Simulation and Modeling for Anomaly Detection in IoT Network Using Machine Learning

Mukherjee, Indrajit; Sahu, Nilesh Kumar; Sahana, Sudip Kumar

doi:10.1007/s10776-021-00542-7

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Mukherjee et al [ 13 ] proposed to identify anomalies in smart devices and IoT systems. A supervised learning model was used to predict anomalies in historical data, which could be incorporated into real-world scenarios, preventing future anomalies and attacks.…”

Section: Existing Workmentioning

confidence: 99%

“… Verma et al [ 3 ] CIDDS-001, UNSW-NB15, and NSL-KDD Accuracy, specificity, sensitivity, false positive rate, and area under the receiver operating characteristic curve Random search algorithms 96.74% By using CART Khatib et al [ 6 ] UNSW-NB15 Accuracy, recall, F1 score, and ROC AUC curve Not mentioned 95% accuracy with DT, RF, and Nystrom-SVM Naung Soe et al [ 8 ] UNSW-NB 15 Processing time and detection accuracy CFS Not mentioned Khammassi et al [ 12 ] KDD99 dataset and the UNSW-NB15 dataset Accuracy, Recall, DR, FAR Wrapper, GA, and LR With a subset of only 18 features, the KDD99 dataset showed 99.90% accuracy of classification, 99.81% DR, and 0.105% FAR. Mukherjee et al [ 13 ] From the ML data repository Kaggle, which was provided by Xavier. Accuracy Not mentioned 99.4% accuracy in the first case and 99.99% accuracy in the second case Brun et al [ 14 ] Own a simulated dataset Time series of the difference between the numbers of initiated and established TCP connexions per time slot, attack probability predicted by the dense RNN Not mentioned Time-series of the difference between the numbers of initiated and established TCP connections per time slot (10 s) Tyagi et al [ 15 ] …”

Section: Existing Workmentioning

confidence: 99%

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Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

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Section: Existing Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Their model outperformed previous statistical methods by capturing time dependencies and combining evidence from multiple water quality indicators. Mukherjee et al [2] compared various classification algorithms like logistic regression and random forest for detecting anomalies in Internet-of-Things sensor data. Lu et al [3] proposed a sliding window approach to extract time series features and identify outlier subsequences in VOC sensor data, followed by time series decomposition and clustering to pinpoint anomalous values.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM-Based Anomaly Detection in Manufacturing Environmental Monitoring Data

2023

autml

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With the proliferation of environmental monitoring data, using machine learning techniques for anomaly detection in environmental time series data has become an active research direction. This study employs Long Short-Term Memory (LSTM) neural network models to detect anomalies in manufacturing emission data. The research first preprocesses the data by handling missing values and conducting stationarity tests. The data will be divided into training and testing sets, with the model trained on normal data and tested for anomalies. Experiments show LSTM outperforms classic methods like Isolation Forest, Matrix Profile, and AutoEncoder in handling enclosed pipeline emission data. This study is primarily due to LSTM's ability to capture long-term dependencies in time series data. Establishing this model facilitates improved environmental protection and safety management, enables automated monitoring and warning, reduces manual intervention, and lowers enterprise environmental compliance risks. This study provides an effective anomaly detection model for monitoring manufacturing emissions, serving as a reliable reference for introducing machine learning into environmental monitoring domains.

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“…To monitor events between virtual resources and end-users, blockchain and data analytics for audits are used in combination. More recently, in 2022, Mukherjee et al [116] used a supervised ML model to detect anomalies in smart devices and IoT systems, which may then be used in real-world settings to prevent future abnormalities and attacks. An experiment employed ML algorithms over DS2oS traffic trace data [117] and evaluated its effectiveness against the state of the art.…”

Section: Iot Forensicsmentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

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Network forensics focuses on the identification and investigation of internal and external network attacks, the reverse engineering of network protocols, and the uninstrumented investigation of networked devices. It lies at the intersection of digital forensics, incident response and network security. Network attacks exploit software and hardware vulnerabilities and communication protocols. The scope of a network forensic investigation can range from Internet-wide down to a single device's network traffic. Network analysis tools (NATs) aid security professionals and law enforcement in the capturing, identification and analysis of network traffic. However, in most instances, the sheer volume of data to be analyzed is enormous and, despite some built-in NAT automation, the investigation of network traffic is often an arduous process. Furthermore, significant expert time remains wasted in the investigation of a high frequency of false positive alerting from automated systems. To address this globally impacting problem, artificial intelligence based approaches are becoming increasingly employed to automatically detect attacks and increase network traffic classification accuracy. This paper provides a comprehensive survey of the state-of-the-art in network forensics and the application of expert systems, machine learning, deep learning, and ensemble/hybrid approaches to a range of application areas in the field. These include network traffic analysis, intrusion detection systems, Internetof-Things devices, cloud forensics, DNS tunneling, smart grid forensics, and vehicle forensics. In addition, the current challenges and future research directions for each of the aforementioned application areas is discussed.

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Simulation and Modeling for Anomaly Detection in IoT Network Using Machine Learning

Cited by 12 publications

References 14 publications

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

LSTM-Based Anomaly Detection in Manufacturing Environmental Monitoring Data

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

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