Adversarial Defense: DGA-Based Botnets and DNS Homographs Detection Through Integrated Deep Learning

Ravi, Vinayakumar; Alazab, Mamoun; Subramaniam, Sriram; Arunachalam, Ajay

doi:10.1109/tem.2021.3059664

Cited by 59 publications

(29 citation statements)

References 42 publications

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“…The model performance establishes strong generalizability of the approach through tests for a set of three novel diseases (taken from two different open-source datasets [ 64 ], [ 65 ]): COVID-19, SARS-CoV-1 and MERS-CoV. The main adversarial attack algorithms identified to target the proposed model include the L-BFGS algorithm and Fast gradient sign method (FGSM) [ 82 ]. Moreover, quantitatively analysing the vulnerability of the model to possible adversarial attacks w.r.t.…”

Section: Discussionmentioning

confidence: 99%

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network

et al. 2022

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Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling its rapid spread. Moreover, development of a conventional CAD model is only possible after disease outbreak and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, recognizing them is a challenge in existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have potential to cause pandemic in future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify chest X-ray scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

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Section: Discussionmentioning

confidence: 99%

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network

et al. 2022

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“…So that defense mechanism is needed to keep the system away from malicious events. Hence, this kind of adversarial attack was defended by implementing the security algorithm or locking framework [32].…”

Section: Case Studymentioning

confidence: 99%

Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique

Dewangan

Sahu

et al. 2022

Multimed Tools Appl

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Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.

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“…In recent times, neural networks are increasingly leveraging at the provisioning of smart systems for health diagnostics and treatment. The machine learning [24] and deep learning [25]- [28] approaches were developed in previous works for the automated diagnoses of a brain hemorrhage [29], [30]. The brain hemorrhage classification proposed by different authors using different methods to diagnose specific types.…”

Section: Related Workmentioning

confidence: 99%

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

et al. 2021

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Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

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Adversarial Defense: DGA-Based Botnets and DNS Homographs Detection Through Integrated Deep Learning

Cited by 59 publications

References 42 publications

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network

Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

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