Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach

Kotenko, Igor; Saenko, Igor; Kushnerevich, Alexey; Branitskiy, Alexander

doi:10.1109/empdp.2019.8671571

Cited by 26 publications

(13 citation statements)

References 6 publications

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“…Modern big data technologies like Apache Spark and Apache Flink process their data in-memory. Incorporating these technologies to develop new security analytics will enhance the performance and efficiency for security analytics [2,179,180].…”

Section: Computing-in-memorymentioning

confidence: 99%

Deep learning and big data technologies for IoT security

Amanullah

Habeeb

Nasaruddin

et al. 2020

Computer Communications

257

119

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Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. Contents * Corresponding author.

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Section: Computing-in-memorymentioning

confidence: 99%

Deep learning and big data technologies for IoT security

Amanullah

Habeeb

Nasaruddin

et al. 2020

Computer Communications

257

119

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“…Popular devices gaining knowledge of algorithms are incapable to detect complex data breaches [18]. In this research, we examined different algorithms for the different sub-processes of the framework shown in Figure 2 [19].…”

Section: A Framework To Solve Attack Detection In Iot Using Machine Learningmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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“…In response to the SGD update of (L) /modification, the current gradient weight w oscillates. The authors of [9] employed LSTM to identify and categorise Android malware, with the Android real-world malware test dataset attaining the greatest accuracy. To detect attacks, we use LSTM in our proposal.…”

Section: IIImentioning

confidence: 99%

Comparative Study of Properties of Self Compacting Concrete Made With Ground Granulated Blast Furnace Slag and Metakaoline

Meshram¹

2018

IJRASET

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Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection.

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Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach

Cited by 26 publications

References 6 publications

Deep learning and big data technologies for IoT security

Deep learning and big data technologies for IoT security

Attack Detection in IoT using Machine Learning

Comparative Study of Properties of Self Compacting Concrete Made With Ground Granulated Blast Furnace Slag and Metakaoline

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