ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili, M.; Goki, Seyedamiryousef Hosseini; Masjidi, Behnam Hajipour Khire; Sameh, Mahdi; Gharagozlou, Hamid; Mohammed, Amin Salih

doi:10.1155/2022/8481452

Cited by 37 publications

(12 citation statements)

References 95 publications

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“…ML approach can be applied to IoT security for Data Collection and Preprocessing, Feature Extraction, Feature selection, Model Selection, Model Training, and Anomaly Detection. 5 IoT has become part of day-to-day life of humans, as improving performance IoT has changed human life by transforming feature networks and results in improving the quality of living. This concept refers to a massive network of internet-connected devices, often known as IoT devices, these devices include a large number of gadgets, a hard drive, powerful computing, and effective communication capabilities.…”

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

“…ML algorithms use IDS to train and adapt to data patterns, anomalies, and threats in IoT network traffic and their system behaviors. ML approach can be applied to IoT security for Data Collection and Preprocessing, Feature Extraction, Feature selection, Model Selection, Model Training, and Anomaly Detection 5 . IoT has become part of day‐to‐day life of humans, as improving performance IoT has changed human life by transforming feature networks and results in improving the quality of living.…”

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

“…ML classifiers such as naive Bayes, neural networks, and support vector machines (SVMs), are used in classification and to evaluate the performance of the model. 5 Performance and computational complexity are major concerns in NIDS for a better detection rate. This can be achieved by detecting and removing irrelevant features from the dataset and retrieving more significant features.…”

Section: Introductionmentioning

confidence: 99%

“…The NIDS uses a machine learning (ML) classifier to distinguish between regular network traffic and data associated with network attacks for detection purposes based on past data. ML classifiers such as naive Bayes, neural networks, and support vector machines (SVMs), are used in classification and to evaluate the performance of the model 5 . Performance and computational complexity are major concerns in NIDS for a better detection rate.…”

Section: Introductionmentioning

confidence: 99%

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Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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Network Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

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Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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“…For determining intrusions, ML and DL mechanisms were introduced in Jayalaxmi et al 38 DNN was analyzed in Vinayakumar et al 39 for efficient intrusion detection. ML algorithms were explained in Esmaeili et al 40 to diagnose DDoS attacks in IoT.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Habib,

Khursheed

2024

Concurrency and Computation

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SummaryInternet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.

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ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Cited by 37 publications

References 95 publications

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

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