Biblio

Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.

With the growing use of the Robot Operating System (ROS), it can be argued that it has become a de-facto framework for developing robotic solutions. ROS is used to build robotic applications for industrial automation, home automation, medical and even automatic robotic surveillance. However, whenever ROS is utilized, security is one of the main concerns that needs to be addressed in order to ensure a secure network communication of robots. Cyber-attacks may hinder evolution and adaptation of most ROS-enabled robotic systems for real-world use over the Internet. Thus, it is important to address and prevent security threats associated with the use of ROS-enabled applications. In this paper, we propose a novel approach for securing ROS-enabled robotic system by integrating ROS with the Message Queuing Telemetry Transport (MQTT) protocol. We manage to secure robots' network communications by providing authentication and data encryption, therefore preventing man-in-the-middle and hijacking attacks. We also perform real-world experiments to assess how the performance of a ROS-enabled robotic surveillance system is affected by the proposed approach.

Smart buildings are controlled by multiple cyber-physical systems that provide critical services such as heating, ventilation, lighting and access control. These building systems are becoming increasingly vulnerable to both cyber and physical attacks. We introduce a multi-model methodology for assessing the security of these systems, which utilises INTO-CPS, a suite of modelling, simulation, and analysis tools for designing cyber-physical systems. Using a fan coil unit case study we show how its security can be systematically assessed when subjected to Man-in-the-Middle attacks on the data connections between system components. We suggest our methodology would enable building managers and security engineers to design attack countermeasures and refine their effectiveness.

Great numbers of embedded devices are performing safety critical operations, which means it is very important to keep them operating without interference. Update is the weak point that could be exploited by potential attackers to gain access to the system, sabotage it or to simply steal someone else's intellectual property. This paper presents an implementation of secure update process for embedded systems which prevents man-in-the-middle attacks. By using a combination of hash functions, symmetric and asymmetric encryption algorithms it demonstrates how to achieve integrity, authenticity and confidentiality of the update package that is sent to the target hardware. It covers implementation starting from key exchange, next explaining update package encryption process and then decryption on the target hardware. It does not go into a detail about specific encryption algorithms that could be used. It presents a generalized model for secure update that could be adjusted to specific needs.

In this paper, we address the fundamental problem of securely bootstrapping a group of wireless devices to a hub, when none of the devices share prior associations (secrets) with the hub or between them. This scenario aligns with the secure deployment of body area networks, IoT, medical devices, industrial automation sensors, autonomous vehicles, and others. We develop VERSE, a physical-layer group message integrity verification primitive that effectively detects advanced wireless signal manipulations that can be used to launch man-in-the-middle (MitM) attacks over wireless. Without using shared secrets to establish authenticated channels, such attacks are notoriously difficult to thwart and can undermine the authentication and key establishment processes. VERSE exploits the existence of multiple devices to verify the integrity of the messages exchanged within the group. We then use VERSE to build a bootstrapping protocol, which securely introduces new devices to the network. Compared to the state-of-the-art, VERSE achieves in-band message integrity verification during secure pairing using only the RF modality without relying on out-of-band channels or extensive human involvement. It guarantees security even when the adversary is capable of fully controlling the wireless channel by annihilating and injecting wireless signals. We study the limits of such advanced wireless attacks and prove that the introduction of multiple legitimate devices can be leveraged to increase the security of the pairing process. We validate our claims via theoretical analysis and extensive experimentations on the USRP platform. We further discuss various implementation aspects such as the effect of time synchronization between devices and the effects of multipath and interference. Note that the elimination of shared secrets, default passwords, and public key infrastructures effectively addresses the related key management challenges when these are considered at scale.

Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.

Wearable devices are being more popular in our daily life. Especially, smart wristbands are booming in the market recently, which can be used to monitor health status, track fitness data, or even do medical tests, etc. For this reason, smart wristbands can obtain a lot of personal data. Hence, users and manufacturers should pay more attention to the security aspects of smart wristbands. However, we have found that some Bluetooth Low Energy based smart wristbands have very weak or even no security protection mechanism, therefore, they are vulnerable to replay attacks, man-in-the-middle attacks, brute-force attacks, Denial of Service (DoS) attacks, etc. We have investigated four different popular smart wristbands and a smart watch. Among them, only the smart watch is protected by some security mechanisms while the other four smart wristbands are not protected. In our experiments, we have also figured out all the message formats of the controlling commands of these smart wristbands and developed an Android software application as a testing tool. Powered by the resolved command formats, this tool can directly control these wristbands, and any other wristbands of these four models, without using the official supporting applications.

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.

This paper describes biometric-based cryptographic techniques for providing confidential communications and strong, mutual and multifactor authentication on the Internet of Things. The described security techniques support the goals of universal access when users are allowed to select from multiple choice alternatives to authenticate their identities. By using a Biometric Authenticated Key Exchange (BAKE) protocol, user credentials are protected against phishing and Man-in-the-Middle attacks. Forward secrecy is achieved using a Diffie-Hellman key establishment scheme with fresh random values each time the BAKE protocol is operated. Confidentiality is achieved using lightweight cryptographic algorithms that are well suited for implementation in resource constrained environments, those limited by processing speed, limited memory and power availability. Lightweight cryptography can offer strong confidentiality solutions that are practical to implement in Internet of Things systems, where efficient execution, and small memory requirements and code size are required.

There has been a rampant surge in compromise of consumer grade small scale routers in the last couple of years. Attackers are able to manipulate the Domain Name Space (DNS) settings of these devices hence making them capable of initiating different man-in-the-middle attacks. By this study we aim to explore and comprehend the current state of these attacks. Focusing on the Indian Autonomous System Number (ASN) space, we performed scans over 3 months to successfully find vulnerable routers and extracted the DNS information from these vulnerable routers. In this paper we present the methodology followed for scanning, a detailed analysis report of the information we were able to collect and an insight into the current trends in the attack patterns. We conclude by proposing recommendations for mitigating these attacks.