords: Fog computing | Intrusion detection | IoT network | Machine learning | Security |
مقاله انگلیسی |
3 |
Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020 Crash Detection is essential in providing timely information to traffic management centers and the public to
reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding
highway traffic. For many years, researchers have explored several techniques for early and precise detection of
crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant
real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this
data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of
using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor
Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this
study. This real-world traffic data is used to design feature set for the deep learning models for crash detection
and crash risk prediction. The results show that a deep model has better crash detection performance and similar
crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was
conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It
was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash. Keywords: Crash detection | Crash prediction | Deep learning |
مقاله انگلیسی |
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Multi-Objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control
رویکرد یادگیری تقویت چند هدفه برای بهبود ایمنی در تقاطع ها با کنترل سیگنال ترافیک تطبیقی-2020 Adaptive traffic signal control (ATSC) systems improve traffic efficiency, but their impacts on traffic safety vary
among different implementations. To improve the traffic safety pro-actively, this study proposes a safety-oriented
ATSC algorithm to optimize traffic efficiency and safety simultaneously. A multi-objective deep reinforcement
learning framework is utilized as the backend algorithm. The proposed algorithm was trained and
evaluated on a simulated isolated intersection built based on real-world traffic data. A real-time crash prediction
model was calibrated to provide the safety measure. The performance of the algorithm was evaluated by the realworld
signal timing provided by the local jurisdiction. The results showed that the algorithm improves both
traffic efficiency and safety compared with the benchmark. A control policy analysis of the proposed ATSC
revealed that the abstracted control rules could help the traditional signal controllers to improve traffic safety,
which might be beneficial if the infrastructure is not ready to adopt ATSCs. A hybrid controller is also proposed
to provide further traffic safety improvement if necessary. To the best of the authors’ knowledge, the proposed
algorithm is the first successful attempt in developing adaptive traffic signal system optimizing traffic safety. Keywords: Traffic safety | Adaptive Signal control | Multi-objective reinforcement learning | Deep learning |
مقاله انگلیسی |
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The two judgments of the European Court of Justice in the four cases of Privacy International, La Quadrature du Net and Others, French Data Network and Others and Ordre des Barreaux francophones et germanophone and Others: The Grand Chamber is trying hard to square the circle of data retention
دو رای دیوان دادگستری اروپا در چهار پرونده Privacy International، La Quadrature du Net and Others، French Data Network and Others و Ordre des Barreaux francophones et germanophone و دیگران: اتاق بزرگ به شدت تلاش می کند دایره ای را مربع کند: نگهداری داده ها-2020 On 6 October 2020, the Grand Chamber of the European Court of Justice rendered two land- mark judgments in Privacy International, La Quadrature du Net and Others, French Data Network and Others as well as Ordre des barreaux francophones et germanophone and Others. The Grand Chamber confirmed that EU law precludes national legislation which requires a provider of electronic communications services to carry out the general and indiscriminate trans- mission or retention of traffic data and location data for the purpose of combating crime in general or of safeguarding national security.In situations where a Member State is facing a serious threat to national security which proves to be genuine and present or foreseeable, such State may however derogate from the obligation to ensure the confidentiality of data relating to electronic communications by requiring, by way of legislative measures, the general and indiscriminate retention of this data for a period which is limited in time to what is strictly necessary but which may be extended if the threat persists.1 In respect of combating serious crime and preventing serious threats to public security, a Member State may also provide for the targeted retention of this data and its expedited retention. Such an interference with fundamental rights must be accompanied by effective safeguards and be reviewed by a court or by an independent administrative authority. It is likewise open to a Member State to carry out a general and✩ © 2021 Published by Elsevier Ltd. All rights reserved.E-mail address: xtracol@eurojust.europa.eu1 Joined Cases 511/18, C-512/18 and 520/18 La Quadrature du Net and Others [2020] paras 168 and 177.https://doi.org/10.1016/j.clsr.2021.105540 0267-3649indiscriminate retention of IP addresses assigned to the source of a communication where the retention period is limited to what is strictly necessary or even to carry out a general and indiscriminate retention of data relating to the civil identity of users of means of electronic communication. In the latter case, the retention is not subject to a specific time limit. Keywords: European Court of Justice | Privacy International | La Quadrature du Net | Metadata | Retention of personal data | Access to personal data | National security | Article 4(2) of the treaty on EU | Articles 1(3), 3, 5, 15(1) of the | e-privacy directive | Articles 6, 7, 8, 11 and 52(1) of the | Charter of Fundamental Rights | UK | Brexit | Adequacy decision |
مقاله انگلیسی |
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A reinforcement learning model for personalized driving policies identification
یک مدل یادگیری تقویتی برای شناسایی شخصیت های سیاسی محور -2020 Optimizing driving performance by addressing personalized aspects of driving behavior
