Design of Graph-Based Layered Learning-Driven Model for Anomaly Detection in Distributed Cloud IoT Network

Khalaf, Osamah Ibrahim; Ogudo, Kingsley A.; Sangeetha, S.

doi:10.1155/2022/6750757

Cited by 13 publications

(14 citation statements)

References 21 publications

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“…The new parameter "Q ¼ q 1 , q 2 , …, q j h i " given above as in (12) represents the likelihood feature representation to each dimensionality-reduced network traffic features data. The aggregated likelihood feature representation is mathematically represented as given below.…”

Section: • Hosmer Lemeshow Logistic Regression Window-based Attack De...mentioning

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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Section: • Hosmer Lemeshow Logistic Regression Window-based Attack De...mentioning

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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show abstract

“…All strategies used to lower energy-specific hardware components/levels are covered in extreme detail. There is much emphasis on techniques deployed at the hardware-level (network-or server-level) that can lead to energy-efficient or ecologically friendly data centers [122][123][124][125][126][127][128][129][130][131][132].…”

Section: Related Surveysmentioning

confidence: 99%

“…A hypervisor is the system software that works as an operating system (abstraction layer) for virtual machines and coordinates with the underlying hardware components according to the virtual machine's predefined instructions [124][125][126][127]. Virtualization is not a new concept in the IT sector as it has already been implemented with our grand old Main Frames, which belong to second-generation computing devices.…”

Section: Rq5: Describe Various Energy Efficiency Techniques Employed ...mentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…To create learning representations that are efcient and resistant to intrinsic EEG noise as well as inter-and intrasubject variation, deep recurrent convolutional neural networks are used [15]. To avoid the need for handcrafted features, deep belief networks, an unsupervised feature learning architecture, were applied to the sleep data [16]. Compared with handcrafted features, the deep belief network (DBN) technique improved the sleep classifcation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal

Sangeetha

Mathivanan

Muthukumaran

et al. 2023

Mathematical Problems in Engineering

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Electroencephalography (EEG) is a reliable method for identifying the onset of sleepiness behind the wheel. Using EEG technology for driving fatigue detection still presents challenges in extracting informative elements from noisy EEG signals. Due to their extensive computational parallelism, which is similar to how the brain processes information, neural networks have been explored as potential solutions for extracting relevant information from EEG data. The existing machine learning frameworks suffer from high computing costs and slow convergence, both of which contribute to low classification accuracy and efficiency due to the large number of hyper parameters that need to be improved. It is necessary to automate this micronap detection process before it can be used in real-time scenarios. To distinguish between micronap and non-micronap states, a deep neural network (DNN) framework is developed in this research using different EEG representations as input. Additional EEG representations utilized in this investigation include cleaned EEG as a time series, log-power spectrum, 2D-spatial map of log-power spectrum, and raw EEG. Finally, traditional machine learning algorithms are evaluated for their effectiveness in detecting micronaps from these EEG inputs. The findings suggest that micronap detection can be greatly improved by combining cleaned EEG with DNN.

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Design of Graph-Based Layered Learning-Driven Model for Anomaly Detection in Distributed Cloud IoT Network

Cited by 13 publications

References 21 publications

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

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

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal

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