ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

Anitha*, A. Arul; Arockiam, L.

doi:10.35940/ijitee.k1875.0981119

Cited by 22 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anitha and Arockiam [45] conducted an Artificial Neural Network-based IDS (AN-NIDS) technique for detecting attacks in RPL. The proposed approach uses MLP to identify DIS and VN attacks.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the proposed mechanism is not suitable for constrained devices. [45] The proposed ANNIDS approach achieved the highest TPR, Precision, Recall, and F-Measure results and detected the DIS and VN attacks accurately. However, the authors did not mention the dataset and selected features used.…”

Section: The Feature Ranking Technique Work By Measuring the Pearson'...mentioning

confidence: 86%

“…Summary: Table 2 shows that some ML-based approaches select the optimum subset of features to improve the detection accuracy, classify the network traffic into normal and abnormal behavior, or use ML algorithms for both purposes. However, several works did not provide details about feature selection technique used in their approach [3,8,10,21,45,50,51,53,54,56,60].…”

Section: Ac Precision Recall Specificitymentioning

confidence: 99%

“…In addition, other studies, such as [7,8,21,45,50,51,53,59,60,62], did not mention the number of features used or selected in their approach. Moreover, two studies [57,60] did not disclose the type of program used in the implementation step, and did not provide information about the dataset and the feature selection technique used.…”

Section: Ac Precision Recall Specificitymentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Al-Amiedy

Anbar

Belaton

et al. 2022

Sensors

View full text Add to dashboard Cite

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

show abstract

“…Anitha and Arockiam [45] conducted an Artificial Neural Network-based IDS (AN-NIDS) technique for detecting attacks in RPL. The proposed approach uses MLP to identify DIS and VN attacks.…”

Section: Resultsmentioning

confidence: 99%

Section: The Feature Ranking Technique Work By Measuring the Pearson'...mentioning

confidence: 86%

Section: Ac Precision Recall Specificitymentioning

confidence: 99%

Section: Ac Precision Recall Specificitymentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

Al-Amiedy

Anbar

Belaton

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…ANN is the most suitable model for IoT attacks detection and has had many implementations. Recently, the authors of [28] implemented an ANN based model for detecting IoT based attacks. The model was successful and can be used on IoT networks to perform intrusion detection.…”

Section: Naive Bayesmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher,

Ullah,

Ahmad

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

show abstract

A Two-Phase Cycle Algorithm Based on Multi-objective Genetic Algorithm and Modified BP Neural Network for Effective Cyber Intrusion Detection

Gong

Liu

Yin

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

Cited by 22 publications

References 11 publications

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

A Two-Phase Cycle Algorithm Based on Multi-objective Genetic Algorithm and Modified BP Neural Network for Effective Cyber Intrusion Detection

Contact Info

Product

Resources

About