Biblio
Robots are networks of a variety of computing devices, such as powerful computing platforms but also tiny microcontrollers. The Robot Operating System (ROS) is the dominant framework for powerful computing devices. While ROS version 2 adds important features like quality of service and security, it cannot be directly applied to microcontrollers because of its large memory footprint. The micro-ROS project has ported the ROS 2 API to microcontrollers. However, the standard ROS 2 concepts are not enough for real-time performance: In the ROS 2 release “Foxy”, the standard ROS 2 Executor, which is the central component responsible for handling timers and incoming message data, is neither real-time capable nor deterministic. Domain-specific requirements of mobile robots, like sense-plan-act control loops, cannot be addressed with the standard ROS 2 Executor. In this paper, we present an advanced Executor for the ROS 2 C API which provides deterministic scheduling and supports domain-specific requirements. A proof-of-concept is demonstrated on a 32-bit microcontroller.
In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.
As Electric Power is one of the major concerns, so the concept of the automatic lighting and security system saves the electrical energy. By using the automatic lightning, the consumption of electrical power can be minimized to a greater extent and for that sensors and microcontrollers can be designed in such a manner such that lights get ON/OFF based on motion in a room. The various sensors used for sensing the motion in an area are PIR motion sensor, IR Motion Sensor. An IR sensor senses the heat of an object and detects its motion within some range as it emits infrared radiations and this complete process can be controlled by microcontroller. Along with that security system can be applied in this concept by programming the microcontroller in such a way that if there is some movement in an area then lights must get ON/OFF automatically or any alarm must start. This chapter proposes the framework for the smart lightning with security systems in a building so that electrical power can be utilized efficiently and secures the building.
The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.
This paper introduces a new methodology to generate additional hardware security in commercial off-the-shelf (COTS) system-on-a-chip (SoC) integrated circuits (ICs) that have already been fabricated and packaged. On-chip analog hardware blocks such as analog to digital converters (ADCs), digital to analog converters (DACs) and comparators residing within an SoC are repurposed and connected to one another to generate unique physically unclonable function (PUF) responses. The PUF responses are digitized and processed on-chip to create keys for use in encryption and device authentication activities. Key generation and processing algorithms are presented that minimize the effects of voltage and temperature fluctuations to maximize the repeatability of a key within a device. Experimental results utilizing multiple on-chip analog blocks inside a common COTS microcontroller show reliable key generation with minimal overhead.
Automobiles provide comfort and mobility to owners. While they make life more meaningful they also pose challenges and risks in their safety and security mechanisms. Some modern automobiles are equipped with anti-theft systems and enhanced safety measures to safeguard its drivers. But at times, these mechanisms for safety and secured operation of automobiles are insufficient due to various mechanisms used by intruders and car thieves to defeat them. Drunk drivers cause accidents on our roads and thus the need to safeguard the driver when he is intoxicated and render the car to be incapable of being driven. These issues merit an integrated approach to safety and security of automobiles. In the light of these challenges, an integrated microcontroller-based hardware and software system for safety and security of automobiles to be fixed into existing vehicle architecture, was designed, developed and deployed. The system submodules are: (1) Two-step ignition for automobiles, namely: (a) biometric ignition and (b) alcohol detection with engine control, (2) Global Positioning System (GPS) based vehicle tracking and (3) Multisensor-based fire detection using neuro-fuzzy logic. All submodules of the system were implemented using one microcontroller, the Arduino Mega 2560, as the central control unit. The microcontroller was programmed using C++11. The developed system performed quite well with the tests performed on it. Given the right conditions, the alcohol detection subsystem operated with a 92% efficiency. The biometric ignition subsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiency in locations registered with the neuro-fuzzy system. The vehicle tracking subsystem operated with an efficiency of 90%.
Distinguishing and classifying different types of malware is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. In this paper, a system for classifying malware programs is presented. The paper describes the architecture of the system and assesses its performance on a publicly available database (provided by Microsoft for the Microsoft Malware Classification Challenge BIG2015) to serve as a benchmark for future research efforts. First, the malicious programs are preprocessed such that they are visualized as gray scale images. We then make use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of those images/programs. We compare the performance of this approach against traditional machine learning and pattern recognition algorithms. Our experimental results show that the deep learning architecture yields a boost in performance over those conventional/standard algorithms. A hold-out validation analysis using the superior architecture shows an accuracy in the order of 99.15%.
As demonstrated recently, Wireless Physical Layer Security (WPLS) has the potential to offer substantial advantages for key management for small resource-constrained and, therefore, low-cost IoT-devices, e.g., the widely applied 8-bit MCU 8051. In this paper, we present a WPLS testbed implementation for independent performance and security evaluations. The testbed is based on off-the-shelf hardware and utilizes the IEEE 802.15.4 communication standard for key extraction and secret key rate estimation in real-time. The testbed can include generically multiple transceivers to simulate legitimate parties or eavesdropper. We believe with the testbed we provide a first step to make experimental-based WPLS research results comparable. As an example, we present evaluation results of several test cases we performed, while for further information we refer to https://pls.rub.de.
