Towards a Universal Features Set for IoT Botnet Attacks Detection

Hussain, Faisal; Fayyaz, Ubaid U.; Shah, Ghalib A.; Toqeer, Abdullah; Ali, Ahmad

doi:10.1109/inmic50486.2020.9318106

Cited by 21 publications

(24 citation statements)

References 23 publications

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“…Feature selection plays a significant role in the performance of a machine learning model. After preprocessing the data, we applied a Logistic Regression (LR) algorithm due to its efficient implementation in the existing literature [3,18]. Using the LR algorithm, we selected the ten most significant features to train and test the machine learning models: ['frame.time_delta', 'tcp.time_delta', 'tcp.flags.ack', 'tcp.flags.push', 'tcp.flags.reset', 'mqtt.hdrflags', 'mqtt.msgtype', 'mqtt.qos', 'mqtt.retain', 'mqtt.ver'].…”

Section: Features Selectionmentioning

confidence: 99%

“…Furthermore, it is estimated that by 2025 there will be 75.44 billion IoT connected worldwide [1]. IoT footprints have been identified in various domains such as manufacturing, agriculture, transportation, electric grid, healthcare, among others [2][3][4]. In an IoT-based healthcare system, security is the primary concern as the data is directly related to human beings.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is very challenging to design a security mechanism for IoT devices. The manufacturing industries are producing a massive amount of IoT products to create low-cost IoT devices in a short period [3,18]. Due to the race to the market for capturing the market as earlier as possible, the manufacturers are giving less importance to the device security [3].…”

Section: Introductionmentioning

confidence: 99%

“…The manufacturing industries are producing a massive amount of IoT products to create low-cost IoT devices in a short period [3,18]. Due to the race to the market for capturing the market as earlier as possible, the manufacturers are giving less importance to the device security [3]. Moreover, the IoT device manufacturers also create back doors to access the machine remotely or use that device for their malicious intent.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

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Section: Features Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

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“…The recent trends reveal that the cyber-attacks increasing day by day with the rapid increase of insecure IoT devices [6]. Among the recently reported cyber-attacks, botnet and distributed denial of service (DDoS) attacks are the most prevalent attacks, which are increased both in frequency and magnitude over the last decade [4], [6]. A botnet attack is a cyber-attack in which an attacker first scans a network to look for weakly secured or vulnerable (IoT) devices.…”

Section: Introductionmentioning

confidence: 99%

A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks

Hussain

Abbas

Pires³

et al. 2021

IEEE Access

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The botnet attack is a multi-stage and the most prevalent cyber-attack in the Internet of Things (IoT) environment that initiates with scanning activity and ends at the distributed denial of service (DDoS) attack. The existing studies mostly focus on detecting botnet attacks after the IoT devices get compromised, and start performing the DDoS attack. Similarly, the performance of most of the existing machine learning based botnet detection models is limited to a specific dataset on which they are trained. As a consequence, these solutions do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this work, we first produce a generic scanning and DDoS attack dataset by generating 33 types of scan and 60 types of DDoS attacks. In addition, we partially integrated the scan and DDoS attack samples from three publicly-available datasets for maximum attack coverage to better train the machine learning algorithms. Afterwards, we propose a two-fold machine learning approach to prevent and detect IoT botnet attacks. In the first fold, we trained a state-of-the-art deep learning model, i.e., ResNet-18 to detect the scanning activity in the premature attack stage to prevent IoT botnet attacks. While, in the second fold, we trained another ResNet-18 model for DDoS attack identification to detect IoT botnet attacks. Overall, the proposed two-fold approach manifests 98.89% accuracy, 99.01% precision, 98.74% recall, and 98.87% f1-score to prevent and detect IoT botnet attacks. To demonstrate the effectiveness of the proposed two-fold approach, we trained three other ResNet-18 models over three different datasets for detecting scan and DDoS attacks and compared their performance with the proposed two-fold approach. The experimental results prove that the proposed two-fold approach can efficiently prevent and detect botnet attacks as compared to other trained models.INDEX TERMS Internet of Things, iot botnet, botnet detection, iot botnet attacks, iot botnet ddos attack, ddos attack prevention, ddos attack, iot ddos attack, botnet attack, botnet ddos

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Ensemble Machine Learning‐Based Botnet Attack Detection for IoT Applications

Suchithra

2024

Metaheuristics for Machine Learning

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Towards a Universal Features Set for IoT Botnet Attacks Detection

Cited by 21 publications

References 23 publications

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks

Ensemble Machine Learning‐Based Botnet Attack Detection for IoT Applications

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