Digital Forensics Investigation for Attacks on Artificial Intelligence

Digital forensics is a process of collection, identification, extraction, and documentation of electronic evidence which is used in a court of law. There are a large number of tools that help us to make this process easy and simple. In this paper, four tools have been chosen to explore and study. The best digital forensic tools have been chosen according to different parameters in each type of digital forensics. The (Stellar) basis tool and (Forensic Tool Kit) have been explored for computer forensic tools while (Network Map) has been chosen for network forensic tools and (OSFmount) has been studied as a live forensic tool. This paper also covers other types of forensic tools like Database forensic tools, O.S. forensic tools, and Mail forensic tools. The role of Artificial intelligence in Digital Forensic tools has been discussed in this paper by using both Decision Stump and Bayes net machine learning techniques. After making an investigation of the IoT device traffic dataset using these two techniques, Decision Stump gives us less accurate results compared with Bayes net.

Digital forensics (DF) tools are used for post-mortem investigation of cyber-crimes. CFTT (Computer ForensicsTool Testing) Program at National Institute of Standards and Technology (NIST) has defined expectations for aDF tool’s behavior. Understanding these expectations and how DF tools work is critical for ensuring integrityof the forensic analysis results. In this paper, we consider standardization of one class of DF tools which arefor Deleted File Recovery (DFR). We design a list of canonical test file system images to evaluate a DFR tool.Via extensive experiments we find that many popular DFR tools do not satisfy some of the standards, and wecompile a comparative analysis of these tools, which could help the user choose the right tool. Furthermore,one of our research questions identifies the factors which make a DFR tool fail. Moreover, we also providecritique on applicability of the standards. Our findings is likely to trigger more research on compliance ofstandards from the researcher community as well as the practitioners.

This article explores the application of Generative Adversarial Networks (GANs) in improving subsurface rock analysis. GANs generate realistic images of rock features, enhancing the accuracy and efficiency of estimating rock properties. The objective is to advance the field of subsurface rock analysis, offering insights into the potential of GAN techniques. The article reviews recent developments in data-driven rock reconstruction technology, specifically focusing on GANs. It highlights GANs’ ability to generate and estimate rock features like porosity and permeability. These GAN methods are complemented by discriminators to select or reject features, making them integral to the reconstruction model. The article provides a framework for engineers and researchers to effectively utilize these techniques for rock reconstruction, offering an assessment of their strengths and limitations. Digital rock reconstruction, aided by GANs, has promising implications for site selection and recovery. GANs enhance image resolution, improving the field of view and dynamism of parameters in rock analysis. However, GANs heavily rely on high-quality data, which poses a limitation. To address this, conditional GANs have been proposed. The article comprehensively reviews the latest developments in GAN methods for digital rock reconstruction, offering valuable insights for engineers and researchers using GANs for accurate subsurface rock analysis. It also proposes ways to enhance these methods and advance the field. The novelty of this article lies in its exploration of Generative Adversarial Networks (GANs) in the context of subsurface rock analysis. It emphasizes the potential of GAN techniques to generate realistic rock images and estimate properties like porosity and permeability. Additionally, the article discusses the integration of GANs into the digital rock concept, highlighting their role in improving predictive models and supporting decision-making in the oil and gas industry's resource extraction and production strategies.

Generative adversarial network (GAN) has been extensively applied in many fields since it was first proposed in 2014. There have been ongoing attempts at improving the model and developing its extensions. The basic idea behind GAN stems from the two-player zero-sum game. Composed of a generator and a discriminator, GAN is trained through a “battle” between the two networks, where the generator tries to fool the discriminator. In contrast, the discriminator tries not to be deceived. The purpose is to estimate the potential distribution of data samples and generate new data samples. GAN is currently being studied in computer vision, language processing, and information security and is still far from reaching its potential. Since there are limited existing resources on GAN reviews, this paper provides a comprehensive review of a few of the most important GAN variants. We divided the variants into three categories: foundation, extension, and advanced, gave an overview of each architecture, and analyzed their advantages and disadvantages. We also summarized some of the most popular applications of GAN, and finally, we brought up the problems of GAN-based architectures that have yet to be solved and pointed out the future direction of GAN research and development.

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With the rapid development of deep learning technology, the methods based on big data-driven provide a new perspective for the fault diagnosis of machinery. However, mechanical equipment operates in the normal condition most of the time, resulting in the collected data being imbalanced, which affects the performance of mechanical fault diagnosis. As a new approach for generating data, generative adversarial network (GAN) can effectively address the issues of limited data and imbalanced data in practical engineering applications. This paper provides a comprehensive review of GAN for mechanical fault diagnosis. Firstly, the development of GAN-based mechanical fault diagnosis, the basic theory of GAN and various GAN variants (GANs) are briefly introduced. Subsequently, GANs are summarized and categorized from the perspective of labels and models, and the corresponding applications are outlined. Lastly, the limitations of current research, future challenges, future trends and selecting the GAN in the practical application are discussed.

As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.

