Due to the recent advances in the area of deep learning, it has been demonstrated that a deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias better than cardiologists. Moreover, traditionally feature extraction was considered an integral part of ECG pattern recognition; however, recent findings have shown that deep neural networks can carry out the task of feature extraction directly from the data itself. In order to use deep neural networks for their accuracy and feature extraction, high volume of training data is required, which in the case of independent studies is not pragmatic. To arise to this challenge, in this work, the identification and classification of four ECG patterns are studied from a transfer learning perspective, transferring knowledge learned from the image classification domain to the ECG signal classification domain. It is demonstrated that feature maps learned in a deep neural network trained on great amounts of generic input images can be used as general descriptors for the ECG signal spectrograms and result in features that enable classification of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in classifying near 7000 instances by ten-fold cross validation.

The advancements in the Internet has enabled connecting more devices into this technology every day. The emergence of the Internet of Things has aggregated this growth. Lack of security in an IoT world makes these devices hot targets for cyber criminals to perform their malicious actions. One of these actions is the Botnet attack, which is one of the main destructive threats that has been evolving since 2003 into different forms. This attack is a serious threat to the security and privacy of information. Its scalability, structure, strength, and strategy are also under successive development, and that it has survived for decades. A bot is defined as a software application that executes a number of automated tasks (simple but structurally repetitive) over the Internet. Several bots make a botnet that infects a number of devices and communicates with their controller called the botmaster to get their instructions. A botnet executes tasks with a rate that would be impossible to be done by a human being. Nowadays, the activities of bots are concealed in between the normal web flows and occupy more than half of all web traffic. The largest use of bots is in web spidering (web crawler), in which an automated script fetches, analyzes, and files information from web servers. They also contribute to other attacks, such as distributed denial of service (DDoS), SPAM, identity theft, phishing, and espionage. A number of botnet detection techniques have been proposed, such as honeynet-based and Intrusion Detection System (IDS)-based. These techniques are not effective anymore due to the constant update of the bots and their evasion mechanisms. Recently, botnet detection techniques based upon machine/deep learning have been proposed that are more capable in comparison to their previously mentioned counterparts. In this work, we propose a deep learning-based engine for botnet detection to be utilized in the IoT and the wearable devices. In this system, the normal and botnet network traffic data are transformed into image before being given into a deep convolutional neural network, named DenseNet with and without considering transfer learning. The system is implemented using Python programming language and the CTU-13 Dataset is used for evaluation in one study. According to our simulation results, using transfer learning can improve the accuracy from 33.41% up to 99.98%. In addition, two other classifiers of Support Vector Machine (SVM) and logistic regression have been used. They showed an accuracy of 83.15% and 78.56%, respectively. In another study, we evaluate our system by an in-house live normal dataset and a solely botnet dataset. Similarly, the system performed very well in data classification in these studies. To examine the capability of our system for real-time applications, we measure the system training and testing times. According to our examination, it takes 0.004868 milliseconds to process each packet from the network traffic data during testing.

Generative adversarial networks have been able to generate striking results in various domains. This generation capability can be general while the networks gain deep understanding regarding the data distribution. In many domains, this data distribution consists of anomalies and normal data, with the anomalies commonly occurring relatively less, creating datasets that are imbalanced. The capabilities that generative adversarial networks offer can be leveraged to examine these anomalies and help alleviate the challenge that imbalanced datasets propose via creating synthetic anomalies. This anomaly generation can be specifically beneficial in domains that have costly data creation processes as well as inherently imbalanced datasets. One of the domains that fits this description is the hostbased intrusion detection domain. In this work, ADFA-LD dataset is chosen as the dataset of interest containing system calls of small foot-print next generation attacks. The data is first converted into images, and then a Cycle-GAN is used to create images of anomalous data from images of normal data. The generated data is combined with the original dataset and is used to train a model to detect anomalies. By doing so, it is shown that the classification results are improved, with the AUC rising from 0.55 to 0.71, and the anomaly detection rate rising from 17.07% to 80.49%. The results are also compared to SMOTE, showing the potential presented by generative adversarial networks in anomaly generation.

Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

A biometric recognition system is one of the leading candidates for the current and the next generation of smart visual systems. The visual system is the engine of the surveillance cameras that have great importance for intelligence and security purposes. These surveillance devices can be a target of adversaries for accomplishing various malicious scenarios such as disabling the camera in critical times or the lack of recognition of a criminal. In this work, we propose a cross-layer biometric recognition system that has small computational complexity and is suitable for mobile Internet of Things (IoT) devices. Furthermore, due to the involvement of both hardware and software in realizing this system in a decussate and chaining structure, it is easier to locate and provide alternative paths for the system flow in the case of an attack. For security analysis of this system, one of the elements of this system named the advanced encryption standard (AES) is infected by four different Hardware Trojansthat target different parts of this module. The purpose of these Trojans is to sabotage the biometric data that are under process by the biometric recognition system. All of the software and the hardware modules of this system are implemented using MATLAB and Verilog HDL, respectively. According to the performance evaluation results, the system shows an acceptable performance in recognizing healthy biometric data. It is able to detect the infected data, as well. With respect to its hardware results, the system may not contribute significantly to the hardware design parameters of a surveillance camera considering all the hardware elements within the device.

The SECOM dataset contains information about a semiconductor production line, entailing the products that failed the in-house test line and their attributes. This dataset, similar to most semiconductor manufacturing data, contains missing values, imbalanced classes, and noisy features. In this work, the challenges of this dataset are met and many different approaches for classification are evaluated to perform fault diagnosis. We present an experimental evaluation that examines 288 combinations of different approaches involving data pruning, data imputation, feature selection, and classification methods, to find the suitable approaches for this task. Furthermore, a novel data imputation approach, namely "In-painting KNN-Imputation" is introduced and is shown to outperform the common data imputation technique. The results show the capability of each classifier, feature selection method, data generation method, and data imputation technique, with a full analysis of their respective parameter optimizations.Big Data Cogn. Comput. 2018, 2, 30 2 of 20 of fault detection and diagnosis. Previous work has been done to classify this dataset to handle the imbalanced data and irrelevant features. In [1,2], the challenge of imbalanced data was evaluated and approaches for oversampling the minority distribution to create balance between the classes was introduced. In [3], the challenge of imbalanced data was evaluated from an under-sampling perspective as well, showing that oversampling performs better on this dataset. In [2,4-6] different approaches for feature selection were proposed to rise to the challenge of noisy features. The challenge of missing data remains unexplored in the SECOM dataset. To the authors' knowledge, no literature thoroughly classifies the SECOM dataset after performing specialized data imputation.In this work the SECOM dataset is classified using a plethora of combination of approaches. The three challenges of data imbalance, missing data, and noisy data are handled via synthetic data generation, data imputation, and feature selection, respectively. Moreover, different classifiers are evaluated, and their performance is analyzed based on the task at hand. To face the challenge of missing data, a novel data imputation technique called "In-painting KNN-Imputation" is introduced which is inspired by image in-painting. In the end, leveraging the feature importance delivered by the classification model, fault diagnosis is performed, that demonstrates which features and measured parameters during the manufacturing process have high effect on the failure of the device.The rest of the paper is organized as follows. In Section 2 the background knowledge is provided. In this section, the semiconductor manufacturing, the manufacturing processes in this industry are conversed. The methodology of our work including the classification stages, the processes of data preparation, and procedures of constructing, evaluating, and interpreting the model for the data are discussed in Section 3. The designed and executed ex...

Abstract:In this review article for Internet of Things (IoT) applications, important low-power design techniques for digital and mixed-signal analog-digital converter (ADC) circuits are presented. Emerging low voltage logic devices and non-volatile memories (NVMs) beyond CMOS are illustrated. In addition, energy-constrained hardware security issues are reviewed. Specifically, light-weight encryption-based correlational power analysis, successive approximation register (SAR) ADC security using tunnel field effect transistors (FETs), logic obfuscation using silicon nanowire FETs, and all-spin logic devices are highlighted. Furthermore, a novel ultra-low power design using bio-inspired neuromorphic computing and spiking neural network security are discussed.