IoT Security Evolution: Challenges and Countermeasures Review

Internet of Things (IoT) architecture, technologies, applications and security have been recently addressed by a number of researchers. Basically, IoT adds internet connectivity to a system of intelligent devices, machines, objects and/or people. Devices are allowed to automatically collect and transmit data over the Internet, which exposes them to serious attacks and threats. This paper provides an intensive review of IoT evolution with primary focusing on security issues together with the proposed countermeasures. Thus, it outlines the IoT security challenges as a future roadmap of research for new researchers in this domain

Internet of Things security with machine learning techniques:a systematic literature review

Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories. The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network

Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems

IoT-HASS: A Framework For Protecting Smart Home Environment

While many solutions have been proposed for smart home security, the problem that no single solution fully protects the smart home environment still exists. In this research we propose a security framework to protect the smart home environment. The proposed framework includes three engines that complement each other to protect the smart home IoT devices. The first engine is an IDS/IPS module that monitors all traffic in the home network and then detects, alerts users, and/or blocks packets using anomaly-based detection. The second engine works as a device management module that scans and verifies IoT devices in the home network, allowing the user to flag any suspect device. The third engine works as a privacy monitoring module that monitors and detects information transmitted in plaintext and alerts the user if such information is detected. We call the proposed system IoT-Home Advanced Security System or IoT-HASS for short. IoT-HASS was developed using Python 3 and can be implemented in two modes of operation. The in-line mode allows the IoT-HASS to be installed in-line with the traffic inside a Raspberry Pi or a Router. In the in-line mode IoT-HASS acts as an IPS that can detect and block threats as well as alert the user. The second mode is the passive mode where IoT-HASS in not installed in-line with the traffic and can act as an IDS that passively monitors the traffic, detecting threats and alerting the user, but not blocking the attack. IoT-HASS was evaluated via four testing scenarios. It demonstrated superior performance in all testing scenarios in detecting attacks such as DDoS attacks, Brute Force Attacks, and Cross Site Scripting (XSS) Attacks. In each of the four test scenarios, we also tested the device management functionality, which we found to successfully scan and display IoT devices for the homeowner. The extensive evaluating and testing of IoT-HASS showed that IoT-HASS can successfully run in a small device such as a Raspberry Pi, and thus, it will most likely run in an embedded device as an IoT device. Our future research will concentrate on strengthening the current features of IoT-HASS to include additional functionalities

Abordagem baseada em Árvores de Decisão para detecção e identificação de intrusões em ambientes da Internet das Coisas baseados em Computação em Nevoeiro

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.A Internet of Things é um paradigma que está em grande ascensão nos últimos anos. O grande numero de dispositivos e as suas limitações de recursos, desafiam pesquisadores e desenvolvedores na área de segurança da informação. Ataques a dispositivos conectados se tornaram comuns ultimamente. Desse modo, contramedidas devem ser tomadas para oferecer uma camada de segurança na comunicação. Os Intrusion Detection Systems são sistemas que buscam detectar entidades maliciosas que tentam controlar e/ou indisponibilizar uma rede de dispositivos. Muitos dos trabalhos atuais nessa área de pesquisa focam em métodos de anomalias para detecção binária, que simplesmente detecta se um tráfego específico é ataque ou não, mas não é capaz de identificar o tipo de ataque. Adicionalmente, os datasets de treinamento e teste comumente utilizados na área da pesquisa, são criados de forma sintética, tendo um conjunto de features próprios. Essa diferença de features entre os datasets limita a avaliação dos estudos realizados, pois conhecidamente a qualidade das features são importantes para o desempenho do modelo. Recentemente foi disponibilizado um dataset que possui features baseadas no protocolo NetFlow v9, amplamente utilizado por provedores de serviço. Esse dataset proporciona um conjunto de features padrão, ou seja, representando fluxos de pacotes de uma rede Internet of Things "real". Portanto, neste trabalho é proposto métodos de detecção multiclasse, utilizando algoritmos de aprendizado supervisionado, comumente utilizados por pesquisas do estado da arte em detecção de intrusão. A maior contribuição deste trabalho é a utilização de um dataset recente, gerado a partir de uma fusão de outros datasets bastante utilizados na área de pesquisa em detecção de intrusão para ambientes Internet of Things. Através dos experimentos verificou-se que todas as abordagens avaliadas apresentaram taxas de detecção satisfatórias, apresentando baixas taxas de falsos negativos e falsos positivos.The Internet of Things is a paradigm that has been on the rise in recent years. The large number of devices and their resource limitations challenge researchers and developers in the area of information security. Attacks on connected devices have become common lately. Thus, countermeasures must be taken to provide a layer of security in communication. Intrusion Detection Systems are systems that seek to detect malicious entities that try to control and/or make a network of devices unavailable. Much of the current work in this area of research focuses on anomaly methods for binary detection, which simply detect whether a specific traffic is an attack or not, but is not able to identify the type of attack. Additionally, the training and test datasets commonly used in the research area are synthetically created, having a set of their own features. This difference in features between the datasets limits the evaluation of the studies carried out, since the quality of the features is known to be important for the performance of the model. Recently, a dataset was made available that has features based on the NetFlow v9 protocol, widely used by service providers. This dataset provides a set of standard features, that is, representing packet flows from a "real" Internet of Things network. Therefore, in this work, multiclass detection methods are proposed, using supervised learning algorithms, commonly used by state-of-the-art research in intrusion detection. The main contribution of this work is the use of a recent dataset, generated from a fusion of other datasets widely used in the area of intrusion detection research for Internet of Things environments. Through the experiments it was found that all approaches evaluated presented satisfactory detection rates, with low rates of false negatives and false positives

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969