Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques

Abid, Ahlem; Farah, Joumana; Korbaa, Ouajdi

doi:10.1007/s10586-023-04087-7

Cited by 14 publications

(3 citation statements)

References 30 publications

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“…A. Abid et al [31] introduced a real-time, distributed, faulttolerant and scalable system for detecting DDoS attacks in Edge-IIoT dataset by leveraging data fusion and big data technologies such as Kafka and Spark Streaming. They achieved the high accuracy of 99.97% and 99.98% in binary and multiclass classification respectively when multilayer perceptron is used.…”

Section: A Handling Of Vast Amounts Of Streaming Iot Trafficmentioning

confidence: 99%

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

Selvam,

Maheswari Balasubramanian

2024

IEEE Access

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Day by day, the number of devices in IoT networks is increasing, and concurrently, the size of botnets in IoT networks is also expanding. Currently, attackers prefer IoT-based botnets to launch DDoS attacks, as IoT devices offer a vast attack surface. Many researchers have proposed machine and deep learning-based classifiers to classify DDoS and benign network traffic in online streams from IoT devices. However, the performance of the traditional machine and deep learning algorithms deteriorates when sudden concept or data drift occurs in the online streams and the volume and velocity of IoT network traffic increases. To address these challenges, we propose UASDAC, an adaptive and scalable data pipeline designed specifically to handle concept drift and detect DDoS traffic in real-time in massive online streams originating from IoT devices. UASDAC incorporates three key components: an online network stream collector for data collection, an online network stream analyzer with an unsupervised drift detector for detecting drift and DDoS traffic, and an online network stream repository for storing streams for future analytics. UASDAC leverages big data technologies to implement all the three components to achieve scalability. Additionally, UASDAC introduces an effective and efficient retraining technique to adapt to novel patterns in online streams in the presence of concept drift. We evaluated the performance of UASDAC in different concept drift scenarios using the benchmark dataset NSL-KDD and the latest IoT dataset IoT23. Our results demonstrate that UASDAC effectively identifies DDoS traffic in the presence of concept drift, achieving an accuracy range of 99.7% to 99.9%.

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Section: A Handling Of Vast Amounts Of Streaming Iot Trafficmentioning

confidence: 99%

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

Selvam,

Maheswari Balasubramanian

2024

IEEE Access

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“…Threats that result in defects in program design are found using the firewall for IDS. Moreover, the IDS [29,30] makes it possible for forensic suspicion to recognize the program administrator's defenses against cyber threats. Systems for detecting intrusions into networks and systems are available.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Muthubalaji,

Muniyaraj,

Rao

et al. 2024

Big Data Min. Anal.

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Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.

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“…Its allure originated from its inherent cost-efficiency and remarkable reliability, which allowed organizations to scale their operations with unprecedented flexibility. However, with the increasing reliance on cloud infrastructure, there emerged an ominous and ever-present threat of cyberattacks [3][4][5]. These nefarious assaults on digital infrastructure disrupt normal system operations, perpetrating malicious activities that compromise data integrity, confidentiality, availability, and privacy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Ramachandran,

Albathan,

Hussain

et al. 2023

Systems

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Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup’99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently.

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Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques

Cited by 14 publications

References 30 publications

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

UASDAC: An Unsupervised Adaptive Scalable DDoS Attack Classification in Large-Scale IoT Network Under Concept Drift

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

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