Automated abnormal potato plant detection system using deep learning models and portable video cameras

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

Download Full-text

Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Complex & Intelligent Systems ◽

10.1007/s40747-021-00565-w ◽

2021 ◽

Author(s):

Aakshi Mittal ◽

Mohit Dua

Keyword(s):

Deep Learning ◽

Speech Synthesis ◽

Short Term Memory ◽

Detection System ◽

Performance Comparison ◽

Voice Conversion ◽

Learning Models ◽

Dynamic Features ◽

User Identification ◽

Detection Systems

AbstractDetection of spoof is essential for improving the performance of current scenario of Automatic Speaker Verification (ASV) systems. Empowerment to both frontend and backend parts can build the robust ASV systems. First, this paper discuses performance comparison of static and static–dynamic Constant Q Cepstral Coefficients (CQCC) frontend features by using Long Short Term Memory (LSTM) with Time Distributed Wrappers model at the backend. Second, it performs comparative analysis of ASV systems built using three deep learning models LSTM with Time Distributed Wrappers, LSTM and Convolutional Neural Network at backend and using static–dynamic CQCC features at frontend. Third, it discusses implementation of two spoof detection systems for ASV by using same static–dynamic CQCC features at frontend and different combination of deep learning models at backend. Out of these two, the first one is a voting protocol based two-level spoof detection system that uses CNN, LSTM model at first level and LSTM with Time Distributed Wrappers model at second level. The second one is a two-level spoof detection system with user identification and verification protocol, which uses LSTM model for user identification at first level and LSTM with Time Distributed Wrappers for verification at the second level. For implementing the proposed work, a variation in ASVspoof 2019 dataset has been used to introduce all types of spoofing attacks such as Speech Synthesis (SS), Voice Conversion (VC) and replay in single set of dataset. The results show that, at frontend, static–dynamic CQCC feature outperform static CQCC features and at the backend, hybrid combination of deep learning models increases accuracy of spoof detection systems.

Download Full-text

Packet Preprocessing in CNN-Based Network Intrusion Detection System

Electronics ◽

10.3390/electronics9071151 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1151 ◽

Cited By ~ 1

Author(s):

Wooyeon Jo ◽

Sungjin Kim ◽

Changhoon Lee ◽

Taeshik Shon

Keyword(s):

Neural Network ◽

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Model ◽

Learning Models ◽

Packet Data ◽

Deep Learning Model ◽

Better Than

The proliferation of various connected platforms, including Internet of things, industrial control systems (ICSs), connected cars, and in-vehicle networks, has resulted in the simultaneous use of multiple protocols and devices. Chaotic situations caused by the usage of different protocols and various types of devices, such as heterogeneous networks, implemented differently by vendors renders the adoption of a flexible security solution difficult, such as recent deep learning-based intrusion detection system (IDS) studies. These studies optimized the deep learning model for their environment to improve performance, but the basic principle of the deep learning model used was not changed, so this can be called a next-generation IDS with a model that has little or no requirements. Some studies proposed IDS based on unsupervised learning technology that does not require labeled data. However, not using available assets, such as network packet data, is a waste of resources. If the security solution considers the role and importance of the devices constituting the network and the security area of the protocol standard by experts, the assets can be well used, but it will no longer be flexible. Most deep learning model-based IDS studies used recurrent neural network (RNN), which is a supervised learning model, because the characteristics of the RNN model, especially when the long-short term memory (LSTM) is incorporated, are better configured to reflect the flow of the packet data stream over time, and thus perform better than other supervised learning models such as convolutional neural network (CNN). However, if the input data induce the CNN’s kernel to sufficiently reflect the network characteristics through proper preprocessing, it could perform better than other deep learning models in the network IDS. Hence, we propose the first preprocessing method, called “direct”, for network IDS that can use the characteristics of the kernel by using the minimum protocol information, field size, and offset. In addition to direct, we propose two more preprocessing techniques called “weighted” and “compressed”. Each requires additional network information; therefore, direct conversion was compared with related studies. Including direct, the proposed preprocessing methods are based on field-to-pixel philosophy, which can reflect the advantages of CNN by extracting the convolutional features of each pixel. Direct is the most intuitive method of applying field-to-pixel conversion to reflect an image’s convolutional characteristics in the CNN. Weighted and compressed are conversion methods used to evaluate the direct method. Consequently, the IDS constructed using a CNN with the proposed direct preprocessing method demonstrated meaningful performance in the NSL-KDD dataset.

Download Full-text

Attention-Based Deep Learning Models for Detection of Fake News in Social Networks

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.295809 ◽

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Deep Learning ◽

Deception Detection ◽

Detection System ◽

Network Models ◽

Complex Problem ◽

Learning Approaches ◽

Fake News ◽

Learning Models ◽

Neural Network Models ◽

Deep Learning Model

Automatic fake news detection is a challenging problem in deception detection. While evaluating the performance of deep learning-based models, if all the models are giving higher accuracy on a test dataset, it will make it harder to validate the performance of the deep learning models under consideration. So, we will need a complex problem to validate the performance of a deep learning model. LIAR is one such complex, much resent, labeled benchmark dataset which is publicly available for doing research on fake news detection to model statistical and machine learning approaches to combating fake news. In this work, a novel fake news detection system is implemented using Deep Neural Network models such as CNN, LSTM, BiLSTM, and the performance of their attention mechanism is evaluated by analyzing their performance in terms of Accuracy, Precision, Recall, and F1-score with training, validation and test datasets of LIAR.

Download Full-text

MALWARE DETECTION SYSTEM BASED ON DEEP LEARNING TECHNIQUE

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.1.1.177 ◽

2021 ◽

Vol 1 (1) ◽

pp. 33-44

Author(s):

Zahraa Z. Edie ◽

Ammar D. Jasim

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection System ◽

Learning Models ◽

Future Evolution ◽

Malware Classification ◽

Learning Technique ◽

Family Classification

In this paper, we propose a malware classification and detection framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets, we applied a deep Convolutional Neural Network (CNN) based on Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less overfitting problems than the current popular CNN models such as VGG16, The experimental results on a Malimg Dataset which is comprising 9,821 samples from 26 different families ,Malware samples are represented as byteplot grayscale images and a deep neural network is trained freezing the convolutional layers of Xception model adapting the last layer to malware family classification , The performance of our approach was compared with other methods including KNN, SVM, VGG16 etc. , the Xception model can effectively be used to classify and detect malware families and achieve the highest validation accuracy than all other approaches including VGG16 model which are using image-based malware, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion’s solution.

Download Full-text

An Automated Framework for Patient Identification and Verification Using Deep Learning

Revue d intelligence artificielle ◽

10.18280/ria.340605 ◽

2020 ◽

Vol 34 (6) ◽

pp. 709-719

Author(s):

Suresh Tommandru ◽

Domnic Sandanam

Keyword(s):

Deep Learning ◽

Contrast Enhancement ◽

Real Time ◽

Detection System ◽

Medical Emergency ◽

Light Illumination ◽

Learning Models ◽

Patient Identification ◽

Illumination Problem ◽

The Face

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

Download Full-text

A Comparative Analysis of Variant Deep Learning Models for COVID-19 Protective Face Mask Detection

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.5791 ◽

2021 ◽

Vol 12 (6) ◽

pp. 2841-2848

Author(s):

Rohan Katari Et al.

Keyword(s):

Deep Learning ◽

Comparative Analysis ◽

Disease Transmission ◽

Detection System ◽

Face Mask ◽

Public Spaces ◽

World Health ◽

Learning Models ◽

The World ◽

Health Organization

The world is in the midst of a paramount pandemic owing to the rapid dissemination of coronavirus disease (COVID-19) brought about by the spread of the virus ‘SARS-CoV-2’. It is mainly transmitted among persons through airborne diffusion of droplets containing the virus produced by an infected person sneezing or coughing without covering their face. The World Health Organization (WHO) has issued numerous guidelines which state that the spread of this disease can be limited by people shielding their faces with protective face masks when in public or in crowded areas. As a precautionary measure, many nations have implemented obligations for face mask usage in public spaces. But manual monitoring of huge crowds in public spaces for face masks is laborious. Hence, this requires the development of an automated face mask detection system using deep learning models and related technologies. The detection system should be viable and deployable in real-time, predicting the result accurately so as to be used by monitoring bodies to ensure that the face mask guidelines are followed by the public thereby preventing the disease transmission. In this paper we aim to perform a comparative analysis of various sophisticated image classifiers based on deep learning, in terms of vital metrics of performance to identify the effective deep learning based model for face mask detection.

Download Full-text

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Sustainability ◽

10.3390/su12062475 ◽

2020 ◽

Vol 12 (6) ◽

pp. 2475 ◽

Cited By ~ 1

Author(s):

Jae-joon Chung ◽

Hyun-Jung Kim

Keyword(s):

Neural Network ◽

Deep Learning ◽

Air Quality ◽

Deep Neural Network ◽

Short Term Memory ◽

Detection System ◽

Learning Models ◽

Generative Adversarial Network ◽

Detection Model ◽

Adversarial Network

This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

Download Full-text

Intelligent Intrusion Detection System Using Deep Learning Models

2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei51242.2021.9452880 ◽

2021 ◽

Author(s):

Sumit Varshney ◽

Shikha ◽

Shefali Singhi ◽

Bharti Sharma

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Models

Download Full-text

Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

Complexity ◽

10.1155/2021/5538896 ◽

2021 ◽

Vol 2021 ◽

pp. 1-23

Author(s):

Basim Mahbooba ◽

Radhya Sahal ◽

Wael Alosaimi ◽

Martin Serrano

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Detection System ◽

Learning Technologies ◽

Machine Learning Techniques ◽

Learning Models ◽

Network Intrusion ◽

Learning Techniques ◽

The Impact

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS.

Download Full-text