Biblio

Successive approximation register analog-to-digital converter (SAR ADC) is widely adopted in the Internet of Things (IoT) systems due to its simple structure and high energy efficiency. Unfortunately, SAR ADC dissipates various and unique power features when it converts different input signals, leading to severe vulnerability to power side-channel attack (PSA). The adversary can accurately derive the input signal by only measuring the power information from the analog supply pin (AVDD), digital supply pin (DVDD), and/or reference pin (Ref) which feed to the trained machine learning models. This paper first presents the detailed mathematical analysis of power side-channel attack (PSA) to SAR ADC, concluding that the power information from AVDD is the most vulnerable to PSA compared with the other supply pin. Then, an LSB-reused protection technique is proposed, which utilizes the characteristic of LSB from the SAR ADC itself to protect against PSA. Lastly, this technique is verified in a 12-bit 5 MS/s secure SAR ADC implemented in 65nm technology. By using the current waveform from AVDD, the adopted convolutional neural network (CNN) algorithms can achieve \textgreater99% prediction accuracy from LSB to MSB in the SAR ADC without protection. With the proposed protection, the bit-wise accuracy drops to around 50%.

An Industrial Control System (ICS) is essential in monitoring and controlling critical infrastructures such as safety and security. Internet of Things (IoT) in ICSs allows cyber-criminals to utilize systems' vulnerabilities towards deploying cyber-attacks. To distinguish risks and keep an eye on malicious activity in networking systems, An Intrusion Detection System (IDS) is essential. IDS shall be used by system admins to identify unwanted accesses by attackers in various industries. It is now a necessary component of each organization's security governance. The main objective of this intended work is to establish a deep learning-depended intrusion detection system that can quickly identify intrusions and other unwanted behaviors that have the potential to interfere with networking systems. The work in this paper uses One Hot encoder for preprocessing and the Auto encoder for feature extraction. On KDD99 CUP, a data - set for network intruding, we categorize the normal and abnormal data applying a Deep Convolutional Neural Network (DCNN), a deep learning-based methodology. The experimental findings demonstrate that, in comparison with SVM linear Kernel model, SVM RBF Kernel model, the suggested deep learning model operates better.

Facial is the most dynamic part of the human body that conveys information about emotions. The level of diversity in facial geometry and facial look makes it possible to detect various human expressions. To be able to differentiate among numerous facial expressions of emotion, it is crucial to identify the classes of facial expressions. The methodology used in this article is based on convolutional neural networks (CNN). In this paper Deep Learning CNN is used to examine Alex net architectures. Improvements were achieved by applying the transfer learning approach and modifying the fully connected layer with the Support Vector Machine(SVM) classifier. The system succeeded by achieving satisfactory results on icv-the MEFED dataset. Improved models achieved around 64.29 %of recognition rates for the classification of the selected expressions. The results obtained are acceptable and comparable to the relevant systems in the literature provide ideas a background for further improvements.

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset.

The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an F\_1 Score of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively.

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity.

Aim: Object Detection is one of the latest topics in today’s world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

In this paper, we established a unified deep learning-based spam filtering method. The proposed method uses the message byte-histograms as a unified representation for all message types (text, images, or any other format). A deep convolutional neural network (CNN) is used to extract high-level features from this representation. A fully connected neural network is used to perform the classification using the extracted CNN features. We validate our method using several open-source text-based and image-based spam datasets.We obtained an accuracy higher than 94% on all datasets.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement Unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration.

The increased use of Convolutional Neural Networks (CNN) in mission-critical systems has increased the need for robust and resilient networks in the face of both naturally occurring faults as well as security attacks. The lack of robustness and resiliency can lead to unreliable inference results. Current methods that address CNN robustness require hardware modification, network modification, or network duplication. This paper proposes MILR a software-based CNN error detection and error correction system that enables recovery from single and multi-bit errors. The recovery capabilities are based on mathematical relationships between the inputs, outputs, and parameters(weights) of the layers; exploiting these relationships allows the recovery of erroneous parameters (iveights) throughout a layer and the network. MILR is suitable for plaintext-space error correction (PSEC) given its ability to correct whole-weight and even whole-layer errors in CNNs.

The technique of concealing a confidential information in a carrier information is known as steganography. When we use digital images as carriers, it is termed as image steganography. The advancements in digital technology and the need for information security have given great significance for image steganographic methods in the area of secured communication. An efficient steganographic system is characterized by a good trade-off between its features such as imperceptibility and capacity. The proposed scheme implements an edge-detection based adaptive steganography with transform domain embedding, offering high imperceptibility and capacity. The scheme employs an adaptive embedding technique to select optimal data-hiding regions in carrier image, using Canny edge detection and a Convolutional Neural Network (CNN). Then, the secret image is embedded in the Dual-Tree Complex Wavelet Transform (DTCWT) coefficients of the selected carrier image blocks, with the help of Singular Value Decomposition (SVD). The analysis of the scheme is performed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC).

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.