Tamper detection circuits provide the first and most important defensive wall in protecting electronic modules containing security data. A widely used procedure is to cover the entire module with a foil containing fine conductive mesh, which detects intrusion attempts. Detection circuits are further classified as passive or active. Passive circuits have the advantage of low power consumption, however they are unable to detect small variations in the conductive mesh parameters. Since modern tools provide an upper leverage over the passive method, the most efficient way to protect security modules is thus to use active circuits. The active tamper detection circuits are typically probing the conductive mesh with short pulses, analyzing its response in terms of delay and shape. The method proposed in this paper generates short pulses at one end of the mesh and analyzes the response at the other end. Apart from measuring pulse delay, the analysis includes a frequency domain characterization of the system, determining whether there has been an intrusion or not, by comparing it to a reference (un-tampered with) spectrum. The novelty of this design is the combined analysis, in time and frequency domains, of the small variations in mesh characteristic parameters.
Wearable medical devices are playing more and more important roles in healthcare. Unlike the wired connection, the wireless connection between wearable devices and the remote servers are exceptionally vulnerable to malicious attacks, and poses threats to the safety and privacy of the patient health data. Therefore, wearable medical devices require the implementation of reliable measures to secure the wireless network communication. However, those devices usually have limited computational power that is not comparable with the desktop computer and thus, it is difficult to adopt the full-fledged security algorithm in software. In this study, we have developed an efficient authentication and encryption protocol for internetconnected wearable devices using the recognized standards of AES and SHA that can provide two-way authentication between wearable device and remote server and protection of patient privacy against various network threats. We have tested the feasibility of this protocol on the TI CC3200 Launchpad, an evaluation board of the CC3200, which is a Wi-Fi capable microcontroller designed for wearable devices and includes a hardware accelerated cryptography module for the implementation of the encryption algorithm. The microcontroller serves as the wearable device client and a Linux computer serves as the server. The embedded client software was written in ANSI C and the server software was written in Python.
Identity-Based Encryption (IBE) was introduced as an elegant concept for secure data exchange due to its simplified key management by specifically addressing the asymmetric key distribution problems in multi-user scenarios. In the context of ad-hoc network connections that are of particular importance in the emerging Internet of Things, the simple key discovery procedures as provided by IBE are very beneficial in many situations. In this work we demonstrate for the first time that IBE has become practical even for a range of embedded devices that are populated with low-cost ARM Cortex-M microcontrollers or reconfigurable hardware components. More precisely, we adopt the IBE scheme proposed by Ducas et al. at ASIACRYPT 2014 based on the RLWE problem for which we provide implementation results for two security levels on the aforementioned embedded platforms. We give evidence that the implementations of the basic scheme are efficient, as for a security level of 80 bits it requires 103 ms and 36 ms for encryption and decryption, respectively, on the smallest ARM Cortex-M0 microcontroller.
We introduce $μ$DTNSec, the first fully-implemented security layer for Delay/Disruption-Tolerant Networks (DTN) on microcontrollers. It provides protection against eavesdropping and Man-in-the-Middle attacks that are especially easy in these networks. Following the Store-Carry-Forward principle of DTNs, an attacker can simply place itself on the route between source and destination. Our design consists of asymmetric encryption and signatures with Elliptic Curve Cryptography and hardware-backed symmetric encryption with the Advanced Encryption Standard. $μ$DTNSec has been fully implemented as an extension to $μ$DTN on Contiki OS and is based on the Bundle Protocol specification. Our performance evaluation shows that the choice of the curve (secp128r1, secp192r1, secp256r1) dominates the influence of the payload size. We also provide energy measurements for all operations to show the feasibility of our security layer on energy-constrained devices.
The Internet of Vehicles (IoV) is a complex and dynamic mobile network system that enables information sharing between vehicles, their surrounding sensors, and clouds. While IoV opens new opportunities in various applications and services to provide safety on the road, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed, decentralized, dynamic, and mobile infrastructures of the IoV. Forensic investigators will face challenges while identifying necessary pieces of evidence from the IoV environment, and collecting and analyzing the evidence. In this article, we propose TrustIoV - a digital forensic framework for the IoV systems that provides mechanisms to collect and store trustworthy evidence from the distributed infrastructure. Trust-IoV maintains a secure provenance of the evidence to ensure the integrity of the stored evidence and allows investigators to verify the integrity of the evidence during an investigation. Our experimental results on a simulated environment suggest that Trust-IoV can operate with minimal overhead while ensuring the trustworthiness of evidence in a strong adversarial scenario.