Detecting Rogue Manipulation of Smart Home Device Settings

Smart home devices control a home’s environmental and security settings. This includes devices that control home thermostats, sprinkler systems, light bulbs, and home appliances. Malicious manipulation of the settings of these devices by an outside adversary has caused emotional distress and could even cause physical harm. For example, researchers have reported that there is a rise in domestic abuse perpetrated via smart home devices; victims have reported their thermostat settings being unwittingly manipulated and being locked out of their house due to their smart lock code being changed. Rapid adoption of smart home devices by consumers has led to an urgent need to research mitigation strategies to protect consumers from device takeover. Currently there is not an easy way for home users to detect that a malicious actor is making unwanted changes to their smart home devices. Change requests to smart home devices travel across the network in the form of network packets. Most of time the payloads of the packets are encrypted using strong encryption methods, so it is not possible to simply read the contents of the packet to learn if the packet contains instructions for the smart device to change states. Previous research has successfully trained machine learning algorithms to identify unique network traffic patterns indicative of state change requests sent to smart home devices. This research extends previous research by identifying state change requests of smart home devices made by residents via a smart home device app on their smart phones or tablets. This research identified 13 key attributes of 3,178 encrypted network traffic connections. The attributes were used as features to train three machine learning algorithms to recognize state change requests. Four smart home devices were used chosen from the following categories: 1) devices with simple behaviors (turns on and off), 2) devices with complex behaviors (can be turned on for a set amount of time), and 3) devices that send a large amount of data (i.e. video camera). The success of identifying state change requests over encrypted traffic from a mobile app, combined with previous research that identified state changes sent to the smart home device, allows for the development of a system that could block unwanted state changes that originate from a malicious user located outside of the house. Therefore, this research contributes to the body of knowledge of smart home device security and could be extended to the identification of other networking patterns based on encrypted traffic

TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT

The Internet of Things (IoT) is an emerging technology, which comprises wireless smart sensors and actuators. Nowadays, IoT is implemented in different areas such as Smart Homes, Smart Cities, Smart Industries, Military, eHealth, and several real-world applications by connecting domain-specific sensors. Designing a security model for these applications is challenging for researchers since attacks (for example, zero-day) are increasing tremendously. Several security methods have been developed to ensure the CIA (Confidentiality, Integrity, and Availability) for Industrial IoT (IIoT). Though these methods have shown promising results, there are still some security issues that are open. Thus, the security and authentication of IoT based applications become quite significant. In this paper, we propose TD2SecIoT (Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT), which incorporates Elliptic Curve Cryptography (ECC) and Nth-degree Truncated Polynomial Ring Units (NTRU) methods to ensure confidentiality and integrity. The proposed method has been evaluated against different attacks and performance measures (quantitative and qualitative) using the Cooja network simulator with Contiki-OS. The TD2SecIoT has shown a higher security level with reduced computational cost and time

Securing IoT-based collaborative applications using a new compressed and distributed MIKEY mode

International audienceMultimedia internet keying protocol (MIKEY) aims at establishing secure credentials between two communicating entities. However, existing MIKEY modes fail to meet the requirements of low-power and low-processing devices. To address this issue, we combine two previously proposed approaches to introduce a new compressed and distributed MIKEY mode applied to a collaborative internet of things context. A set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the MIKEY pre-shared mode is used in the constrained part of network, while the public key mode is used in the unconstrained part of the network. Furthermore, to mitigate the communication cost we introduce a new header compression scheme that reduces the size of MIKEY's header from 12 bytes to 3 bytes in the best compression case. To assess our approach, we performed a detailed security analysis using a formal validation tool (i.e., Avispa). In addition, we performed an energy evaluation of both communicational and computational costs. The obtained results show that our proposed mode is energy preserving whereas its security properties are preserved untouched

Analysing the Design of Privacy-Preserving Data-Sharing Architecture

Privacy has become an essential software quality to consider in a software system. Privacy practices should be adopted from the early stages of the system design to safeguard personal data from privacy violations. Privacy patterns are proposed in industry and academia as reusable design solutions to address different privacy issues. However, the diverse types and granularity of the patterns lead to difficulty for the practitioner to select and adopt them in the architecture. First, the fragmented information about the system actors in the patterns does not align with the regulatory entities and interactions between them. Second, these privacy patterns lack architectural perspectives that could help weave patterns into concrete software designs. Third, the consequences of applying the patterns have not covered the impacts on software quality attributes. This thesis aims to provide guidance to software architects and practitioners for considering and applying privacy patterns in their design, by adding new perspectives to the existing patterns. First, the research provides an analysis of the relationships between regulatory entities and their responsibility in adopting the patterns in a software design. Then, the research reports studies that were conducted using architectural-level modelling-based approaches, to analyse the architectural views of privacy patterns. The analyses aim to improve understanding of how privacy patterns are applied in software designs and how such a design affects software quality attributes, including privacy, performance, and modifiability. Finally, in an effort to harmonise and unite the extended view of privacy patterns that have a close relation to system architecture, this research proposes an enhanced pattern catalogue and a systematic privacy-by-design (PbD) pattern-selection model that aims to aid and guide software architects in pattern selection during software design. The enhanced pattern catalogue offers consolidated information on the extended view of privacy patterns. The selection model provides a structured way for the practitioner to know when and how to use the pattern catalogue in the system-design process. Two industry case studies are used to evaluate the proposed pattern catalogue and selection model. The findings demonstrate how the proposed frameworks are applicable to different types of data-sharing software systems and their usability in supporting pattern selection decisions in the privacy design

Storing IOT Data Securely in a Private Ethereum Blockchain

Internet of Things (IoT) is a set of technologies that enable network-connected devices to perform an action or share data among several connected devices or to a shared database. The actions can be anything from switching on an Air Conditioning device remotely to turning on the ignition of a car through a command issued from a remote location or asking Alexa or Google Assistant to search for weather conditions in an area. IoT has proved to be game-changing for many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a huge amount of data created by a lot of these devices all of which need to be processed in real time. In this thesis, we propose a method to collect sensor data from IoT devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion within a closed system, suitable for a single enterprise or a group of companies in industries like shipping where sharing data with each other is required. Much like blockchain, we envision a future where IoT devices can connect and disconnect to distributed systems without causing downtime for the data collection or storage or relying on a cloud-based storage system for synchronizing data between devices. We also look at how the performance of some of these distributed systems like Inter Planetary File System (IPFS) and Ethereum Swarm compare on low-powered devices like the raspberry pi

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems