Security Analysis and Prevention of Attacks on IoT Devices

Raghuprasad, Aswin; Padmanabhan, Suraj; Babu, Merin; Binu, P K

doi:10.1109/iccsp48568.2020.9182055

Cited by 24 publications

(7 citation statements)

References 4 publications

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“…Authenticity ensures an authentic message. Message Authentication Code (MAC) is brief information used for message authentication and provides the message's system has seen various attacks over the past few years, making manufacturers and consumers more aware of IoT products [41,42].…”

Section: Iot Attacksmentioning

confidence: 99%

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Haji

Ameen

2021

AJRCoS

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The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

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Section: Iot Attacksmentioning

confidence: 99%

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Haji

Ameen

2021

AJRCoS

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“…The processing layer contains various types of technology, such as data analysis and data storage. The most popular type of attack on the IoT processing layer is a cloud attack since the cloud receives data sent at this phase [80]. The attacker will use trojan worms, horse applications, spyware, malware, and malicious scripting software attacks that can damage IoT system devices in the application layer.…”

Section: Iot Attacksmentioning

confidence: 99%

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

et al. 2021

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In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

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“…After that, the result once again passes through the Rabbit PRNG with the number of system iterations either two for Modified Rabbit v1 or one for Modified Rabbit v2. (5). The resulting key from the second Rabbit PRNG is the final key produced by the proposed Modified Rabbit PRNG.…”

Section: New Key Generation Algorithm For Lorawanmentioning

confidence: 99%

“…LoRaWAN is one of the most popular LPWAN standards quickly adopted in IoT applications. The paper [ 5 ] indicates that 70% of the IoT devices are highly vulnerable to security attacks. Given the observed steady growth of IoT devices, IoT security becomes one of the key factors ensuring internet reliability.…”

Section: Introductionmentioning

confidence: 99%

A Complete Key Management Scheme for LoRaWAN v1.1

Chen

Lech

Wang

2021

Sensors

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Security is one of the major concerns of the Internet of Things (IoT) wireless technologies. LoRaWAN is one of the emerging Low Power Wide Area Networks being developed for IoT applications. The latest LoRaWAN release v.1.1 has provided a security framework that includes data confidentiality protection, data integrity check, device authentication and key management. However, its key management part is only ambiguously defined. In this paper, a complete key management scheme is proposed for LoRaWAN. The scheme addresses key updating, key generation, key backup, and key backward compatibility. The proposed scheme was shown not only to enhance the current LoRaWAN standard, but also to meet the primary design consideration of LoRaWAN, i.e., low power consumption.

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Security Analysis and Prevention of Attacks on IoT Devices

Cited by 24 publications

References 4 publications

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

A Complete Key Management Scheme for LoRaWAN v1.1

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