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2021-03-01
Saputra, R., Andika, J., Alaydrus, M..  2020.  Detection of Blackhole Attack in Wireless Sensor Network Using Enhanced Check Agent. 2020 Fifth International Conference on Informatics and Computing (ICIC). :1–4.
Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.
Tran, Q. T., Tran, D. D., Doan, D., Nguyen, M. S..  2020.  An Approach of BLE Mesh Network For Smart Home Application. 2020 International Conference on Advanced Computing and Applications (ACOMP). :170–174.
Internet of Things (IoT) now has extremely wide applications in many areas of life such as urban management, environmental management, smart shopping, and smart home. Because of the wide range of application fields, the IoT infrastructures are built differently. To make an IoT system indoor with high efficiency and more convenience, a case study for smart home security using Bluetooth Mesh approach is introduced. By using Bluetooth Mesh technology in home security, the user can open the door everywhere inside their house. The system work in a flexible way since it can extend the working range of network. In addition, the system can monitor the state of both the lock and any node in network by using a gateway to transfer data to cloud and enable a website-based interface.
Chakravarty, S., Hopkins, A..  2020.  LoRa Mesh Network with BeagleBone Black. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :306–311.
This paper investigates the use of BeagleBone Black Wireless single-board Linux computers with Long Range (LoRa) transceivers to send and receive information in a mesh network while one of the transmitting/receiving nodes is acting as a relay in the system. An experiment is conducted to examine how long each LoRa node needed to learn the transmission intervals of any other transmitting nodes on the network and to synchronize with the other nodes prior to transmission. The spread factor, bandwidth, and coding rate are all varied for a total of 18 different combinations. A link to the Python code used on the BeagleBone Black is provided at the end of this paper. The best parameter combinations for each individual node and for the system as a whole is investigated. Additional experiments and applications of this technology are explored in the conclusions.
2021-02-23
Park, S. H., Park, H. J., Choi, Y..  2020.  RNN-based Prediction for Network Intrusion Detection. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :572—574.
We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet.
2021-02-22
Gündoğan, C., Amsüss, C., Schmidt, T. C., Wählisch, M..  2020.  IoT Content Object Security with OSCORE and NDN: A First Experimental Comparison. 2020 IFIP Networking Conference (Networking). :19–27.
The emerging Internet of Things (IoT) challenges the end-to-end transport of the Internet by low power lossy links and gateways that perform protocol translations. Protocols such as CoAP or MQTT-SN are degraded by the overhead of DTLS sessions, which in common deployment protect content transfer only up to the gateway. To preserve content security end-to-end via gateways and proxies, the IETF recently developed Object Security for Constrained RESTful Environments (OSCORE), which extends CoAP with content object security features commonly known from Information Centric Networks (ICN). This paper presents a comparative analysis of protocol stacks that protect request-response transactions. We measure protocol performances of CoAP over DTLS, OSCORE, and the information-centric Named Data Networking (NDN) protocol on a large-scale IoT testbed in single- and multi-hop scenarios. Our findings indicate that (a) OSCORE improves on CoAP over DTLS in error-prone wireless regimes due to omitting the overhead of maintaining security sessions at endpoints, and (b) NDN attains superior robustness and reliability due to its intrinsic network caches and hop-wise retransmissions.
Doku, R., Rawat, D. B., Garuba, M., Njilla, L..  2020.  Fusion of Named Data Networking and Blockchain for Resilient Internet-of-Battlefield-Things. 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC). :1–6.
Named Data Network's (NDN) data-centric approach makes it a suitable solution in a networking scenario where there are connectivity issues as a result of the dynamism of the network. Coupling of this ability with the blockchain's well-documented immutable trustworthy-distributed ledger feature, the union of blockchain and NDN in an Internet-of-Battlefield-Things (IoBT) setting could prove to be the ideal alliance that would guarantee data exchanged in an IoBT environment is trusted and less susceptible to cyber-attacks and packet losses. Various blockchain technologies, however, require that each node has a ledger that stores information or transactions in a chain of blocks. This poses an issue as nodes in an IoBT setting have varying computing and storage resources. Moreover, most of the nodes in the IoT/IoBT network are plagued with limited resources. As such, there needs to be an approach that ensures that the limited resources of these nodes are efficiently utilized. In this paper, we investigate an approach that merges blockchain and NDN to efficiently utilize the resources of these resource-constrained nodes by only storing relevant information on each node's ledger. Furthermore, we propose a sharding technique called an Interest Group and introduce a novel consensus mechanism called Proof of Common Interest. Performance of the proposed approach is evaluated using numerical results.
2021-02-16
Mace, J. C., Czekster, R. Melo, Morisset, C., Maple, C..  2020.  Smart Building Risk Assessment Case Study: Challenges, Deficiencies and Recommendations. 2020 16th European Dependable Computing Conference (EDCC). :59—64.
Inter-networked control systems make smart buildings increasingly efficient but can lead to severe operational disruptions and infrastructure damage. It is vital the security state of smart buildings is properly assessed so that thorough and cost effective risk management can be established. This paper uniquely reports on an actual risk assessment performed in 2018 on one of the world's most densely monitored, state-of-the-art, smart buildings. From our observations, we suggest that current practice may be inadequate due to a number of challenges and deficiencies, including the lack of a recognised smart building risk assessment methodology. As a result, the security posture of many smart buildings may not be as robust as their risk assessments suggest. Crucially, we highlight a number of key recommendations for a more comprehensive risk assessment process for smart buildings. As a whole, we believe this practical experience report will be of interest to a range of smart building stakeholders.
Lotfalizadeh, H., Kim, D. S..  2020.  Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—6.
With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
Wang, Y., Kjerstad, E., Belisario, B..  2020.  A Dynamic Analysis Security Testing Infrastructure for Internet of Things. 2020 Sixth International Conference on Mobile And Secure Services (MobiSecServ). :1—6.
IoT devices such as Google Home and Amazon Echo provide great convenience to our lives. Many of these IoT devices collect data including Personal Identifiable Information such as names, phone numbers, and addresses and thus IoT security is important. However, conducting security analysis on IoT devices is challenging due to the variety, the volume of the devices, and the special skills required for hardware and software analysis. In this research, we create and demonstrate a dynamic analysis security testing infrastructure for capturing network traffic from IoT devices. The network traffic is automatically mirrored to a server for live traffic monitoring and offline data analysis. Using the dynamic analysis security testing infrastructure, we conduct extensive security analysis on network traffic from Google Home and Amazon Echo. Our testing results indicate that Google Home enforces tighter security controls than Amazon Echo while both Google and Amazon devices provide the desired security level to protect user data in general. The dynamic analysis security testing infrastructure presented in the paper can be utilized to conduct similar security analysis on any IoT devices.
2021-02-15
Karthikeyan, S. Paramasivam, El-Razouk, H..  2020.  Horizontal Correlation Analysis of Elliptic Curve Diffie Hellman. 2020 3rd International Conference on Information and Computer Technologies (ICICT). :511–519.
The world is facing a new revolutionary technology transition, Internet of things (IoT). IoT systems requires secure connectivity of distributed entities, including in-field sensors. For such external devices, Side Channel Analysis poses a potential threat as it does not require complete knowledge about the crypto algorithm. In this work, we perform Horizontal Correlation Power Analysis (HCPA) which is a type of Side Channel Analysis (SCA) over the Elliptic Curve Diffie Hellman (ECDH) key exchange protocol. ChipWhisperer (CW) by NewAE Technologies is an open source toolchain which is utilized to perform the HCPA by using CW toolchain. To best of our knowledge, this is the first attempt to implemented ECDH on Artix-7 FPGA for HCPA. We compare our correlation results with the results from AES -128 bits provided by CW. Our point of attack is the Double and Add algorithm which is used to perform Scalar multiplication in ECC. We obtain a maximum correlation of 7% for the key guess using the HCPA. We also discuss about the possible cause for lower correlation and few potentials ways to improve it. In Addition to HCPA we also perform Simple Power Analysis (SPA) (visual) for ECDH, to guess the trailing zeros in the 128-bit secret key for different power traces.
Huang, K..  2020.  Online/Offline Revocable Multi-Authority Attribute-Based Encryption for Edge Computing. 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :563–568.
Multi-authority attribute-based encryption (MA-ABE) is a promising technique to achieve fine-grained access control over encrypted data in cross domain applications. However, the dynamic change of users' access privilege brings security problems, and the heavy encryption computational cost is issue for resource-constrained users in IoT. Moreover, the invalid or illegal ciphertext will waste system resources. We propose a large universe MA-CP-ABE scheme with revocation and online/offline encryption. In our scheme, an efficient revocation mechanism is designed to change users' access privilege timely. Most of the encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, the scheme supports ciphertext verification and only valid ciphertext can be stored and transmitted. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource constrained users in edge computing for IoT.
