scholarly journals Coleção de dados sobre ataques a dispositivos de Internet das Coisas

2019 ◽  
Author(s):  
Marcos Felipe Barboza de Abreu ◽  
Kleber Vieira Cardoso ◽  
Thierson Rosa
Keyword(s):  
Internet Of Things ◽  
New Technologies ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Detection Problem ◽  
Test Environment ◽  
New Techniques ◽  
Detection Systems ◽  
Iot Devices

The number of Internet of Things (IoT) devices has increased every day and along with this growth arises the security concerns. Several techniques have been studied for the prevention, detection and treatment of attacks in conventional networks, such as the work of KDD CUP 99 that proposed a labeled collection, which has been quite exploited in recent decades. A good evaluation of techniques and algorithms of intrusion detection systems is related to the existence of good datasets. However, few works exploit the detection of attacks on Internet of Things and until now no collection of data has been proposed for this problem. Along with new technologies and devices arise new techniques of invasion, and even more elaborated. Therefore, it is necessary to treat the attack detection problem in a special way. In view of this, this work is dedicated to setting up a test environment that represents an Internet of Things network, collecting normal device traffic, simulating attacks, assembling a collection of data and analyzing it. For this, we run invasion tests on emulated devices, resulting in a new collection of data. We validate the new collection by applying machine learning algorithms and comparing with the KDD collection.

scholarly journals Recent Advancements in Intrusion Detection Systems for the Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Peter Herrmann
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Research Work ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Systems ◽  
Iot Applications ◽  
Iot Devices

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

scholarly journals Toward lightweight intrusion detection systems using the optimal and efficient feature pairs of the Bot-IoT 2018 dataset

2021 ◽  
Vol 17 (10) ◽  
pp. 155014772110522
Author(s):  
Erman Özer ◽  
Murat İskefiyeli ◽  
Jahongir Azimjonov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Energy Efficient ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Feature Pair ◽  
Feature Based

Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain more than 10 features. Any machine learning–based lightweight intrusion detection systems trained with full features turn into an inefficient and heavyweight intrusion detection systems. This case challenges Internet of Things networks that suffer from power efficiency problems. Therefore, lightweight (energy-efficient), accurate, and high-performance intrusion detection systems are paramount instead of inefficient and heavyweight intrusion detection systems. To address these challenges, a new approach that can help to determine the most effective and optimal feature pairs of datasets which enable the development of lightweight intrusion detection systems was proposed. For this purpose, 10 machine learning algorithms and the recent BoT-IoT (2018) dataset were selected. Twelve best features recommended by the developers of this dataset were used in this study. Sixty-six unique feature pairs were generated from the 12 best features. Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. Similarly, 660 feature-pair-based lightweight intrusion detection systems were developed by training the 10 machine learning algorithms via each feature pair out of the 66 feature pairs. Moreover, the 10 intrusion detection systems trained with 12 best features and the 660 intrusion detection systems trained via 66 feature pairs were compared to each other based on the machine learning algorithmic groups. Then, the feature-pair-based lightweight intrusion detection systems that achieved the accuracy level of the 10 full-feature-based intrusion detection systems were selected. This way, the optimal and efficient feature pairs and the lightweight intrusion detection systems were determined. The most lightweight intrusion detection systems achieved more than 90% detection accuracy.

Security Aspects of the Internet of Things

2021 ◽  
pp. 207-233
Author(s):  
Dominik Hromada ◽  
Rogério Luís de C. Costa ◽  
Leonel Santos ◽  
Carlos Rabadão
Keyword(s):  
Internet Of Things ◽  
New Technologies ◽  
Smart Cities ◽  
The Internet ◽  
Detection Systems ◽  
Private Data ◽  
Promising Solution ◽  
Iot Devices ◽  
Smart Industry ◽  
The Internet Of Things

The Internet of Things (IoT) comprises the interconnection of a wide range of different devices, from Smart Bluetooth speakers to humidity sensors. The great variety of devices enables applications in several contexts, including Smart Cities and Smart Industry. IoT devices collect and process a large amount of data on machines and the environment and even monitor people's activities. Due to their characteristics and architecture, IoT devices and networks are potential targets for cyberattacks. Indeed, cyberattacks can lead to malfunctions of the IoT environment and access and misuse of private data. This chapter addresses security concerns in the IoT ecosystem. It identifies common threats for each of IoT layers and presents advantages, challenges, and limitations of promising countermeasures based on new technologies and strategies, like Blockchain and Machine Learning. It also contains a more in-depth discussion on Intrusion Detection Systems (IDS) for IoT, a promising solution for cybersecurity in IoT ecosystems.

scholarly journals Evaluating the performance of machine learning algorithms for network intrusion detection systems in the internet of things infrastructure

2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Yaser M. Banadaki
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

As numerous Internet-of-Things (IoT) devices are deploying on a daily basis, network intrusion detection systems (NIDS) are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms: XGBoost, random forest, decision tree, and gradient boosting, and evaluates their performance in NIDS, considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the XGBoost performs better than the other algorithms reaching the predicted accuracy of 99.6% in detecting cyberattacks. XGBoost-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The paper also studies the effect of class imbalance and the size of the normal and attack classes. The small numbers of some attacks in training datasets mislead the classifier to bias towards the majority classes resulting in a bottleneck to improving macro recall and macro F1 score. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today’s growing IoT network traffic. 

Intrusion Detection Systems for Internet of Things

2020 ◽  
pp. 148-171
Author(s):  
Gayathri K. S. ◽  
Tony Thomas
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Remote Monitoring ◽  
Human Life ◽  
Intrusion Detection Systems ◽  
Security Vulnerabilities ◽  
Detection Systems ◽  
Iot Devices ◽  
And Storage ◽  
Security Of Iot

Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. These systems improve the comfort and efficiency of human life, but the inherent vulnerabilities in these IoT devices create a backdoor for intruders to enter and attack the entire system. Hence, there is a need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. Since the security of IoT systems is critical, this chapter presents recent research in intrusion detection systems in IoT systems.

scholarly journals Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

2021 ◽  
Author(s):  
Shyla Shyla ◽  
Vishal Bhatnagar ◽  
Vikram Bali ◽  
Shivani Bali
Keyword(s):  
Intrusion Detection ◽  
Gradient Descent ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Stochastic Gradient Descent ◽  
Data Traffic ◽  
Detection Systems ◽  
Moment Estimation ◽  
Classi Fication

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

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