scholarly journals Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic

2021 ◽  
Vol 7 (2) ◽  
pp. 95
Author(s):  
Mochamad Soebagja Budiana ◽  
Ridha Muldina Negara ◽  
Arif Indra Irawan ◽  
Harashta Tatimma Larasati
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Internet Of Things ◽  
Detection System ◽  
Denial Of Service ◽  
The Internet ◽  
Traffic Intensity ◽  
Dos Attacks ◽  
Fuzzy Input ◽  
The Internet Of Things

Message Queuing Telemetry Transport (MQTT) is one of the popular protocols used on the Internet of Things (IoT) networks because of its lightweight nature. With the increasing number of devices connected to the internet, the number of cybercrimes on IoT networks will increase. One of the most popular attacks is the Denial of Service (DoS) attack. Standard security on MQTT uses SSL/TLS, but SSL/TLS is computationally wasteful for low-powered devices. The use of fuzzy logic algorithms with the Intrusion Detection System (IDS) scheme is suitable for detecting DoS because of its simple nature. This paper uses a fuzzy logic algorithm embedded in a node to detect DoS in the MQTT protocol with feature selection nodes. This paper's contribution is that the nodes feature selection used will monitor SUBSCRIBE and SUBACK traffic and provide this information to fuzzy input nodes to detect DoS attacks. Fuzzy performance evaluation is measured against changes in the number of nodes and attack intervals. The results obtained are that the more the number of nodes and the higher the traffic intensity, the fuzzy performance will decrease, and vice versa. However, the number of nodes and traffic intensity will affect fuzzy performance.

A Novel Intrusion Detection System for Internet of Things Network Security

2021 ◽  
Vol 14 (3) ◽  
pp. 20-37
Author(s):  
Arun Kumar Bediya ◽  
Rajendra Kumar
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Assisted Living ◽  
Intrusion Detection System ◽  
Detection System ◽  
Smart Cities ◽  
Denial Of Service ◽  
The Internet ◽  
Smart Vehicles ◽  
The Internet Of Things

Internet of things (IoT) comprises a developing ecosystem of responsive and interconnected devices, sensors, networks, and software. The internet of things keeps on extending with the number of its different equipment segments for smart cities, healthcare, smart homes, assisted living, smart vehicles, transportation, framework, and many more are the areas where the internet of things benefits human lives. IoT networks are meant to be monitored on real-time events, and if these devices get attacked, it can have an unfavorable effect on the system. This paper discussed many possible attacks at IoT networks and distributed denial of service (DDoS) attack is one of the most dangerous among them. Blockchain technology can be utilized to develop a framework to protect IoT systems; blockchain is a new technology used for cryptocurrency transactions. This paper proposed BIoTIDS an intrusion detection system for the IoT network using blockchain. BIoTIDS is able to detect an intruder in the IoT network and also able to identify DDoS attacks in IoT networks.

scholarly journals Battery Drain Denial-of-Service Attacks and Defenses in the Internet of Things

2019 ◽  
Vol 2 ◽  
pp. 37-45 ◽  
Author(s):  
Philokypros P. Ioulianou ◽  
Vassilios G. Vassilakis ◽  
Michael D. Logothetis
Keyword(s):  
Internet Of Things ◽  
Routing Protocol ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Lossy Networks ◽  
Distributed Intrusion Detection ◽  
Iot Devices ◽  
The Impact ◽  
The Internet Of Things

IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a popular routing protocol used in wireless sensor networks and in the Internet of Things (IoT). RPL was standardized by the IETF in 2012 and has been designed for devices with limited resources and capabilities. Open-source RPL implementations are supported by popular IoT operating systems (OS), such as ContikiOS and TinyOS. In this work, we investigate the possibility of battery drain Denial-of-Service (DoS) attacks in the RPL implementation of ContikiOS. In particular, we use the popular Cooja simulator and implement two types of DoS attacks, particularly version number modification and “Hello” flooding. We demonstrate the impact of these attacks on the power consumption of IoT devices. Finally, we discuss potential defenses relying on distributed intrusion detection modules.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals SlowITe, a Novel Denial of Service Attack Affecting MQTT

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2932
Author(s):  
Ivan Vaccari ◽  
Maurizio Aiello ◽  
Enrico Cambiaso
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
The Internet ◽  
Message Queue ◽  
Plain Text ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Low Rate

Security of the Internet of Things is a crucial topic, due to the criticality of the networks and the sensitivity of exchanged data. In this paper, we target the Message Queue Telemetry Transport (MQTT) protocol used in IoT environments for communication between IoT devices. We exploit a specific weakness of MQTT which was identified during our research, allowing the client to configure the behavior of the server. In order to validate the possibility to exploit such vulnerability, we propose SlowITe, a novel low-rate denial of service attack aimed to target MQTT through low-rate techniques. We validate SlowITe against real MQTT services, considering both plain text and encrypted communications and comparing the effects of the threat when targeting different daemons. Results show that the attack is successful and it is able to exploit the identified vulnerability to lead a DoS on the victim with limited attack resources.

scholarly journals DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation

2018 ◽  
Vol 2018 ◽  
pp. 1-30 ◽  
Author(s):  
Michele De Donno ◽  
Nicola Dragoni ◽  
Alberto Giaretta ◽  
Angelo Spognardi
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Detailed Analysis ◽  
Real World ◽  
General Framework ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
The Internet Of Things

The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far.

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