A Systematic Approach for IoT Cyber-Attacks Detection in Smart Cities Using Machine Learning Techniques

2021 ◽  
pp. 215-228
Author(s):  
Mehdi Houichi ◽  
Faouzi Jaidi ◽  
Adel Bouhoula
Keyword(s):  
Machine Learning ◽  
Systematic Approach ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

scholarly journals Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128325-128338 ◽  
Author(s):  
Saba Ameer ◽  
Munam Ali Shah ◽  
Abid Khan ◽  
Houbing Song ◽  
Carsten Maple ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Air Quality ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience

2019 ◽  
Vol 9 (1S3) ◽  
pp. 488-494
Keyword(s):  
Machine Learning ◽  
Fault Tolerance ◽  
Critical Role ◽  
Cyber Attacks ◽  
Three Dimensions ◽  
Machine Learning Techniques ◽  
Advanced Persistent Threat ◽  
Learning Techniques

In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security

scholarly journals Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3497 ◽  
Author(s):  
César Benavente-Peces ◽  
Nisrine Ibadah
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Science ◽  
Smart Cities ◽  
Thermal Modeling ◽  
Modern Society ◽  
Machine Learning Techniques ◽  
Single Family ◽  
Residential Areas ◽  
Learning Techniques

Energy efficiency is a major concern to achieve sustainability in modern society. Smart cities sustainability depends on the availability of energy-efficient infrastructures and services. Buildings compose most of the city, and they are responsible for most of the energy consumption and emissions to the atmosphere (40%). Smart cities need smart buildings to achieve sustainability goals. Building’s thermal modeling is essential to face the energy efficiency race. In this paper, we show how ICT and data science technologies and techniques can be applied to evaluate the energy efficiency of buildings. In concrete, we apply machine learning techniques to classify buildings based on their energy efficiency. Particularly, our focus is on single-family buildings in residential areas. Along this paper, we demonstrate the capabilities of machine learning techniques to classify buildings depending on their energy efficiency. Moreover, we analyze and compare the performance of different classifiers. Furthermore, we introduce new parameters which have some impact on the buildings thermal modeling, especially those concerning the environment where the building is located. We also make an insight on ICT and remark the growing relevance in data acquisition and monitoring of relevant parameters by using wireless sensor networks. It is worthy to remark the need for an appropriate and reliable dataset to achieve the best results. Moreover, we demonstrate that reliable classification is feasible with a few featured parameters.

A Study of Machine Learning Techniques in Cryptography for Cybersecurity

2021 ◽  
Vol 1 (4) ◽  
pp. 22-26
Author(s):  
Ankita Saha ◽  
Chanda Pathak ◽  
Sourav Saha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Sensitive Data ◽  
Security Controls ◽  
Learning Techniques ◽  
Scope Of Application

The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.

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