A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Alimi, Oyeniyi Akeem; Ouahada, Khmaies; Abu‐Mahfouz, Adnan M.; Rimer, Suvendi; Alimi, Kuburat Oyeranti Adefemi

doi:10.3390/su13179597

Cited by 32 publications

(14 citation statements)

References 113 publications

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“…The researchers in [8] proposed an efficient framework that learns minimal temporal preferential attack targeting the LSTM model with electronic medical record inputs, they also proposed an efficient and effective framework that identifies sensitive locations in medical records using adversarial attacks on deep predictive models. The results showed weakness in the deep models, as it was more than half of patients can be successfully attacked by changing only 3% of the recording sites with maximum perturbation less than 0.15 and mean perturbation less than 0.02.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Classification Approach of Network Attacks using Supervised and Unsupervised Learning

Al-Ruwaili,

Ouda

2023

IJACSA

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The increasing scale and sophistication of network attacks have become a major concern for organizations around the world. As a result, there is an increasing demand for effective and accurate classification of network attacks to enhance cyber security measures. Most existing schemes assume that the available training data is labeled; that is, classification is based on supervised learning. However, this is not always the case since the available real data is expected to be unlabeled. In this paper, this issue is tackled by proposing a hybrid classification approach that combines both supervised and unsupervised learning to build a predictive classification model for classifying network attacks. First, unsupervised learning is used to label the data available in the dataset. Then, different supervised machine learning algorithms are utilized to classify data with the labels obtained from the first step and compare the results with the ground truth labels. Moreover, the issue of the unbalanced dataset is addressed using both over-sampling and undersampling techniques. Several experiments have been conducted, using the NSL-KDD dataset, to evaluate the efficiency of the proposed hybrid model and the obtained results demonstrate that the accuracy of our proposed model is comparable to supervised classification methods that assume that all data is labeled.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Classification Approach of Network Attacks using Supervised and Unsupervised Learning

Al-Ruwaili,

Ouda

2023

IJACSA

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“…Theoretical investigation revealed that with random parameters, ELM is more likely to obtain a global optimal solution than traditional networks with all the parameters to be trained [90]. ELM is quite popular nowadays because of its variety of applications such as robotics [91], IoT-based models [92][93][94], control systems [93], etc., as well as its high accuracy, cross-domain adaptation, and low time consumption (training time mostly).…”

Section: Extreme Learning Machinementioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

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“…The CICIDS-2017 dataset is a recently generated open-source dataset provided by the Canadian Institute of Cybersecurity for intrusion detection. The CICIDS-2017 dataset has the attributes of practical real-life network traffic, and its labeling is based on the timestamp, source and destination IPs, source and destination ports, protocols, and attacks [50]. This dataset was captured over a duration of 5 days with 2,830,743 records, 80 network traffic features, and 15 attack types.…”

Section: Used Datasetsmentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

Self Cite

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Cited by 32 publications

References 113 publications

A Hybrid Classification Approach of Network Attacks using Supervised and Unsupervised Learning

A Hybrid Classification Approach of Network Attacks using Supervised and Unsupervised Learning

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

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