scholarly journals Security Framework for IoT Devices against Cyber-attacks

2019 ◽  
Author(s):  
Aliya Tabassum ◽  
Wadha Lebda
Keyword(s):  
Cyber Attacks ◽  
Security Framework ◽  
Iot Devices

scholarly journals State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1598
Author(s):  
Sigurd Frej Joel Jørgensen Ankergård ◽  
Edlira Dushku ◽  
Nicola Dragoni
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Resource Constrained ◽  
Remote Attestation ◽  
Comprehensive Overview ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Iot Devices ◽  
Specialized Hardware

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.

Efficient Cyber Security Framework for Smart Cities

2019 ◽  
pp. 130-157 ◽  
Author(s):  
Amtul Waheed ◽  
Jana Shafi
Keyword(s):  
Cyber Security ◽  
Smart City ◽  
Smart Cities ◽  
Cost Effective ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Security Framework ◽  
Smart Transportation ◽  
Security Issues

Smart cities are established on some smart components such as smart governances, smart economy, science and technology, smart politics, smart transportation, and smart life. Each and every smart object is interconnected through the internet, challenging the security and privacy of citizen's sensitive information. A secure framework for smart cities is the only solution for better and smart living. This can be achieved through IoT infrastructure and cloud computing. The combination of IoT and Cloud also increases the storage capacity and computational power and make services pervasive, cost-effective, and accessed from anywhere and any device. This chapter will discuss security issues and challenges of smart city along with cyber security framework and architecture of smart cities for smart infrastructures and smart applications. It also presents a general study about security mechanism for smart city applications and security protection methodology using IOT service to stand against cyber-attacks.

scholarly journals Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 444 ◽  
Author(s):  
Valerio Morfino ◽  
Salvatore Rampone
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Apache Spark ◽  
Identification Accuracy ◽  
Supervised Machine Learning ◽  
Iot Devices

In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

scholarly journals Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 144 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Detection System ◽  
Detection Performance ◽  
Cyber Attacks ◽  
Raspberry Pi ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
New Feature ◽  
Iot Devices

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

scholarly journals Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Detection System ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

scholarly journals Robust Data Security Framework for IoT Network using Integrated Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 5-23
Author(s):  
Fadele Ayotunde Alaba ◽  
◽  
Abayomi Jegede ◽  
Christopher Ifeanyi Eke ◽  
◽  
...  
Keyword(s):  
Rapid Development ◽  
Cost Effective ◽  
Security Protocol ◽  
Security And Privacy ◽  
Security Framework ◽  
Resource Constrained ◽  
Iot Devices ◽  
Lightweight Authentication ◽  
Resource Constrained Devices ◽  
Constrained Devices

The Internet of Things (IoT) expects to improve human lives with the rapid development of resource-constrained devices and with the increased connectivity of physical embedded devices that make use of current Internet infrastructure to communicate. The major challenging in such an interconnected world of resource-constrained devices and sensors are security and privacy features. IoT is demand new approaches to security like a secure lightweight authentication technique, scalable approaches to continuous monitoring and threat mitigation, and new ways of detecting and blocking active threats. This paper presents the proposed security framework for IoT network. A detail understanding of the existing solutions leads to the development of security framework for IoT network. The framework was developed using cost effective design approach. Two components are used in developing the protocol. The components are Capability Design (mainly a ticket, token or key that provides authorization to access a device) and Advanced Encryption Standard (AES)-Galois Counter Mode (GCM) (a-security protocol for constrained IoT devices). AES-GCM is an encryption process that is based on authentication and well suitable IoT.

scholarly journals POSSIBILITIES FOR PROVIDING AN INFORMATION SECURITY DURING RETRIEVAL OF NETWORK DATA

2019 ◽  
pp. 689-693
Author(s):  
Veselka Stoyanova
Keyword(s):  
Data Centers ◽  
Cyber Attacks ◽  
Security Framework ◽  
Iot Security ◽  
Computing Power ◽  
Smart Buildings ◽  
Electrical Grids ◽  
Wide Range ◽  
The Internet Of Things ◽  
Require Data

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). I also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown).

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