Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

Lawal, Muhammad Aminu; Shaikh, Riaz Ahmed; Hassan, Syed Raheel

doi:10.1109/access.2020.2976624

Cited by 26 publications

(13 citation statements)

References 60 publications

(79 reference statements)

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“…Furthermore, data integration and correlation are other important challenges from the NSM perspective [8,153]. Recently, some authors have pointed the need of designing and implementing high-level SIEMs and effective IDSs that can be adapted to IoT protocols [137,152,[154][155][156]. Most of research papers in recent years have been focused in the proposal of new IDSs and/or enhancing the detection methods [133,135,[137][138][139]157].…”

Section: Iot and Iiotmentioning

confidence: 99%

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Present and Future of Network Security Monitoring

2021

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Network Security Monitoring (NSM) is a popular term to refer to the detection of security incidents by monitoring the network events. An NSM system is central for the security of current networks, given the escalation in sophistication of cyberwarfare. In this paper, we review the state-of-the-art in NSM, and derive a new taxonomy of the functionalities and modules in an NSM system. This taxonomy is useful to assess current NSM deployments and tools for both researchers and practitioners. We organize a list of popular tools according to this new taxonomy, and identify challenges in the application of NSM in modern network deployments, like Software Defined Network (SDN) and Internet of Things (IoT).

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Section: Iot and Iiotmentioning

confidence: 99%

“…Additionally, some authors take explicitly into account the constraints in IoT resources in their IDS proposal [137]. Other authors highlight the necessity of anomaly detection and mitigation in IoT networks [156,158], as well as the lack of IoT datasets for training and testing detection algorithms [13,141].…”

Section: Iot and Iiotmentioning

confidence: 99%

Present and Future of Network Security Monitoring

2021

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“…The anomaly-based IDS can detect unknown attacks, even though it can be suitable for IoT. It suffers from false alarms when normal traffic is classified as abnormal traffic [26]. Consequently, the fog computing paradigm can be employed in the IoT to effectively host anomaly mitigation frameworks.…”

Section: Intrusion Detection System (Ids)mentioning

confidence: 99%

“…The anomaly-based IDS can detect unknown attacks, even though it can be suitable for IoT. It suffers from false alarms when normal traffic is classified as abnormal traffic [26].…”

Section: Intrusion Detection System (Ids)mentioning

confidence: 99%

An Anomaly Mitigation Framework for IoT Using Fog Computing

2020

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The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

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“…These facts make it difficult to deploy robust security mechanisms and allow attackers to disable or compromise IoT devices, and often to disable or compromise the entire environment using these IoT devices. The main principles of security, which are confidentiality, integrity, and availability (CIA), are also important when implementing IoT devices [ 1 ]. IoT manufacturers place little emphasis on the safety of IoT devices due to financial savings.…”

Section: Introductionmentioning

confidence: 99%

Resistance of IoT Sensors against DDoS Attack in Smart Home Environment

Huraj

Šimon

Horák

2020

Sensors

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Smart devices along with sensors are gaining in popularity with the promise of making life easier for the owner. As the number of sensors in an Internet of Things (IoT) system grows, a question arises as to whether the transmission between the sensors and the IoT devices is reliable and whether the user receives alerts correctly and in a timely manner. Increased deployment of IoT devices with sensors increases possible safety risks. It is IoT devices that are often misused to create Distributed Denial of Service (DDoS) attacks, which is due to the weak security of IoT devices against misuse. The article looks at the issue from the opposite point of view, when the target of a DDoS attack are IoT devices in a smart home environment. The article examines how IoT devices and the entire smart home will behave if they become victims of a DDoS attack aimed at the smart home from the outside. The question of security was asked in terms of whether a legitimate user can continue to control and receive information from IoT sensors, which is available during normal operation of the smart home. The case study was done both from the point of view of the attack on the central units managing the IoT sensors directly, as well as on the smart-home personal assistant systems, with which the user can control the IoT sensors. The article presents experimental results for individual attacks performed in the case study and demonstrates the resistance of real IoT sensors against DDoS attack. The main novelty of the article is that the implementation of a personal assistant into the smart home environment increases the resistance of the user’s communication with the sensors. This study is a pilot testing the selected sensor sample to show behavior of smart home under DDoS attack.

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Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

Cited by 26 publications

References 60 publications

Present and Future of Network Security Monitoring

Present and Future of Network Security Monitoring

An Anomaly Mitigation Framework for IoT Using Fog Computing

Resistance of IoT Sensors against DDoS Attack in Smart Home Environment

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