Enhancing IoT Botnets Attack Detection Using Machine Learning-IDS and Ensemble Data Preprocessing Technique

Hikal, Noha A.; El-Gayar, M. M.

doi:10.1007/978-981-15-3075-3_6

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…The results comparing the proposed model on the Flickr8K and Flickr30K datasets are presented in Table III IV, the proposed model yields a strong CD score of 123.9 and exhibits relative enhancements of around 0.9%, 0.5%, and 0.7% in B-4, MR, and CD scores, respectively. We can use the proposed model in IoT systems in [33], [34] to ensure controllability, safety and effectiveness as a future work. Unlike other methods, this improvement stems from the proposed model's image feature maps incorporating spectral information alongside spatial and semantic details.…”

Section: F Discussionmentioning

confidence: 99%

Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

EL-GAYAR

2023

IJACSA

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While modern systems for managing, retrieving, and analyzing images heavily rely on deriving semantic captions to categorize images, this task presents a considerable challenge due to the extensive capabilities required for manual processing, particularly with large images. Despite significant advancements in automatic image caption generation and human attention prediction through convolutional neural networks, there remains a need to enhance attention models in these networks through efficient multi-scale features utilization. Addressing this need, our study presents a novel image decoding model that integrates a wavelet-driven convolutional neural network with a dual-stage discrete wavelet transform, enabling the extraction of salient features within images. We utilize a wavelet-driven convolutional neural network as the encoder, coupled with a deep visual prediction model and Long Short-Term Memory as the decoder. The deep Visual Prediction Model calculates channel and location attention for visual attention features, with local features assessed by considering the spatial-contextual relationship among objects. Our primary contribution is to propose an encoder and decoder model to automatically create a semantic caption on the image based on the semantic contextual information and spatial features present in the image. Also, we improved the performance of this model, demonstrated through experiments conducted on three widely used datasets: Flickr8K, Flickr30K, and MSCOCO. The proposed approach outperformed current methods, achieving superior results in BLEU, METEOR, and GLEU scores. This research offers a significant advancement in image captioning and attention prediction models, presenting a promising direction for future work in this field.

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Section: F Discussionmentioning

confidence: 99%

Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

EL-GAYAR

2023

IJACSA

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“…In future work, we suggest to use other dataset (preferring IoT data) and evaluate accuracy. Also, we suggest to evaluate our model against different attacks such as DoS, phishing and forgery attacks such as these attacks demonstrated in these references [29,30].…”

Section: Discussionmentioning

confidence: 99%

A Novel Model for Securing Seals using Blockchain and Digital Signature based on QR Codes

Wezza

El-Gayar

Abo-Elfetoh

2022

Preprint

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Every person in the world needs documents that prove their identity, graduation from the university, residence, marriage, and other essential records that must be safely handled. The credibility of the documents depends on the stamp seal imprints found in them belonging to the government entity, the source of the credibility. But recently, with the rapid development of technology, the number of forged documents and forged stamp seals imprints has increased tremendously, which has led to significant security and social problems. Most of the world's governments depend on securing documents by securing stamp seal imprints through using Ultra Violets inks and issuing them through a centralized environment, which contains many challenges and problems. Central repositories may have security issues such as a single point of failure challenge, data unavailability due to central system failures, or a Denial of Service (DoS) attack. Therefore, this paper will present a proposal that solves the challenges in electronic systems (complete digitalization) and paper systems (partial digitalization). This manuscript proposes a smart securing model to secure stamp seal imprints by encrypting data with stamp seal image of the seal and storing it as a block through the decentralized Blockchain platform. After that, a quick response code (QR) is created to access that block quickly and securely. Also, the stamp seal's hash image and the details of the stamp seal's data source in the blockchain provide a shared, immutable, and transparent history of the stamp seals without relying on any third party. After several experiments, the results proved an accuracy and security rate of stamp seals and documents that reached 98%, with a high retrieval speed. Thus, a safe, fast, and non-changeable environment was provided to retrieve necessary information and ensure its authenticity.

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“…Figure 2 shows the main subphases of this stage. Algorithm 1 shows the preprocessing steps [ 35 , 36 ]. Input dataset includes two datasets of CXR images with 1024 × 1024 and 512 × 512 pixel resolution from different sources [ 37 , 38 ].…”

Section: Proposed Framework and Methodsmentioning

confidence: 99%

COVID‐19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence

Abdulaal

Mehedi

Abusorrah

et al. 2022

Contrast Media & Molecular Imaging

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Coronavirus 2019 (COVID-19) has become a pandemic. e seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. is paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. e second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. is paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

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Enhancing IoT Botnets Attack Detection Using Machine Learning-IDS and Ensemble Data Preprocessing Technique

Cited by 13 publications

References 12 publications

Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

A Novel Model for Securing Seals using Blockchain and Digital Signature based on QR Codes

COVID‐19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence

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