ScaRR: Scalable Runtime Remote Attestation for Complex Systems

The introduction of remote attestation (RA) schemes has allowed academia and industry to enhance the security of their systems. The commercial products currently available enable only the validation of static properties, such as applications fingerprint, and do not handle runtime properties, such as control-flow correctness. This limitation pushed researchers towards the identification of new approaches, called runtime RA. However, those mainly work on embedded devices, which share very few common features with complex systems, such as virtual machines in a cloud. A naive deployment of runtime RA schemes for embedded devices on complex systems faces scalability problems, such as the representation of complex control-flows or slow verification phase. In this work, we present ScaRR: the first Scalable Runtime Remote attestation schema for complex systems. Thanks to its novel control-flow model, ScaRR enables the deployment of runtime RA on any application regardless of its complexity, by also achieving good performance. We implemented ScaRR and tested it on the benchmark suite SPEC CPU 2017. We show that ScaRR can validate on average 2M control-flow events per second, definitely outperforming existing solutions.Comment: 14 page

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

RSSI Based Indoor Passive Localization for Intrusion Detection and Tracking

A real time system for intrusion detection and tracking based on wireless sensor network technology is designed by using the IITH mote which is de- veloped and designed in IIT Hyderabad as the communication module in the network.This paper describes the Device-Free Passive Localization system based on RSSI.The main objective of this paper is to design a DFP Local- ization system that is easily redeployable, recon�gurable, easy to use, and operates in real time. In addition the detection of humans is to be done.The em- bedded intrusion detection algorithm is designed so that it is able to cope with the limited resources, in terms of computational power and available memory space, of the microcontroller unit (MCU) found in the nodes. and various challenges and problem faced during the real test bed deployment and also proposed solutions to overcome them.We presented an alternative algo- rithm based on the minimum Euclidean distance classi�er.our result shows that the localization accuracy of this system is increased when using the proposed algorith

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Autoscopy: Detecting Pattern-Searching Rootkits via Control Flow Tracing

Traditional approaches to rootkit detection assume the execution of code at a privilege level below that of the operating system kernel, with the use of virtual machine technologies to enable the detection system itself to be immune from the virus or rootkit code. In this thesis, we approach the problem of rootkit detection from the standpoint of tracing and instrumentation techniques, which work from within the kernel and also modify the kernel\u27s run-time state to detect aberrant control flows. We wish to investigate the role of emerging tracing frameworks (Kprobes, DTrace etc.) in enforcing operating system security without the reliance on a full-blown virtual machine just for the purposes of such policing. We first build a novel rootkit prototype that uses pattern-searching techniques to hijack hooks embedded in dynamically allocated memory, which we present as a showcase of emerging attack techniques. We then build an intrusion detection system-- autoscopy, atop kprobes, that detects anomalous control flow patterns typically exhibited by rootkits within a running kernel. Furthermore, to validate our approach, we show that we were able to successfully detect 15 existing Linux rootkits. We also conduct performance analyses, which show the overhead of our system to range from 2% to 5% on a wide range of standard benchmarks. Thus by leveraging tracing frameworks within operating systems, we show that it is possible to introduce real-world security in devices where performance and resource constraints are tantamount to security considerations

Detecting Safety and Security Faults in PLC Systems with Data Provenance

Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies

Trustworthy Wireless Personal Area Networks

In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems. First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX. Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions