scholarly journals New Authentication Algorithm Based on Verifiable Encryption with Digital Identity

Cryptography ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 19 ◽  
Author(s):  
Maki Kihara ◽  
Satoshi Iriyama

We propose a new authentication algorithm for small internet of things (IoT) devices without key distribution and secure servers. Encrypted private data are stored on the cloud server in the registration step and compared with incoming encrypted data without decryption in the verification step. We call a set of encryptions that can verify two encrypted data items without decryption a verifiable encryption (VE). In this paper, we define VE, and claim that several cryptosystems belong to the VE class. Moreover, we introduce an authentication algorithm based on VE, and show an example of the algorithm and discuss its performance and security. As the algorithm neither shares any secret keys nor decrypts, its computation time becomes very small.

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4034
Author(s):  
Arie Haenel ◽  
Yoram Haddad ◽  
Maryline Laurent ◽  
Zonghua Zhang

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.


2021 ◽  
Vol 5 (1) ◽  
pp. 28-39
Author(s):  
Minami Yoda ◽  
Shuji Sakuraba ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga

Internet of Things (IoT) for smart homes enhances convenience; however, it also introduces the risk of the leakage of private data. TOP10 IoT of OWASP 2018 shows that the first vulnerability is ”Weak, easy to predict, or embedded passwords.” This problem poses a risk because a user can not fix, change, or detect a password if it is embedded in firmware because only the developer of the firmware can control an update. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using a static analysis called Socket Search and String Search to protect from first vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, those studies required a lot of time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information: string search and socket search. In string search, the algorithm finds a function that uses the strcmp or strncmp symbol. In socket search, the algorithm finds a function that is referenced by the socket symbol. In this experiment, we measured the ability of our proposed method by searching six firmware in the real world that has a backdoor. We ran three methods: string search, socket search, and whole search to compare the two methods. As a result, all methods found login information from five of six firmware and one unexpected password. Our method reduces the analysis time. The whole search generally takes 38 mins to complete, but our methods finish the search in 4-6 min.


2021 ◽  
pp. 1-37
Author(s):  
Michele De Donno ◽  
Xenofon Fafoutis ◽  
Nicola Dragoni

The Internet of Things (IoT) is evolving our society; however, the growing adoption of IoT devices in many scenarios brings security and privacy implications. Current security solutions are either unsuitable for every IoT scenario or provide only partial security. This paper presents AntibIoTic 2.0, a distributed security system that relies on Fog computing to secure IoT devices, including legacy ones. The system is composed of a backbone, made of core Fog nodes and Cloud server, a Fog node acting at the edge as the gateway of the IoT network, and a lightweight agent running on each IoT device. The proposed system offers fine-grained, host-level security coupled with network-level protection, while its distributed nature makes it scalable, versatile, lightweight, and easy to deploy, also for legacy IoT deployments. AntibIoTic 2.0 can also publish anonymized and aggregated data and statistics on the deployments it secures, to increase awareness and push cooperations in the area of IoT security. This manuscript recaps and largely expands previous works on AntibIoTic, providing an enhanced design of the system, an extended proof-of-concept that proves its feasibility and shows its operation, and an experimental evaluation that reports the low computational overhead it causes.


2019 ◽  
Vol 6 (6) ◽  
pp. 703
Author(s):  
Eri Haryanto ◽  
Imam Riadi

<p>Perangkat Internet of Things (IoT) merupakan perangkat cerdas yang memiliki interkoneksi dengan jaringan internet global. Investigasi kasus yang menyangkut perangkat IoT akan menjadi tantangan tersendiri bagi investigator forensik. Keberagaman jenis perangkat dan teknologi akan memunculkan tantangan baru bagi investigator forensik. Dalam penelitian ini dititikberatkan forensik di level internal device perangkat IoT. Belum banyak bahkan belum penulis temukan penelitian sejenis yang fokus dalam analisis forensik perangkat IoT pada level device. Penelitian yang sudah dilakukan sebelumnya lebih banyak pada level jaringan dan level cloud server perangkat IoT. Pada penelitian ini dibangun environment perangkat IoT berupa prototype smart home sebagai media penelitian dan kajian tentang forensik level device. Pada penelitian ini digunakan analisis model forensik yang meliputi collection, examination, analysis, dan reporting dalam investigasi forensik untuk menemukan bukti digital. Penelitian ini berhasil mengungkap benar-benar ada serangan berupa injeksi malware terhadap perangkat IoT yang memiliki sistem operasi Raspbian, Fedberry dan Ubuntu Mate. Pengungkapan fakta kasus mengalami kesulitan pada perangkat IoT yang memiliki sistem operasi Kali Linux. Ditemukan 1 IP Address komputer penyerang yang diduga kuat menanamkan malware dan mengganggu sistem kerja perangkat IoT.</p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The Internet of Things (IoT) is an smart device that has interconnection with global internet networks. Investigating cases involving IoT devices will be a challenge for forensic investigators. The diversity of types of equipment and technology will create new challenges for forensic investigators. In this study focused on forensics at the IoT device's internal device level, there have not been many similar research that focuses on forensic analysis of IoT devices at the device level. Previous research has been done more at the network level and cloud level of IoT device's. In this study an IoT environment was built  a smart home prototype as a object for research and studies on forensic level devices. This study, using forensic model analysis which includes collection, examination, analysis, and reporting in finding digital evidence. This study successfully revealed that there was really an attack in the form of malware injection against IoT devices that have Raspbian, Fedberry and Ubuntu Mate operating systems. Disclosure of the fact that the case has difficulties with IoT devices that have the Kali Linux operating system. Found 1 IP Address of an attacker's computer that is allegedly strongly infusing malware and interfering with the work system of IoT devices.</em></p><p><em><strong><br /></strong></em></p>


Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1258
Author(s):  
Behnam Atazadeh ◽  
Hamed Olfat ◽  
Behzad Rismanchi ◽  
Davood Shojaei ◽  
Abbas Rajabifard

In multi-owned buildings, a community of residents live in their private properties while they use and share communal spaces and facilities. Proper management of multi-owned buildings is underpinned by rules related to health, safety, and security of the residents and visitors. Utilizing Internet of Things (IoT) devices to collect information about the livable space has become a significant trend since the introduction of first smart home appliances back in 2000. The question about who owns the IoT generated data and under what terms it can be shared with others is still unclear. IoT devices, such as security camera and occupancy sensors, can provide safety for their owners, while these devices may capture private data from the neighborhood. In fact, the residents are sometimes not aware of regulations that can prevent them from installing and collecting data from shared spaces that could breach other individuals’ privacy. On the other hand, Building Information Modelling (BIM) provides a rich 3D digital data environment to manage the physical, functional, and ownership aspects of buildings over their entire lifecycle. This study aims to propose a methodology to utilize BIM for defining the legal ownership of the IoT generated data. A case study has been used to discuss key challenges related to the ownership of IoT data in a multi-owned building. This study confirmed that BIM environment can facilitate the understanding of legal ownership of IoT datasets and supports the interpretation of who has the entitlement to use the IoT datasets in multi-owned buildings.


Author(s):  
Anamika A. Mishra ◽  
Krushnalee Surve ◽  
Devika C. Babu ◽  
Upendra Verma

Internet of Things is the extension of Internet connectivity into physical devices, called IoT devices which are connected to Cloud Servers, which help them perform many functions, including, but not limited to security protocols. However, the distance between the Cloud Server and the end device could hamper the connectivity and also risk the security. Authentication is one of the major issues that needs to be taken care of in this scenario. This paper aims to look into this issue as well as to provide a viable solution.


2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110353
Author(s):  
Mohammad Babar ◽  
Muhammad Sohail Khan

Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.


2022 ◽  
pp. 148-175
Author(s):  
Anish Khan ◽  
Dragan Peraković

The internet of things is a cutting-edge technology that is vulnerable to all sorts of fictitious solutions. As a new phase of computing emerges in the digital world, it intends to produce a huge number of smart gadgets that can host a wide range of applications and operations. IoT gadgets are a perfect target for cyber assaults because of their wide dispersion, availability/accessibility, and top-notch computing power. Furthermore, as numerous IoT devices gather and investigate private data, they become a gold mine for hostile actors. Hence, the matter of fact is that security, particularly the potential to diagnose compromised nodes, as well as the collection and preservation of testimony of an attack or illegal activity, have become top priorities. This chapter delves into the timeline and the most challenging security and privacy issues that exist in the present scenario. In addition to this, some open issues and future research directions are also discussed.


Author(s):  
Dominik Hromada ◽  
Rogério Luís de C. Costa ◽  
Leonel Santos ◽  
Carlos Rabadão

The Internet of Things (IoT) comprises the interconnection of a wide range of different devices, from Smart Bluetooth speakers to humidity sensors. The great variety of devices enables applications in several contexts, including Smart Cities and Smart Industry. IoT devices collect and process a large amount of data on machines and the environment and even monitor people's activities. Due to their characteristics and architecture, IoT devices and networks are potential targets for cyberattacks. Indeed, cyberattacks can lead to malfunctions of the IoT environment and access and misuse of private data. This chapter addresses security concerns in the IoT ecosystem. It identifies common threats for each of IoT layers and presents advantages, challenges, and limitations of promising countermeasures based on new technologies and strategies, like Blockchain and Machine Learning. It also contains a more in-depth discussion on Intrusion Detection Systems (IDS) for IoT, a promising solution for cybersecurity in IoT ecosystems.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Amineh Amini ◽  
Hadi Saboohi ◽  
Teh Ying Wah ◽  
Tutut Herawan

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.


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