Review of DL-Based Generation Techniques of Augmented Images using Portraits Specification

Berrahal, Mohammed; Azizi, Mostafa

doi:10.1109/icds50568.2020.9268710

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…DL is a Machine Learning (ML) based on the artificial neural networks (ANN); it is considered as a computing system inspired by the information processing and distributed communication nodes in biological brain where the machine can learn from several data instances, letting it to classify other instances [11]. Deep learning is widely used in various research fields, it is well known by its detecting capacity for the perfect features in raw data over consecutive nonlinear transformations, with each adjustment attaining further high level of complexity and abstraction [12] [13]. Some of the most known deep learning algorithms are CNN (Convolutional Neural Networks), RNN ( Recurrent Neural Networks), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) [14].…”

Section: B Deep Learning (Dl)mentioning

confidence: 99%

A Lightweight Optimized Deep Learning-based Host-Intrusion Detection System Deployed on the Edge for IoT

Idrissi¹,

Azizi²,

Moussaoui³

2022

IJCDS

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The Internet of Things (IoT) is now present in every domain from applications in smart homes, Smart Cities, Industrial Internet of Things (IIoT), such as e-Health, and beyond. The wide use of Internet of Things is making its security a real concern. Techniques based on artificial intelligence (AI) and its subsets machine learning (ML) and deep learning (DL) are commonly used to develop a secure Intrusion Detection System (IDS) for IoT. Researchers and industrialists are commonly using commercial Internet of Things devices, broadly available on the market. In this paper, we present an analysis of the possibility to deploy a Deep Learning-Based Host-Intrusion Detection System (DL-HIDS) on some specific commercial IoT devices. We performed multiple optimizations regarding the types of our used devices to meet their limited hardware specifications. In our conducted analysis, we consider such criteria, as memory consumption and inference timing (attacks prediction timing), to conclude which model fits better to our proposed lightweight DL-HIDS for each studied device, and to anticipate about which IDS we must generate and expectedly deploy based on the characteristics of the devices we possess. The paper also discusses the proposed methodology for such deployment in a real IoT environment. The obtained results about the implementation of our DL-HIDS on different considered devices (up to 99.74% in accuracy and an inference of not more of 1µs for attacks prediction) are promising and prove that we can manage to install a suited IDS for each device, but it should be minutiously supported by a central IDS in fog or cloud layers.

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Section: B Deep Learning (Dl)mentioning

confidence: 99%

A Lightweight Optimized Deep Learning-based Host-Intrusion Detection System Deployed on the Edge for IoT

Idrissi¹,

Azizi²,

Moussaoui³

2022

IJCDS

Self Cite

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“…CNN or ConvNets are a class of deep neural networks that are used in many fields but mostly in pattern recognition [10]. It is composed of an input layer, many hidden layers in between, and an output layer as shown in Figure 1 like the multi-layer perceptron (MLP) networks.…”

Section: Background 21 Convolutional Neural Networkmentioning

confidence: 99%

Accelerating the update of a DL-based IDS for IoT using deep transfer learning

Idrissi¹,

Azizi²,

Moussaoui³

2021

IJEECS

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<p>Deep learning (DL) models are nowadays broadly applied and have shown outstanding performance in a variety of fields, including our focus topic of "IoTcybersecurity". Deep learning-based intrusion detection system (DL-IDS) models are more fixated and depended on the trained dataset. This poses a problem for these DL-IDS, especially with the known mutation and behavior changes of attacks, which can render them undetected. As a result, the DL-IDShas become outdated. In this work, we present a solution for updating DL-ID Semploying a transfer learning technique that allows us to retrain and fine-tune pre-trained models on small datasets with new attack behaviors. In our experiments, we built CNN-based IDS on the Bot-IoT dataset and updated it on small data from a new dataset named TON-IoT. We obtained promising results in multiple metrics regarding the detection rate and the training between the initial training for the original model and the updated one, in the matter of detecting new attacks behaviors and improving the detection rate for some classes by overcoming the lack of their labeled data.</p>

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“…Sentiment analysis (SA) also known as emotion AI or opinion mining is the analysis of feelings from dematerialized textual sources on large amounts of data (big data), or from images [13]. There are three usual granularity levels for opinion mining [14]: the document, the sentence and the aspect levels [15].…”

Section: Background 21 Sentiment Analysismentioning

confidence: 99%

A comparative study of deep learning based language representation learning models

Boukabous¹,

Azizi²

2021

IJEECS

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Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. In this paper, we highlight the most important language representation learning models in NLP and provide an insight of their evolution. We also summarize, compare and contrast these different models on sentiment analysis, and thus discuss their main strengths and limitations. Our obtained results show that BERT is the best language representation learning model.

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Review of DL-Based Generation Techniques of Augmented Images using Portraits Specification

Cited by 17 publications

References 18 publications

A Lightweight Optimized Deep Learning-based Host-Intrusion Detection System Deployed on the Edge for IoT

A Lightweight Optimized Deep Learning-based Host-Intrusion Detection System Deployed on the Edge for IoT

Accelerating the update of a DL-based IDS for IoT using deep transfer learning

A comparative study of deep learning based language representation learning models

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