A smart city cyber security platform for narrowband networks

Elsaeidy, Asmaa; Elgendi, Ibrahim; Munasinghe, Kumudu S.; Sharma, Dharmendra; Jamalipour, Abbas

doi:10.1109/atnac.2017.8215388

Cited by 22 publications

(19 citation statements)

References 14 publications

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“…• Cyber attacks are considered as one of the biggest threats to IoT applications [199], and mostly the network and the application layers are the focus of the attackers [199].…”

Section: Lessons Learnt and Pitfallsmentioning

confidence: 99%

“…Whenever we talk about cryptographic security, key management is an associated challenge, and it is always considered to be an open research issue [200]. After, device, data, and network security, application layer security is also very essential as mostly the network and the application layers are the focus of the attackers [199]. Therefore, [89,154] highlight threats to IoT systems that rely on websites and application for service delivery, and also propose security measures.…”

Section: Lessons Learnt and Pitfallsmentioning

confidence: 99%

See 1 more Smart Citation

Anatomy of Threats to the Internet of Things

Makhdoom

Abolhasan

Lipman

et al. 2019

IEEE Commun. Surv. Tutorials

299

164

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The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from Industrial Control Systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, Cyber Physical Systems (CPS) and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT including ICS and CPS. We also deduce an attack strategy of a Distributed Denial of Service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and the open research challenges. Index Terms-Threats to the IoT, Internet of Things, malware attacks on the Internet of Things, attack methodology, security and privacy, IoT security framework, security guidelines.

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“…• Cyber attacks are considered as one of the biggest threats to IoT applications [199], and mostly the network and the application layers are the focus of the attackers [199].…”

Section: Lessons Learnt and Pitfallsmentioning

confidence: 99%

Section: Lessons Learnt and Pitfallsmentioning

confidence: 99%

Anatomy of Threats to the Internet of Things

Makhdoom

Abolhasan

Lipman

et al. 2019

IEEE Commun. Surv. Tutorials

299

164

View full text Add to dashboard Cite

show abstract

“…Many aspects and components of a smart city for upgrading the standard of the people are described in [4]. Authors in [5] suggest a platform design to secure a smart city facing cyber attackers. The structure is giving a warning DL model to identify attackers based on the user's data performance.…”

Section: Related Workmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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“…The results showed the ability of deep belief networks to detect the attacks effectively. In [19], a deep restricted Boltzmann machines model was combined with threshold detector for distributed Denial of service attack detection. Applying the deep component for extracting high level features from the raw dataset records contributes to the efficiency of discriminating between normal and attack traffic.…”

Section: Related Workmentioning

confidence: 99%

Replay Attack Detection in Smart Cities Using Deep Learning

et al. 2020

Self Cite

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Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research. INDEX TERMS Smart cities, intrusion detection, replay attack, deep learning, convolutional neural networks.

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A smart city cyber security platform for narrowband networks

Cited by 22 publications

References 14 publications

Anatomy of Threats to the Internet of Things

Anatomy of Threats to the Internet of Things

Attack Detection in IoT using Machine Learning

Replay Attack Detection in Smart Cities Using Deep Learning

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