Aiming at the problems of the traditional convolutional neural network (CNN), such as too many parameters, single scale feature and inefficiency by some useless features, a lightweight multi-scale network with attention is proposed for facial expression recognition. The network uses the lightweight convolutional neural network model Xception and combines with the convolutional block attention module (CBAM) to learn key facial features; In addition, depthwise separable convolution module with convolution kernel of 3 × 3, 5 × 5 and 7 × 7 are used to extract features of facial expression image, and the features are fused to expand the receptive field and obtain more rich facial feature information. Experiments on facial expression datasets Fer2013 and KDEF show that the expression recognition accuracy is improved by 2.14% and 2.18% than the original Xception model, and the results further verify the effectiveness of our methods.

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a type of automatic program to determine whether the user is human or not. The most common type of CAPTCHA is a kind of message interpretation by twisting the letters and adding slight noises in the background, plays a role of verification code. In this paper, we will introduce the basis of Convolutional Neural Network first. Then based on the handwritten digit recognition using CNN, we will develop a network for CAPTCHA image recognition.

A 'Completely Automated Public Turing test to tell Computers and Humans Apart' or better known as CAPTCHA is a image based test used to determine the authenticity of a user (ie. whether the user is human or not). In today's world, almost all the web services, such as online shopping sites, require users to solve CAPTCHAs that must be read and typed correctly. The challenge is that recognizing the CAPTCHAs is a relatively easy task for humans, but it is still hard to solve for computers. Ideally, a well-designed CAPTCHA should be solvable by humans at least 90% of the time, while programs using appropriate resources should succeed in less than 0.01% of the cases. In this paper, a deep neural network architecture is presented to extract text from CAPTCHA images on various platforms. The central theme of the paper is to develop an efficient & intelligent model that converts image-based CAPTCHA to text. We used convolutional neural network based architecture design instead of the traditional methods of CAPTCHA detection using image processing segmentation modules. The model consists of seven layers to efficiently correlate image features to the output character sequence. We tried a wide variety of configurations, including various loss and activation functions. We generated our own images database and the efficacy of our model was proven by the accuracy levels of 99.7%.

With the developing of computer technology, Convolutional Neural Network (CNN) has made big development in both application region and research field. However, CAPTCHA (one Turing Test to tell difference between computer and human) technology is also widely used in many websites verification process and it has received great attention from researchers. In this essay, we introduced the CNN based on tensorflow framework and use the MINIST data set which is used in handwritten digit recognition to analyze the parameters and the structure of the CNN model. Moreover, we use different activation functions and compares them with different epochs. We also analyze many problems during the experiment to make the original data and the result more accurate.

Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of an event or between events by traditional machine learning-based techniques from natural language text is a challenging task due to the complexity of the language to be comprehended by the machines. In this study, the Knowledge-oriented convolutional neural network (K-CNN) technique is used to extract the causal relation from online news articles related to the South China Sea (SCS) dispute. The proposed K-CNN model contains a Knowledge-oriented channel that can capture the causal phrases of causal relationships. A Data-oriented channel that captures the position information was added to the K-CNN model in this phase. The online news articles were collected from the national news agency and then the sentences which contain relation such as causal, message-topic, and product-producer were extracted. Then, the extracted sentences were annotated and converted into lower form and base form followed by transformed into the vector by looking up the word embedding table. A word filter that contains causal keywords was generated and a K-CNN model was developed, trained, and tested using the collected data. Finally, different architectures of the K-CNN model were compared to find out the most suitable architecture for this study. From the study, it was found out that the most suitable architecture was the K-CNN model with a Knowledge-oriented channel and a Data-oriented channel with average pooling. This shows that the linguistic clues and the position features can improve the performance in extracting the causal relation from the SCS online news articles. Keywords-component; Convolutional Neural Network, Causal Relation Extraction, South China Sea.

Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.

Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data.

This paper proposes a novel model to detect Deep-Fakes, which are hyper-realistic fake videos generated by advanced AI algorithms involving facial superimposition. With a growing number of DeepFakes involving prominent political figures that hold a lot of social capital, their misuse can lead to drastic repercussions. These videos can not only be used to circulate false information causing harm to reputations of individuals, companies and countries, but also has the potential to cause civil unrest through mass hysteria. Hence it is of utmost importance to detect these DeepFakes and promptly curb their spread. We therefore propose a CNN-based model that learns inherently distinct patterns that change between a DeepFake and a real video. These distinct features include pixel distortion, inconsistencies with facial superimposition, skin colour differences, blurring and other visual artifacts. The proposed model has trained a CNN (Convolutional Neural Network), to effectively distinguish DeepFake videos using a frame-based approach based on aforementioned distinct features. Herein, the proposed work demonstrates the viability of our model in effectively identifying Deepfake faces in a given video source, so as to aid security applications employed by social-media platforms in credibly tackling the ever growing threat of Deepfakes, by effectively gauging the authenticity of videos, so that they may be flagged or ousted before they can cause irreparable harm.