ABSTRACTData mining and natural language processing (NLP) are fundamental fields that interact in many ways. Text mining shares many topics, such as sentiment analysis and content understanding. Combining these two fields enables more efficient mining of text data and the extraction of valuable information. In particular, the GAN (Generative Adversarial Network) architecture has achieved success in image generation and has started to be used on text data. However, training GANs is fraught with difficulties due to the complexity of text data. Linguistic studies show important differences between languages. Language is characterized by fluidity, ambiguity, and context‐sensitive interpretations, and text‐generating GAN models can struggle to deal with these complexities. The interaction between data quality, language structure, and complex interpretation can lead to inconsistency and ambiguity in the text production of GAN models. These problems are particularly pronounced when complexities such as semantic subtleties, idiomatic expressions, and context‐dependent usages come into play. Text generation is an area of GAN models used in NLP to generate language and enrich text‐based applications. Work in this area can contribute to analyzing, classifying, and processing text data. Many methods and techniques have been proposed to improve the performance of text GANs. However, some problems may be encountered in the optimization of these methods. Therefore, it is essential to use optimized methods. In conclusion, GANs can be an important tool to improve text generation in NLP. Still, they require continuous research and innovation to deal with factors such as language complexity and data quality.

In recent years, researchers have introduced various methods in many domains into medical image processing so that its effectiveness and efficiency can be improved to some extent. The applications of generative adversarial networks (GAN) in medical image processing are evolving very fast. In this paper, the state of the art in this area has been reviewed. Firstly, the basic concepts of the GAN were introduced. And then, from the perspectives of the medical image denoising, detection, segmentation, synthesis, reconstruction and classification, the applications of the GAN were summarized. Finally, prospects for further research in this area were presented.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

During the development of a new specialist graduate professional study program at Zagreb University of Applied Science “Information Security and Digital Forensics”, a number of challenges was addressed. The key challenges were: a) Defining the curriculum and b) Defining and building computer infrastructure for conducting laboratory exercises. We will address second: defining the necessary equipment for conducting laboratory exercises. New possibilities are given by new technologies, and it would be possible to expand the working environment which, apart from laboratory exercises, will enable independent student work. We will describe and define setting of the environment that can also be used for independent research in the field of information security and digital forensics for expert and scientific community. We will use TVZ private cloud as a place to build digital forensic laboratory, maintained by digital forensic management process tool, where different virtual machines with different digital forensic software will be accessible for students and independent researches.

Digital investigation is becoming an increasing concern. Many digital forensic tools are being developed to deal with the challenge of investigating digital crimes. Acquisition of volatile memory is one of the vital steps of digital forensics process. Passwords data, indications of digital forensics methods, memory malware may be contained in volatile data which may overlooked by the investigator. The Success of memory acquisition mainly depends on the effectiveness of the memory acquisition tool. This paper compares memory forensics tools based on processing time and left artifacts on volatile memory. Furthermore, we examined how the processing time of the tools varies in terms of different volatile memory size. In order to conduct this work, we use the following tools: FTK Imager, Pro Discover, Nigilant32, Helix3(dd), OSForensics and Belkasoft RAM Capturer. The results show that Belkasoft RAM Capturer has the least amount of left artifacts, and it has also the lowest processing time. Moreover, this work concludes that tested tools are significantly different based on left artifacts on the volatile memory with 95% confidence level. Also, statistically, increasing the memory size × times does not increase the processing time × times of the tools.

Smart grids are crucial for meeting rising energy demands driven by global population growth and urbanization. By integrating renewable energy sources, they enhance efficiency, reliability, and sustainability. However, ensuring their availability and security requires advanced operational control and safety measures. Although artificial intelligence and machine learning can help assess grid stability, challenges such as data scarcity and cybersecurity threats, particularly adversarial attacks, remain. Data scarcity is a major issue, as obtaining real-world instances of grid instability requires significant expertise, resources, and time. Yet, these instances are critical for testing new research advancements and security mitigations. This paper introduces a novel framework for detecting instability in smart grids using only stable data. It employs a Generative Adversarial Network (GAN) where the generator is designed not to produce near-realistic data but instead to generate Out-Of-Distribution (OOD) samples with respect to the stable class. These OOD samples represent unstable behavior, anomalies, or disturbances that deviate from the stable data distribution. By training exclusively on stable data and exposing the discriminator to OOD samples, our framework learns a robust decision boundary to distinguish stable conditions from any unstable behavior, without requiring unstable data during training. Furthermore, we incorporate an adversarial training layer to enhance resilience against attacks. Evaluated on a real-world dataset, our solution achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks. Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms. • Tackles smart grid instability detection while addressing data scarcity, which makes it difficult to obtain real-world instability instances for model training. • Introduced a novel GAN-based framework that detects grid instability using only stable data, addressing data scarcity. • The generator, unlike the vanilla GAN, produces out-of-distribution (OOD) samples relative to the stable class to train the discriminator. • Added an adversarial training layer to the GAN framework to classify adversarial attacks as instances of instability. • Successfully deployed the model on a single-board computer, demonstrating efficient real-time performance with an average response time of under 7ms.

This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.