ARCADIS: Asynchronous Remote Control-Flow Attestation of Distributed IoT Services

Remote Attestation (RA) has gained a broad attention over recent years as an essential security mechanism that enables integrity verification of remote IoT devices. Typically, existing RA protocols aim at detecting malware presence in program memory. Recent RA schemes work towards attesting also data memory and focus mainly on detecting runtime attacks that manipulate stack pointers to hijack the execution flow of a running program. Despite different RA approaches, some data memory attacks still remain undetected. This paper proposes ERAMO, a novel RA protocol that investigates memory offloading technique in attesting broad memory regions of IoT devices. Instead of running a complex RA protocol on a resource-constrained IoT device, ERAMO leverages the emerging paradigm of Fog Computing to securely offload memory contents of IoT devices to nearby powerful devices. This approach aims at increasing the effectiveness of RA protocols by attesting larger data memory regions and allowing powerful devices to perform complex analysis of IoT devices’ state. We validate and evaluate ERAMO with a hardware proof-of-concept implementation using an ARM Cortex-M33 based microcontroller that provides ARM TrustZone to support secure isolation of the RA procedure. The conducted experiments confirm the feasibility of ERAMO and demonstrate that offloading technique increases the RA effectiveness in attesting dynamic memory regions.

Remote attestation has emerged as a valuable security mechanism which aims to verify remotely whether or not a potentially untrusted device has been compromised. The protocols of Remote attestation are particularly important for securing Internet of Things (IoT) systems which, due to the large number of interconnected devices and limited security protections, are susceptible to a wide variety of cyber attacks. To guarantee the integrity of a software running on a single device, remote attestation is usually executed as an uninterrupted procedure: at the attestation time, a device stops the normal operation and executes the attestation of the entire device without interruption. The remote attestation protocols that aim to attest a large number of devices also follow the assumption on uninterrupted execution: when a device attests its network neighbours, each device verified in the neighborhood suspends its normal operation until the attestation protocol is completed. To avoid unnecessary suspension of the normal operation of the devices, this paper proposes a novel Secure Asynchronous Remote Attestation (SARA) protocol that releases the constraint of synchronous interaction among devices. In particular, SARA is an attestation protocol that exploits asynchronous communication capabilities among IoT devices in order to attest a distributed IoT service executed by them. SARA verifies both that each IoT device is not compromised (device trustworthiness), and that the exchanged communication data have not maliciously influence the communicating devices (legitimate operations). By tracing the execution order of each service invocation of an asynchronous distributed service, SARA allows each service to collect accurately historical data of its interactions, and transmits asynchronously such historical data to other interacting services. We have implemented and validated SARA through a realistic simulation on the Contiki emulator that demonstrates the functionality and efficiency of our protocol. The results confirm the suitability of SARA for low-end devices.

Internet of Things (IoT) technology promises to bring new value creation opportunities across all major industrial sectors. This will yield industries to deploy more devices into their networks. A key pillar to ensure the safety and security of the running services on these devices is remote attestation. Unfortunately,existing solutions fail to cope with the recent challenges raised by large IoT networks. In particular, the heterogeneity of the devices used in the network affects the performance of a remote attestation protocol. Another challenge in these networks is their dynamic nature: More IoT devices may be added gradually over time. This poses a problem in terms of key management in remote attestation. We propose FADIA, the first lightweight collaborative remote attestation protocol that is designed with fairness in mind. FADIA enables fair distribution of load/tasks on the attesting devices to achieve better performance. We also leverage the Eschenauer-Gligor scheme to enable efficient addition of devices to the network. We implement our solution on heterogeneous embedded devices and evaluate it in real scenarios. The evaluation shows that FADIA can (i) increase the lifetime of a network by an order of magnitude and (ii) decrease the remote attestation runtime by a factor of 1.6.

Smart factories, critical infrastructures, and medical devices largely rely on embedded systems that need to satisfy realtime constraints to complete crucial tasks. Recent studies and reports have revealed that many of these devices suffer from crucial vulnerabilities that can be exploited with fatal consequences. Despite the security and safety-critical role of these devices, they often do not feature state-of-the-art security mechanisms. Moreover, since realtime systems have strict timing requirements, integrating new security mechanisms is not a viable option as they often influence the device's runtime behavior. One solution is to offload security enhancements to a remote instance, the so-called remote attestation.We present RealSWATT, the first software-based remote attestation system for realtime embedded devices. Remote attestation is a powerful security service that allows a party to verify the correct functionality of an untrusted remote device. In contrast to previous remote attestation approaches for realtime systems, RealSWATT does neither require custom hardware extensions nor trusted computing components. It is designed to work within real-world IoT networks, connected through Wi-Fi. RealSWATT leverages a dedicated processor core for remote attestation and provides the required timing guarantees without hardware extensions. We implement RealSWATT on the popular ESP32 microcontroller, and we evaluate it on a real-world medical device with realtime constraints. To demonstrate its applicability, we furthermore integrate RealSWATT into a framework for off-the-shelf IoT devices and apply it to a smart plug, a smoke detector, and a smart light bulb.},
\nbooktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Remote attestation is a security technique through which a remote trusted party (i.e., Verifier) checks the trust-worthiness of a potentially untrusted device (i.e., Prover). In the Internet of Things (IoT) systems, the existing remote attestation protocols propose various approaches to detect the modified software and physical tampering attacks. However, in an inter-operable IoT system, in which IoT devices interact autonomously among themselves, an additional problem arises: a compromised IoT service can influence the genuine operation of other invoked service, without changing the software of the latter. In this paper, we propose a protocol for Remote Attestation of Distributed IoT Services (RADIS), which verifies the trust-worthiness of distributed IoT services. Instead of attesting the complete memory content of the entire interoperable IoT devices, RADIS attests only the services involved in performing a certain functionality. RADIS relies on a control-flow attestation technique to detect IoT services that perform an unexpected operation due to their interactions with a malicious remote service. Our experiments show the effectiveness of our protocol in validating the integrity status of a distributed IoT service.

