Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection

Singh, Priyanka; Borgohain, Samir Kumar; Sarkar, Achintya Kumar; Kumar, Jitender; Sharma, Lakhan Dev

doi:10.1155/2023/9544481

Cited by 6 publications

(1 citation statement)

References 58 publications

(63 reference statements)

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“…The choice of two dense layers with Rectified Linear Unit (ReLU) activation function ( Agarap, 2018 ), a learning rate of 10 –3 over 800 epochs, and the Adam as optimizer ( Kingma and Ba, 2015 ) was opted due to the ability of DL to complex information through multiple layers ( Bebis and Georgiopoulos, 1994 ). In addition to FFNN, we utilized various machine learning classifiers for comparative analysis following the previous studies ( Singh et al, 2023 ; Saxe and Berlin, 2015 ). AdaBoost, an ensemble learning method, was chosen for its ability to combine weak classifiers, such as decision trees, to form a robust classifier ( Freund and Schapire, 1997 ).…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Aalam,

Ahanger,

Masoodi

et al. 2024

Front. Mol. Biosci.

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Esophageal cancer (EC) remains a significant health challenge globally, with increasing incidence and high mortality rates. Despite advances in treatment, there remains a need for improved diagnostic methods and understanding of disease progression. This study addresses the significant challenges in the automatic classification of EC, particularly in distinguishing its primary subtypes: adenocarcinoma and squamous cell carcinoma, using histopathology images. Traditional histopathological diagnosis, while being the gold standard, is subject to subjectivity and human error and imposes a substantial burden on pathologists. This study proposes a binary class classification system for detecting EC subtypes in response to these challenges. The system leverages deep learning techniques and tissue-level labels for enhanced accuracy. We utilized 59 high-resolution histopathological images from The Cancer Genome Atlas (TCGA) Esophageal Carcinoma dataset (TCGA-ESCA). These images were preprocessed, segmented into patches, and analyzed using a pre-trained ResNet101 model for feature extraction. For classification, we employed five machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression (LR), Decision Tree (DT), AdaBoost (AD), Random Forest (RF), and a Feed-Forward Neural Network (FFNN). The classifiers were evaluated based on their prediction accuracy on the test dataset, yielding results of 0.88 (SVC and LR), 0.64 (DT and AD), 0.82 (RF), and 0.94 (FFNN). Notably, the FFNN classifier achieved the highest Area Under the Curve (AUC) score of 0.92, indicating its superior performance, followed closely by SVC and LR, with a score of 0.87. This suggested approach holds promising potential as a decision-support tool for pathologists, particularly in regions with limited resources and expertise. The timely and precise detection of EC subtypes through this system can substantially enhance the likelihood of successful treatment, ultimately leading to reduced mortality rates in patients with this aggressive cancer.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Aalam,

Ahanger,

Masoodi

et al. 2024

Front. Mol. Biosci.

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SwiftR: Cross-platform ransomware fingerprinting using hierarchical neural networks on hybrid features

Karbab¹,

Debbabi²,

Derhab³

2023

Expert Systems with Applications

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Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

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The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

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Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection

Cited by 6 publications

References 58 publications

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

SwiftR: Cross-platform ransomware fingerprinting using hierarchical neural networks on hybrid features

Deep learning hybridization for improved malware detection in smart Internet of Things

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