and without posing unrealistic restrictions on personal mobility may have far reaching
implications to traffic safety, flow operations and the environment, as well as significant
benefits for users. The present work addresses the problem of delivering personalized driving
policies based on Reinforcement Learning for enhancing existing Intelligent
Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed
framework is implemented on appropriate driving behavior metrics derived from
smartphone sensors’ data streams. Aggressiveness, speeding and mobile usage are considered
to describe the driving profile per trip and are presented as inputs to the Q-learning
algorithm. The implementation of the proposed methodological approach produces personalized
quantified driving policies to be exploited for self-improvement. Finally, this
paper establishes validation measures of the quality and effectiveness of the produced policies
and methodological tools for comparing and classifying the examined drivers. Keywords: Reinforcement learning | Q-learning | Machine learning | Intelligent transportation systems | Traffic data |
مقاله انگلیسی |
7 |
Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques
نقشه برداری از الگوهای مکانی-زمانی و تشخیص عوامل ازدحام ترافیک با تکنیک های تلفیق داده ها و استخراج داده های چند منبع-2019 The study focuses on mapping spatiotemporal patterns and detecting the potential drivers of traffic congestion
with multi-source data. First, based on real-time traffic data retrieved from an online map, the k-means clustering
algorithm was applied to classify the spatiotemporal distribution of congested roads. Then, we applied a
geographical detector (Geo-detector) to mine the potential factors for each spatiotemporal pattern. The results
showed six congestion patterns for intra-regional roads and inter-regional roads on weekdays. On both intraregional
and inter-regional roads, congestion density reflected by building height was the strongest indicator
during the morning peak period. Public facilities such as hospitals, tourist sites and green spaces located near
areas of employment or residential areas contributed to congestion during and off-peak hours. On intra-regional
roads, the sparse road network and greater distance from the city center contribute to congestion during peak
hours. On inter-regional roads, the number of bus stops contributed most to the early evening peak congestion,
while the design of the entrances to large buildings in mixed business areas and public service areas increased
the level of congestion. The results suggest that land use should be more mixed in high-density areas as this
would reduce the number of trips made to the city center. However, mixed land-use planning should also be
combined with a detailed design of the microenvironment to improve accessibility for different travel modes in
order to increase the efficiency of traffic and reduce congestion. The innovative approach can be potentially
applied in traffic congestion and land use planning studies elsewhere based on real-time multi-source data. Keywords: Traffic congestion | Land use | Spatiotemporal pattern | Multi-source data |
مقاله انگلیسی |
8 |
Vehicle speed measurement model for video-based systems
مدل اندازه گیری سرعت خودرو برای سیستم های مبتنی بر ویدئو-2019 Advanced analysis of road traffic data is an essential component of today’s intelligent transportation systems. This paper presents a video-based vehicle speed measurement sys- tem based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model intro- duced to generate the pdf (probability density function) of a vehicle’s speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circum- stances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios. Keywords: Speed measurement system | Motion analysis | Machine vision | Pattern recognition | Intelligent transportation systems |
مقاله انگلیسی |
9 |
IoV distributed architecture for real-time traffic data analytics
معماری توزیع شده IoV را برای تحلیل داده های ترافیک در زمان واقعی-2018 In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics
of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three
main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer
in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as
visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using
events retrieved from a Vanets-type micro simulation. Finally, we present and discuss the first obtained results as well as the
advantages and limitations of the proposed architecture.
Keywords: IoV, Big Data analytics, Fog computing, Real-time data analytics, Traffic control |
مقاله انگلیسی |
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سیستم تشخیص نفوذ توزیع شده برای محیط های ابری بر اساس تکنیک های داده کاوی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 16 تقریبا دو دهه بعد از ظهور انها؛ محاسبات ابری همچنان در میان سازمان ها و کاربران فردی در حال افزایش است. بسیاری از مسائل امنیتی همراه انتقال برای این الگوی محاسباتی شامل تشخیص نفوذ به وجود می اید. ابزارهای حمله و نفوذ با شکستن سیستم های تشخیص نفوذ سنتی (IDS) با مقدار زیادی از اطلاعات ترافیک شبکه و رفتارهای پویا پیچیده تر شده است. IDSs ابری موجود از کمبود دقت تشخیص؛ نرخ مثبت کاذب بالا و زمان اجرای بالا رنج می برد. در این مقاله ما یک یادگیری توزیع ماشینی بر مبنی سیستم تشخیص نفوذ برای محیط های ابری را ارائه می دهیم. سیستم پیشنهاد شده برای مندرجات در سمت ابری به وسیله اندازه همراه اجزای شبکه لبه از ابرهای ارائه شده است. اینها به ترافیک رهگیری شبکه های ورودی به لبه شبکه routers از از لایه فیزیکی اجازه می دهد. یک الگوریتم پنجره کشویی (sliding window) مبتنی بر زمان برای پیش پردازش شبکه گرفتار ترافیک در هر router ابری استفاده می شود و سپس در نمونه تشخیص ناهنجاری دسته بندی Naive Bayes استفاده می شود. یک مجموعه از گره های سرور کالا بر مبنی یک Hadoop و MapReduce برای هر نمونه تشخیص ناهنجاری از زمانی که تراکم شبکه افزایش می یابد؛ در دسترس است. برای هر پنجره زمانی؛ داده ترافیک ناهنجاری شبکه در هر طرف router برای یک سرور ذخیره سازی مرکزی هماهنگ شده است. بعد؛ یک طبقه بندی یادگیری گروهی بر مبنی یک Forest تصادفی برای اجرای یک مرحله دسته بندی چند کلاسه نهایی به منظور تشخیص انواعی از هر حمله استفاده می شود.
لغات کلیدی: سیستم های تشخیص نفوذ | محاسبات ابری | یادگیری ماشین | هادوپ | MapReduce |
مقاله ترجمه شده |