Automated Video and Audio based Stress Detection using Deep Learning Techniques

In today's world, stress has become an undoubtedly severe problem that affects people's health. Stress can modify a person's behavior, ideas, and feelings in addition to having an impact on mental health. Unchecked stress can contribute to chronic illnesses including high blood pressure, diabetes, and obesity. Early stress detection promotes a healthy lifestyle in society. This work demonstrates a deep learning-based method for identifying stress from facial expressions and speech signals.An image dataset formed by collecting images from the web is used to construct and train the model Convolution Neural Network (CNN), which then divides the images into two categories: stressed and normal. Recurrent Neural Network (RNN), which is used to categorize speech signals into stressed and normal categories based on the features extracted by the MFCC (Mel Frequency Cepstral Coefficient), is thought to perform better on sequential data since it maintains the past results to determine the final output

Development of a Reference Design for Intrusion Detection Using Neural Networks for a Smart Inverter

The purpose of this thesis is to develop a reference design for a base level implementation of an intrusion detection module using artificial neural networks that is deployed onto an inverter and runs on live data for cybersecurity purposes, leveraging the latest deep learning algorithms and tools. Cybersecurity in the smart grid industry focuses on maintaining optimal standards of security in the system and a key component of this is being able to detect cyberattacks. Although researchers and engineers aim to design such devices with embedded security, attacks can and do still occur. The foundation for eventually mitigating these attacks and achieving more robust security is to identify them reliably. Thus, a high-fidelity intrusion detection system (IDS) capable of identifying a variety of attacks must be implemented. This thesis provides an implementation of a behavior-based intrusion detection system that uses a recurrent artificial neural network deployed on hardware to detect cyberattacks in real time. Leveraging the growing power of artificial intelligence, the strength of this approach is that given enough data, it is capable of learning to identify highly complex patterns in the data that may even go undetected by humans. By intelligently identifying malicious activity at the fundamental behavior level, the IDS remains robust against new methods of attack. This work details the process of collecting and simulating data, selecting the particular algorithm, training the neural network, deploying the neural network onto hardware, and then being able to easily update the deployed model with a newly trained one. The full system is designed with a focus on modularity, such that it can be easily adapted to perform well on different use cases, different hardware, and fulfill changing requirements. The neural network behavior-based IDS is found to be a very powerful method capable of learning highly complex patterns and identifying intrusion from different types of attacks using a single unified algorithm, achieving up to 98% detection accuracy in distinguishing between normal and anomalous behavior. Due to the ubiquitous nature of this approach, the pipeline developed here can be applied in the future to build in more and more sophisticated detection abilities depending on the desired use case. The intrusion detection module is implemented in an ARM processor that exists at the communication layer of the inverter. There are four main components described in this thesis that explain the process of deploying an artificial neural network intrusion detection algorithm onto the inverter: 1) monitoring and collecting data through a front-end web based graphical user interface that interacts with a Digital Signal Processor that is connected to power-electronics, 2) simulating various malicious datasets based on attack vectors that violate the Confidentiality-Integrity-Availability security model, 3) training and testing the neural network to ensure that it successfully identifies normal behavior and malicious behavior with a high degree of accuracy, and lastly 4) deploying the machine learning algorithm onto the hardware and having it successfully classify the behavior as normal or malicious with the data feeding into the model running in real time. The results from the experimental setup will be analyzed, a conclusion will be made based upon the work, and lastly discussions of future work and optimizations will be discussed

Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any update.Comment: 6 pages, TAAI 2017 versio

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Machine Learning DDoS Detection for Consumer Internet of Things Devices

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep Learning and Security (DLS '18