Deep Learning for Identifying Malicious Firmware

Mapping Intimacies ◽

10.5121/csit.2021.111506 ◽

2021 ◽

Author(s):

David Noever ◽

Samantha E. Miller Noever

Keyword(s):

Deep Learning ◽

Operating Systems ◽

Deep Neural Network ◽

Forensic Analysis ◽

Embedded Devices ◽

Learning Methods ◽

Digit Recognition ◽

Image Conversion ◽

Thumbnail Images ◽

The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and alsothat typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants.

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Detection Method for Classifying Malicious Firmware

International Journal of Network Security & Its Applications ◽

10.5121/ijnsa.2021.13601 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1-9

Author(s):

David Noever ◽

Samantha E. Miller Noever

Keyword(s):

Operating Systems ◽

Deep Neural Network ◽

Detection Method ◽

Forensic Analysis ◽

Embedded Devices ◽

Learning Methods ◽

Digit Recognition ◽

Image Conversion ◽

Thumbnail Images ◽

The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants. To explain how the model makes classification decisions, the research applies traditional statistical methods such as both single and ensembles of decision trees with identifiable pixel or byte values that contribute the malicious or benign determination.

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Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13132575 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2575

Author(s):

Jiangbo Xi ◽

Ming Cong ◽

Okan K. Ersoy ◽

Weibao Zou ◽

Chaoying Zhao ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Hyperspectral Image ◽

Least Squares Method ◽

Support Vector ◽

Learning Networks ◽

Learning Methods ◽

Sliding Windows ◽

Deep Layers

Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number of pixels used as the training instances. Therefore, it is hard to fully use the advantages of deep learning networks; for example, the very deep layers with a large number of parameters lead to overfitting. This paper proposed a dynamic wide and deep neural network (DWDNN) for HSI classification, which includes multiple efficient wide sliding window and subsampling (EWSWS) networks and can grow dynamically according to the complexity of the problems. The EWSWS network in the DWDNN was designed both in the wide and deep direction with transform kernels as hidden units. These multiple layers of kernels can extract features from the low to high level, and because they are extended in the wide direction, they can learn features more steadily and smoothly. The sliding windows with the stride and subsampling were designed to reduce the dimension of the features for each layer; therefore, the computational load was reduced. Finally, all the weights were only from the fully connected layer, and the iterative least squares method was used to compute them easily. The proposed DWDNN was tested with several HSI data including the Botswana, Pavia University, and Salinas remote sensing datasets with different numbers of instances (from small to big). The experimental results showed that the proposed method had the highest test accuracies compared to both the typical machine learning methods such as support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and the recently proposed deep learning methods including the 2D convolutional neural network (CNN) and the 3D CNN designed for HSI classification.

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Tri-net for Semi-Supervised Deep Learning

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/278 ◽

2018 ◽

Cited By ~ 11

Author(s):

Dong-Dong Chen ◽

Wei Wang ◽

Wei Gao ◽

Zhi-Hua Zhou

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Error Rate ◽

Deep Neural Network ◽

Deep Neural Networks ◽

State Of The Art ◽

Fine Tuning ◽

Learning Methods ◽

Model Initialization

Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudo-labeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8.30% error rate on CIFAR-10 by using only 4000 labeled examples.

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Hybrid CNN-LSTM Model for Answer Identification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4281.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1163-1166

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Comparative Analysis ◽

Hybrid Model ◽

Deep Neural Network ◽

Extraction Methods ◽

Learning Methods ◽

Automatic Feature Extraction

User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .

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Skin Lesion Classification Using Deep Learning Methods

Математическая биология и биоинформатика ◽

10.17537/2020.15.180 ◽

2020 ◽

Vol 15 (2) ◽

pp. 180-194

Author(s):

E.Yu. Shchetinin ◽

A.V. Demidova ◽

D.S. Kulyabov ◽

L.A. Sevastyanov

Keyword(s):

Neural Network ◽

Deep Learning ◽

Skin Lesion ◽

Deep Neural Network ◽

Computer Experiments ◽

Test Sample ◽

Skin Lesions ◽

Learning Methods ◽

Data Set ◽

Noise Contamination

In this paper, we propose an approach to solving the problem of recognizing skin lesions, namely melanoma, based on the analysis of dermoscopic images using deep learning methods. For this purpose, the architecture of a deep convolutional neural network was developed, which was applied to the processing of dermoscopic images of various skin lesions contained in the HAM10000 data set. The data under study were preprocessed to eliminate noise, contamination, and change the size and format of images. In addition, since the disease classes are unbalanced, a number of transformations were performed to balance them. The data obtained in this way were divided into two classes: Melanoma and Benign. Computer experiments using the built deep neural network based on the data obtained in this way have shown that the proposed approach provides 94% accuracy on the test sample, which exceeds similar results obtained by other algorithms.

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Research on Time Series Anomaly Detection: Based on Deep Learning Methods

Journal of Physics Conference Series ◽

10.1088/1742-6596/2132/1/012012 ◽

2021 ◽

Vol 2132 (1) ◽

pp. 012012

Author(s):

Jiaqi Zhou

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Anomaly Detection ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Detection Task ◽

Detection Methods ◽

Learning Methods

Abstract Time series anomaly detection has always been an important research direction. The early time series anomaly detection methods are mainly statistical methods and machine learning methods. With the powerful functions of deep neural network being continuously mined by researchers, the effect of deep neural network in anomaly detection task has been significantly better than the traditional methods. In view of the continuous development and application of deep neural networks such as transformer and graph neural network (GNN) in time series anomaly detection in recent years, the body of research lacks a comparative evaluation of deep learning methods in recent years. This paper studies various deep neural networks suitable for time series, which are divided into three categories according to anomaly detection methods. The evaluation is conducted on public datasets. By analyzing the evaluation criteria, this paper discusses the performance of each model, as well as the problems and development direction in the field of time series anomaly detection in the future. This study found that in the time series anomaly detection task, transformer is suitable for dealing with long-time series prediction, and studying the graph structure of time series may be the best way to deal with time series anomaly detection in the future

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An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism

Future Internet ◽

10.3390/fi11040096 ◽

2019 ◽

Vol 11 (4) ◽

pp. 96 ◽

Cited By ~ 3

Author(s):

Li ◽

Liu ◽

Zhang ◽

Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Deep Neural Network ◽

Attention Mechanism ◽

Sentiment Classification ◽

Linguistic Knowledge ◽

Learning Methods ◽

Classification Tasks ◽

Text Sentiment Analysis

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.

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