Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

There is an increasing demand for processing large volumes of unstructured data for a wide variety of applications. However, protection measures for these big data sets are still in their infancy, which could lead to significant security and privacy issues. Attribute-based access control (ABAC) provides a dynamic and flexible solution that is effective for mediating access. We analyzed and implemented a prototype application of ABAC to large dataset processing in Amazon Web Services, using open-source versions of Apache Hadoop, Ranger, and Atlas. The Hadoop ecosystem is one of the most popular frameworks for large dataset processing and storage and is adopted by major cloud service providers. We conducted a rigorous analysis of cybersecurity in implementing ABAC policies in Hadoop, including developing a synthetic dataset of information at multiple sensitivity levels that realistically represents healthcare and connected social media data. We then developed Apache Spark programs that extract, connect, and transform data in a manner representative of a realistic use case. Our result is a framework for securing big data. Applying this framework ensures that serious cybersecurity concerns are addressed. We provide details of our analysis and experimentation code in a GitHub repository for further research by the community.

Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding led us to develop a hypothesis that most classical machine learning models, such as random forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and, at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on the CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems.

Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension designed specifically for MCUs, is an emerging hardware security technique fortifying software security of MCU-based IoT devices. This article introduces a comprehensive security framework for IoT devices using TrustZone-M-enabled MCUs, in which device security is protected in five dimensions, i.e., hardware, boot-time software, runtime software, network, and over-the-air (OTA) update. Along developing the framework, we also present the first security analysis of potential runtime software security issues in TrustZone-M-enabled MCUs. In particular, we explore the feasibility of launching stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against nonsecure callable (NSC) functions in the context of TrustZone-M. We validate these attacks using SAM L11, a microchip MCU with TrustZone-M and provide defense mechanisms in the runtime software dimension of the proposed framework. The security framework is implemented with a full-fledged secure and trustworthy air quality monitoring device using SAM L11 as its MCU.

Blockchain technology has heralded a new era in digital innovation, revolutionizing our approach to designing and building distributed applications in the digital sphere. Blockchain technology operates as an immutable digital ledger, where each entry representing a digital transaction is indelible and cannot be altered once established. Initially designed as the fundamental framework for cryptocurrencies, blockchain has outgrown its original purpose, demonstrating significant potential in various industries and offering a variety of security and privacy features. Our study provides a thorough and current survey of blockchain applications, security, privacy concepts, primitives, and threat models. It stands out by concentrating on how blockchain technology intersects with emerging fields like IoT, EVs, FinTech, and healthcare systems in a single framework. To provide security and privacy features, blockchain systems employ different foundational notions and primitives while tackling diverse adversarial scenarios with various capabilities and goals. This study presents a fresh examination of the current state of applications, security and privacy notions and primitives, and threat models in blockchain systems. Additionally, this work highlights existing gaps in knowledge and outlines open questions, aiming to stimulate interest in further advancements in the field.

In today’s cyber world, worms pose a great threat to the global network infrastructure. In this paper, we propose a worm detection system based on deep learning. It includes two main modules: one worm detection module based on a convolutional neural network (CNN) and one automatic worm signature generation module based on a deep neural network (DNN). In the CNN-based worm detection module, we propose three kinds of data preprocessing methods: frequency processing, frequency weighted processing, and difference processing, and use CNN to train the model for worm detection. In the DNN-based worm signature generation module, there are two phrase:DNN is firstly utilized for training the model with worm payloads and their corresponding signatures as input in the training phrase. After worm payloads are fed into the trained DNN model in the test phrase, worm signatures are generated by our proposed Signature Beam Search algorithm. In the experiment, we firstly analyzed the impact of different data preprocessing methods and the number of convolution-pooling layers in the CNN model on the worm detection performance. Then we analyzed the effects of different signatures in the DNN algorithm on the automatic generation of worm signatures. Experiments show that the generated signatures have a good detection performance.