Securing ZigBee IoT Network Against HULK Distributed Denial of Service Attack

Asonye, Ekele A.; Anwuna, Ifeoma; Musa, Sarhan M.

doi:10.1109/honet50430.2020.9322808

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…This methodology can be utilized in smart agriculture applications and encompasses four ML algorithms, i.e., RF, KNN, Naive Bayes and SVMs. The experimental results showed the high effectiveness of the proposed methodology in distinguishing normal traffic from attack traffic [22].…”

Section: Related Workmentioning

confidence: 90%

“…Their experimental results showed that their approach can provide promising results for network flow classification. Towards the same direction, Asonye et al [22] proposed a network traffic classification methodology aiming to secure ZigBee IoT networks against the HTTP Unbearable Load King (HULK) threat. This methodology can be utilized in smart agriculture applications and encompasses four ML algorithms, i.e., RF, KNN, Naive Bayes and SVMs.…”

Section: Related Workmentioning

confidence: 99%

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Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Πεππές

Daskalakis

Alexakis

et al. 2021

Sensors

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The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.

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Section: Related Workmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Πεππές

Daskalakis

Alexakis

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It takes its name from a satellite weapon used to similarly damage electronics in the James Bond movie of the same name. A GoldenEye attack involves the attacker saturating the target network or system with a lot of traffic, which makes it unable to respond to incoming requests [36,37]. This kind of attack can be very successful if the attacker combines several attack channels to send as much traffic as possible, such as HTTP, TCP, and UDP flooding.…”

Section: Accuracymentioning

confidence: 99%

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

Muthunambu,

Prabakaran,

Kavin

et al. 2024

CMC

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The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with Long Short-Term Memory (LSTM) units. The proposed model can identify various types of cyberattacks, including conventional and distinctive forms. Recurrent networks, a specific kind of feedforward neural networks, possess an intrinsic memory component. Recurrent Neural Networks (RNNs) incorporating Long Short-Term Memory (LSTM) mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods. Metrics such as data types, training duration, accuracy, number of false positives, and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks. RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events, hence enhancing their decision-making capabilities. A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model. This model utilises Recurrent Neural Networks, specifically exploiting LSTM techniques. The proposed model achieves detection accuracy (99.5%), generalisation (99%), and falsepositive rate (0.72%), the parameters findings reveal that it is superior to state-of-the-art techniques.

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“…Due to this attack, the attacker can access databases, application layers, and private data. 55 Asonye et al 56 proposed the security solution against the HULK DDoS attack. The attack, which is very difficult to identify and harmful, was detected with the help of the ZigBee network.…”

Section: Application Layermentioning

confidence: 99%

“…Due to this attack, the attacker can access databases, application layers, and private data 55 . Asonye et al 56 proposed the security solution against the HULK DDoS attack. The attack, which is very difficult to identify and harmful, was detected with the help of the ZigBee network. Trojan horse : This is one of the most common attacks in which if the user downloads some malicious file, then this virus automatically gets installed inside the user PC.…”

Section: Taxonomy Of Iot Security Attacksmentioning

confidence: 99%

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities

Patel,

Kakkar

et al. 2023

Security and Privacy

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With the advancement of innovative technologies, the Internet of Things (IoT) has become quite popular in various applications such as smart homes, smart cities, agriculture, healthcare, and so forth. However, the heterogeneity of IoT protocols can pose security challenges to wireless networks. Thus, we have presented a systematic review of IoT technologies such as ZigBee, Radio Frequency Identification (RFID), Bluetooth, WiFi, Ethernet, AR, embedded subscriber identification modules (E‐SIM), and smart sensors, along with their communication protocols, which are part of the IoT‐layered architecture. We have bifurcated the IoT‐layered architecture into the perception, network, and application layers. Furthermore, we have presented IoT security attacks associated with the perception, network, and application layers of the IoT‐layered architecture. Moreover, we have discussed several IoT security solutions, open issues, and research challenges dedicated to the security of IoT networks. IoT security solutions can provide secure and efficient data communication over wireless networks.

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Securing ZigBee IoT Network Against HULK Distributed Denial of Service Attack

Cited by 6 publications

References 11 publications

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities

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