2021-02-08
Fauzan, A., Sukarno, P., Wardana, A. A..  2020.  Overhead Analysis of the Use of Digital Signature in MQTT Protocol for Constrained Device in the Internet of Things System. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE). :415–420.
This paper presents an overhead analysis of the use of digital signature mechanisms in the Message Queue Telemetry Transport (MQTT) protocol for three classes of constrained-device. Because the resources provided by constrained-devices are very limited, the purpose of this overhead analysis is to help find out the advantages and disadvantages of each class of constrained-devices after a security mechanism has been applied, namely by applying a digital signature mechanism. The objective of using this digital signature mechanism is for providing integrity, that if the payload sent and received in its destination is still original and not changed during the transmission process. The overhead analysis aspects performed are including analyzing decryption time, signature verification performance, message delivery time, memory and flash usage in the three classes of constrained-device. Based on the overhead analysis result, it can be seen that for decryption time and signature verification performance, the Class-2 device is the fastest one. For message delivery time, the smallest time needed for receiving the payload is Class-l device. For memory usage, the Class-2 device is providing the biggest available memory and flash.
2021-02-03
Rehan, S., Singh, R..  2020.  Industrial and Home Automation, Control, Safety and Security System using Bolt IoT Platform. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :787—793.
This paper describes a system that comprises of control, safety and security subsystem for industries and homes. The entire system is based on the Bolt IoT platform. Using this system, the user can control the devices such as LEDs, speed of the fan or DC motor, monitor the temperature of the premises with an alert sub-system for critical temperatures through SMS and call, monitor the presence of anyone inside the premises with an alert sub-system about any intrusion through SMS and call. If the system is used specifically in any industry then instead of monitoring the temperature any other physical quantity, which is critical for that industry, can be monitored using suitable sensors. In addition, the cloud connectivity is provided to the system using the Bolt IoT module and temperature data is sent to the cloud where using machine-learning algorithm the future temperature is predicted to avoid any accidents in the future.
2021-02-01
Jiang, H., Du, M., Whiteside, D., Moursy, O., Yang, Y..  2020.  An Approach to Embedding a Style Transfer Model into a Mobile APP. 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). :307–316.
The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.
Jin, H., Wang, T., Zhang, M., Li, M., Wang, Y., Snoussi, H..  2020.  Neural Style Transfer for Picture with Gradient Gram Matrix Description. 2020 39th Chinese Control Conference (CCC). :7026–7030.
Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.
Kfoury, E. F., Khoury, D., AlSabeh, A., Gomez, J., Crichigno, J., Bou-Harb, E..  2020.  A Blockchain-based Method for Decentralizing the ACME Protocol to Enhance Trust in PKI. 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). :461–465.
Blockchain technology is the cornerstone of digital trust and systems' decentralization. The necessity of eliminating trust in computing systems has triggered researchers to investigate the applicability of Blockchain to decentralize the conventional security models. Specifically, researchers continuously aim at minimizing trust in the well-known Public Key Infrastructure (PKI) model which currently requires a trusted Certificate Authority (CA) to sign digital certificates. Recently, the Automated Certificate Management Environment (ACME) was standardized as a certificate issuance automation protocol. It minimizes the human interaction by enabling certificates to be automatically requested, verified, and installed on servers. ACME only solved the automation issue, but the trust concerns remain as a trusted CA is required. In this paper we propose decentralizing the ACME protocol by using the Blockchain technology to enhance the current trust issues of the existing PKI model and to eliminate the need for a trusted CA. The system was implemented and tested on Ethereum Blockchain, and the results showed that the system is feasible in terms of cost, speed, and applicability on a wide range of devices including Internet of Things (IoT) devices.
Sendhil, R., Amuthan, A..  2020.  Privacy Preserving Data Aggregation in Fog Computing using Homomorphic Encryption: An Analysis. 2020 International Conference on Computer Communication and Informatics (ICCCI). :1–5.
In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.
2021-01-28
Lin, G., Zhao, H., Zhao, L., Gan, X., Yao, Z..  2020.  Differential Privacy Information Publishing Algorithm based on Cluster Anonymity. 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). :226—233.

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data.

Zhang, M., Wei, T., Li, Z., Zhou, Z..  2020.  A service-oriented adaptive anonymity algorithm. 2020 39th Chinese Control Conference (CCC). :7626—7631.

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.

2021-01-25
Ghazo, A. T. Al, Ibrahim, M., Ren, H., Kumar, R..  2020.  A2G2V: Automatic Attack Graph Generation and Visualization and Its Applications to Computer and SCADA Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 50:3488–3498.
Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.