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

Cognitive radio (CR) is considered as a key enabling communication technology that offers efficient wireless connectivity to Internet of Things (IoT) devices. Its integration with the future 5G architecture is expected to advance the IoT paradigm. Security attacks can severely degrade the performance of such CR‐based IoT (CRIoT) networks. The effect of security attacks can be reduced by implementing defensive approaches. However, such solutions come at the expense of degrading spectrum efficiency and consuming more network resources. In this paper, we investigate the spectrum sharing and access problem in a multidevice single‐transceiver CRIoT network with time‐critical applications under jamming attacks. Our main goal is to maximize the number of simultaneously served IoT devices over all available idle channels while ensuring delay requirement, hardware, link quality, security attacks, and spectrum utilization constraints. This problem is formulated as a total unimodular binary linear programming, which is shown to be solvable in polynomial time. Unlike most of previous security‐aware channel assignment solutions that conduct the channel assignment sequentially, our solution simultaneously provides secured distributed channel‐assignment decisions for multiple CRIoT links (batching method). Batching can be realized through an admission control stage for CR IoT devices to announce their control packets. Simulation results reveal that our solution significantly improves network performance compared to previous security‐aware schemes.

With the recent technological advances in the design of IoT devices and their widespread use, it has become increasingly common for alarm services that provide security for facilities to use IoT devices. However, the conventional centralized management of such IoT devices involves risks, such as data falsification that is vulnerable to cyber-attacks, as well as problems pertaining to the data volume, integrity, and reliability of the IoT devices themselves. Therefore, managing the logs of a large number of IoT devices is difficult. For this reason, blockchain is often used in forensics and other applications. However, to the best of our knowledge, there has been no research to-date that can automatically determine the actual level of anomalies from the logs of IoT devices using smart contracts. In this study, we propose a method to securely record the logs of IoT devices in a blockchain and use those logs to automatically detect anomalies. We also propose a method to estimate the degree of anomalies based on the logs using smart contracts and automate the sequence of events performed by the security companies.

The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

Background. With the proliferation of Internet of Things (IoT) networks in the digital age, the risk of cyberattacks, especially DDoS attacks, is also increasing. IoT devices such as smart refrigerators, thermostats, webcams and other Internet-connected home appliances are being targeted by attackers who can use them as part of a botnet to conduct DDoS attacks. These devices often have inadequate network security and are rarely updated, making them vulnerable. DDoS attacks can result in significant losses such as lost revenue, reputational damage and costs to restore services. So, the vulnerability of IoT networks to DDoS attacks and the need to develop effective protection measures is a pressing issue. Objective. The purpose of the paper is to analyse software that supports DDoS attacks in IoT networks. Provide general recommendations to help improve approaches to defence measures in IoT networks against DDoS-enabled malware. Methods. Five main DDoS attack models are considered: agent-handler model, reflexive model, IRC-based model, web-based model, and P2P-based model. Three most dangerous software capable of DDoS attacks on IoT networks are analyzed: Mirai, XOR.DDoS and Linux.Hydra. Results. There are many models and methods of DDoS attacks on IoT networks. The most dangerous are Mirai, XOR.DDoS and Linux.Hydra. Each of these software has its own specific characteristics and methods of carrying out attacks. The study also showed that there are several effective measures to counter these attacks, including setting strong passwords, regularly updating software, setting up traffic filters and restricting network access. Conclusions. Key aspects of DDoS attacks, their models and process steps are considered. The paper focuses on the three most dangerous software used to conduct such attacks and provides recommendations on how to counteract them.

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.

Comparative study of IoT forensic frameworks

The need for secure Internet of Things (IoT) devices is growing as IoT\ndevices are becoming more integrated into vital networks. Many systems rely on\nthese devices to remain available and provide reliable service. Denial of\nservice attacks against IoT devices are a real threat due to the fact these low\npower devices are very susceptible to denial-of-service attacks. Machine\nlearning enabled network intrusion detection systems are effective at\nidentifying new threats, but they require a large amount of data to work well.\nThere are many network traffic data sets but very few that focus on IoT network\ntraffic. Within the IoT network data sets there is a lack of CoAP denial of\nservice data. We propose a novel data set covering this gap. We develop a new\ndata set by collecting network traffic from real CoAP denial of service attacks\nand compare the data on multiple different machine learning classifiers. We\nshow that the data set is effective on many classifiers.\n

Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.