Intrusion detection systems for IoT-based smart environments: a survey

Elrawy, Mohamed Faisal; Awad, Ali Ismail; Hamed, Hesham F. A.

doi:10.1186/s13677-018-0123-6

Cited by 300 publications

(155 citation statements)

References 97 publications

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“…A Gaussian dissimilarity measure Aljawarneh and Vangipuram (2018) is proposed to perform similarity computation for anomaly detection in IoT environment. Elrawy, Awad, and Hamed (2018) Fahim and Sillitti (2019) presents a comprehensive survey of various attacks in IoT‐based smart environments, recent IoT paradigm, various IDS architectures for IoT systems and several recent security challenges in IoT‐based smart environments. Nguyen et al (2019) proposed a new collaborative and intelligent NIDS architecture for anomaly detection in SDN‐based cloud IoT networks which obtained better detection accuracy results for DDoS attacks.…”

Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

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Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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“…Advanced persistent threats: these kinds of threats introduce false alarm rate and miss detection rates in IoT systems [92]. Network sniffers: Encrypts all passwords, disable CDP, SSH, SSL and IP security [95] Transmission threats: Fake data insertion in between communication links through three processes insertion, replication and manipulation [99].…”

Section: Suggested Layers In Architecture Model Of Iotmentioning

confidence: 99%

“…NE shows that attackers do not attack frequently when playing hybrid strategy games, and defenders can adjust detection strategies to improve security based on the knowledge of system expertise. In [95] Elwray defined intrusion detection system for IoT environment to lessen the gravity of threats and vulnerabilities. In [98] Ko proposed a platform to prevent cyber-attacks with information on fragile devices connected to the Internet.…”

Section: Prospective Solutions For Iotmentioning

confidence: 99%

A Novel Security Architecture of Internet of Things

Ali¹,

He²,

Ruan³

2019

IJCTE

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In this era, the preeminent internet of things industry paradigms grasps the pledge to transfigure the concept of communication with the connectivity of billions of devices and objects, that's why, IoT persuasion is being appraised for this revaluation. Thousands of scientists, researchers, scholars and organizations are endowing worthwhile attention to support this regime change. Reasoning from this fact, security and privacy issues of IoT are getting highest cogitations in this decade. In this paper, we reviewed previous presented models, which have unanimously great efforts by it selves. But did not pay significant attention towards security and privacy layers as independent layers, so, we suggested that privacy and security layers must be considered in architecture of IoT, because sooner or later, both layers will be the parts of IoT model. We also included different types of threats of IoT layers and their perspective solutions. This contribution of work will be helpful to design and achieve a better security solution regarding IoT to overcome security risks and privacy breaches.

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“…In this manner, regular IDS might not be completely appropriate for IoT systems. Security of IoT is a consistent and major problem; along these, a recent comprehension of the vulnerabilities of security of IoT frameworks and advancement of relating moderation methodologies were needed [5].…”

Section: Idsmentioning

confidence: 99%

“…Hence, FCM begins by initializing U as a membership matrix by random values satisfying equation (3). The fuzzy cluster centers c is then calculated utilizing equation (5). The cost function is then computing utilizing equation (4) and depends on its value the algorithm may stop, whether it is under certain value or the improvement from the past iteration is less than specific threshold.…”

Section: (4)mentioning

confidence: 99%

Intrusion Detection in IoT based Smart Networks using Fuzzy Brain Storm Optimization Technique

B*,

Venkatachalam,

Saroja

2019

IJEAT

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The Internet of Things (IoT) is characterized as an approach where objects are outfitted with sensors, processors, and actuators which include design of hardware board and development, protocols, web APIs, and software systems, which combined to make an associated architecture of embedded systems. This connected environment enables technologies to get associated with different networks, platforms, and devices, making a web of communication which is reforming the manner in which we communicate with the world digitally. These connected embedded systems are changing behaviour and interactions with our environment, networks, and homes, and also with our own bodies in terms of smart devices. Security and privacy are the most significant consideration in the field of real-world communication and mainly on IoTs. With the evolution of IoT the network layer security in the IoT has drawn greater focus. The security vulnerabilities in the IoT system could make security risks based on any application. Therefore there is an essential requirement for IDS for the IoT based systems for avoiding security attacks based on security vulnerabilities. This paper proposed a fuzzy c-means clustering with brain storm optimization algorithm (FBSO) for IDS based on IoT system. The NSL-KDD dataset is utilized to evaluate and simulate the proposed algorithm. The results demonstrate that the proposed technique efficiently recognize intrusion attacks and decrease the network difficulties

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Intrusion detection systems for IoT-based smart environments: a survey

Cited by 300 publications

References 97 publications

A machine learning approach for imputation and anomaly detection in IoT environment

A machine learning approach for imputation and anomaly detection in IoT environment

A Novel Security Architecture of Internet of Things

Intrusion Detection in IoT based Smart Networks using Fuzzy Brain Storm Optimization Technique

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