Mazlisham, M. H., Adnan, S. F. Syed, Isa, M. A. Mat, Mahad, Z., Asbullah, M. A..  2020.  Analysis of Rabin-P and RSA-OAEP Encryption Scheme on Microprocessor Platform. 2020 IEEE 10th Symposium on Computer Applications Industrial Electronics (ISCAIE). :292–296.
This paper presents an analysis of Rabin-P encryption scheme on microprocessor platform in term of runtime and energy consumption. A microprocessor is one of the devices utilized in the Internet of Things (IoT) structure. Therefore, in this work, the microprocessor selected is the Raspberry Pi that is powered with a smaller version of the Linux operating system for embedded devices, the Raspbian OS. A comparative analysis is then conducted for Rabin-p and RSA-OAEP cryptosystem in the Raspberry Pi setup. A conclusion can be made that Rabin-p performs faster in comparison to the RSA-OAEP cryptosystem in the microprocessor platform. Rabin-p can improve decryption efficiency by using only one modular exponentiation while produces a unique message after the decryption process.
2021-01-22
Akbari, I., Tahoun, E., Salahuddin, M. A., Limam, N., Boutaba, R..  2020.  ATMoS: Autonomous Threat Mitigation in SDN using Reinforcement Learning. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.
Machine Learning has revolutionized many fields of computer science. Reinforcement Learning (RL), in particular, stands out as a solution to sequential decision making problems. With the growing complexity of computer networks in the face of new emerging technologies, such as the Internet of Things and the growing complexity of threat vectors, there is a dire need for autonomous network systems. RL is a viable solution for achieving this autonomy. Software-defined Networking (SDN) provides a global network view and programmability of network behaviour, which can be employed for security management. Previous works in RL-based threat mitigation have mostly focused on very specific problems, mostly non-sequential, with ad-hoc solutions. In this paper, we propose ATMoS, a general framework designed to facilitate the rapid design of RL applications for network security management using SDN. We evaluate our framework for implementing RL applications for threat mitigation, by showcasing the use of ATMoS with a Neural Fitted Q-learning agent to mitigate an Advanced Persistent Threat. We present the RL model's convergence results showing the feasibility of our solution for active threat mitigation.
2021-01-20
Li, Y., Yang, Y., Yu, X., Yang, T., Dong, L., Wang, W..  2020.  IoT-APIScanner: Detecting API Unauthorized Access Vulnerabilities of IoT Platform. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1—5.

The Internet of Things enables interaction between IoT devices and users through the cloud. The cloud provides services such as account monitoring, device management, and device control. As the center of the IoT platform, the cloud provides services to IoT devices and IoT applications through APIs. Therefore, the permission verification of the API is essential. However, we found that some APIs are unverified, which allows unauthorized users to access cloud resources or control devices; it could threaten the security of devices and cloud. To check for unauthorized access to the API, we developed IoT-APIScanner, a framework to check the permission verification of the cloud API. Through observation, we found there is a large amount of interactive information between IoT application and cloud, which include the APIs and related parameters, so we can extract them by analyzing the code of the IoT application, and use this for mutating API test cases. Through these test cases, we can effectively check the permissions of the API. In our research, we extracted a total of 5 platform APIs. Among them, the proportion of APIs without permission verification reached 13.3%. Our research shows that attackers could use the API without permission verification to obtain user privacy or control of devices.

2021-01-18
Sebbah, A., Kadri, B..  2020.  A Privacy and Authentication Scheme for IoT Environments Using ECC and Fuzzy Extractor. 2020 International Conference on Intelligent Systems and Computer Vision (ISCV). :1–5.
The internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme's performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.
Bentahar, A., Meraoumia, A., Bendjenna, H., Chitroub, S., Zeroual, A..  2020.  Fuzzy Extractor-Based Key Agreement for Internet of Things. 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). :25–29.
The emergence of the Internet of Things with its constraints obliges researchers in this field to find light and accurate solutions to secure the data exchange. This document presents secure authentication using biometrics coupled with an effective key agreement scheme to save time and energy. In our scheme, the agreed key is used to encrypt transmission data between different IoT actors. While the fuzzy extractor based on the fuzzy vault principle, is used as authentication and as key agreement scheme. Besides, our system incorporates the Reed Solomon and Hamming codes to give some tolerance to errors. The experimental results have been discussed according to several recognition rates and computation times. Indeed, the recognition rate results have been compared to other works to validate our system. Also, we clarify how our system resists to specific transmission attacks without affecting lightness and accuracy.