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2021-10-12
Lalouani, Wassila, Younis, Mohamed.  2020.  Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :546–553.
In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.
Sun, Yizhen, Lin, Dandan, Song, Hong, Yan, Minjia, Cao, Linjing.  2020.  A Method to Construct Vulnerability Knowledge Graph Based on Heterogeneous Data. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :740–745.
In recent years, there are more and more attacks and exploitation aiming at network security vulnerabilities. It is effective for us to prevent criminals from exploiting vulnerabilities for attacks and help security analysts maintain equipment security that knows vulnerabilities and threats on time. With the knowledge graph, we can organize, manage, and utilize the massive information effectively in cyberspace. In this paper we construct the vulnerability ontology after analyzing multi-source heterogeneous databases. And the vulnerability knowledge graph is established. Experimental results show that the accuracy of entity recognition for extracting vendor names reaches 89.76%. The more rules used in entity recognition, the higher the accuracy and the lower the error rate.
2021-09-30
Desnitsky, Vasily A., Kotenko, Igor V., Parashchuk, Igor B..  2020.  Neural Network Based Classification of Attacks on Wireless Sensor Networks. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :284–287.
The paper proposes a method for solving problems of classifying multi-step attacks on wireless sensor networks in the conditions of uncertainty (incompleteness and inconsistency) of the observed signs of attacks. The method aims to eliminate the uncertainty of classification of attacks on networks of this class one the base of the use of neural network approaches to the processing of incomplete and contradictory knowledge on possible attack characteristics. It allows increasing objectivity (accuracy and reliability) of information security monitoring in modern software and hardware systems and Internet of Things networks that actively exploit advantages of wireless sensor networks.
Ashiquzzaman, Md., Mitra, Shuva, Nasrin, Kazi Farjana, Hossain, Md. Sanawar, Apu, Md. Khairul Hasan.  2020.  Advanced Wireless Control amp; Feedback Based Multi-functional Automatic Security System. 2020 IEEE Region 10 Symposium (TENSYMP). :1046–1049.
In this research work, an advanced automatic multifunctional compact security system technology is developed using wireless networking system. The security system provides smart security and also alerts the user to avoid the critical circumstances in the daily security issues is held. This system provides a smart solution to the variety of different problems via remote control by the software name Cayenne. This software provides the user to control the system using smart mobile or computer from all over the world and needs to be connected via internet. The system provides general security for essential purposes as the Motion detecting system alerts for any kind of movement inside the area where it is installed, the gas detecting system alerts the user for any type of gas leakage inside the room and also clearing the leaking gas by exhaust fan automatically, the fire detection system detects instantly when a slight fire is emerged also warning the user with alarm, the LDR system is for electrical door lock and it can be controlled by Cayenne using mobile or computer and lastly a home light system which can be turned on/off by the user of Cayenne. Raspberry Pi has been used to connect and control all the necessary equipment. The system provides the most essential security for home and also for corporate world and it is very simple, easy to operate, and consumes small space.
2021-09-16
Mancini, Federico, Bruvoll, Solveig, Melrose, John, Leve, Frederick, Mailloux, Logan, Ernst, Raphael, Rein, Kellyn, Fioravanti, Stefano, Merani, Diego, Been, Robert.  2020.  A Security Reference Model for Autonomous Vehicles in Military Operations. 2020 IEEE Conference on Communications and Network Security (CNS). :1–8.
In a previous article [1] we proposed a layered framework to support the assessment of the security risks associated with the use of autonomous vehicles in military operations and determine how to manage these risks appropriately. We established consistent terminology and defined the problem space, while exploring the first layer of the framework, namely risks from the mission assurance perspective. In this paper, we develop the second layer of the framework. This layer focuses on the risk assessment of the vehicles themselves and on producing a highlevel security design adequate for the mission defined in the first layer. To support this process, we also define a reference model for autonomous vehicles to use as a common basis for the assessment of risks and the design of the security controls.
Asci, Cihan, Wang, Wei, Sonkusale, Sameer.  2020.  Security Monitoring System Using Magnetically-Activated RFID Tags. 2020 IEEE SENSORS. :1–4.
Existing methods for home security monitoring depend on expensive custom battery-powered solutions. In this article, we present a battery-free solution that leverages any off-the-shelf passive radio frequency identification (RFID) tag for real-time entry detection. Sensor consists of a printed RFID antenna on paper, coupled to a magnetic reed switch and is affixed on the door. Opening of the door triggers the reed switch causing RFID signal transmission detected by any off-the-shelf passive RFID reader. This paper shows simulation and experimental results for such magnetically-actuated RFID (or magRFID) opening sensor.
Rieger, Craig, Kolias, Constantinos, Ulrich, Jacob, McJunkin, Timothy R..  2020.  A Cyber Resilient Design for Control Systems. 2020 Resilience Week (RWS). :18–25.
The following topics are dealt with: security of data; distributed power generation; power engineering computing; power grids; power system security; computer network security; voltage control; risk management; power system measurement; critical infrastructures.
2021-09-07
Ahmed, Faruk, Mahmud, Md Sultan, Yeasin, Mohammed.  2020.  Assistive System for Navigating Complex Realistic Simulated World Using Reinforcement Learning. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
Finding a free path without obstacles or situation that pose minimal risk is critical for safe navigation. People who are sighted and people who are blind or visually impaired require navigation safety while walking on a sidewalk. In this paper we develop assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We train the reinforcement model in a simulated robotic environment which is used to avoid sidewalk obstacles. A conversational agent is built by training with real conversation data. The reinforcement learning model along with a conversational agent improved the obstacle avoidance experience about 2.5% from the base case which is 78.75%.
Lenard, Teri, Bolboacă, Roland, Genge, Bela.  2020.  LOKI: A Lightweight Cryptographic Key Distribution Protocol for Controller Area Networks. 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). :513–519.
The recent advancement in the automotive sector has led to a technological explosion. As a result, the modern car provides a wide range of features supported by state of the art hardware and software. Unfortunately, while this is the case of most major components, in the same vehicle we find dozens of sensors and sub-systems built over legacy hardware and software with limited computational capabilities. This paper presents LOKI, a lightweight cryptographic key distribution scheme applicable in the case of the classical invehicle communication systems. The LOKI protocol stands out compared to already proposed protocols in the literature due to its ability to use only a single broadcast message to initiate the generation of a new cryptographic key across a group of nodes. It's lightweight key derivation algorithm takes advantage of a reverse hash chain traversal algorithm to generate fresh session keys. Experimental results consisting of a laboratory-scale system based on Vector Informatik's CANoe simulation environment demonstrate the effectiveness of the developed methodology and its seamless impact manifested on the network.
2021-08-31
Castro-Coronado, Habib, Antonino-Daviu, Jose, Quijano-López, Alfredo, Fuster-Roig, Vicente, Llovera-Segovia, Pedro.  2020.  Evaluation of the Detectability of Damper Cage Damages in Synchronous Motors through the Advanced Analysis of the Stray Flux. 2020 IEEE Energy Conversion Congress and Exposition (ECCE). :2058–2063.
The determination of the damper cage health is a matter of great importance in those industries that use large synchronous motors in their processes. In the past, unexpected damages of that element implied economic losses amounting up to several million \$. The problem is that, in the technical literature, there is a lack of non-invasive techniques enabling the reliable condition monitoring of this element. This explains the fact that, in industry, rudimentary methods are still employed to determine its condition. This paper proposes the analysis of the stray flux as a way to determine the condition of the damper cage. The paper shows that the analysis of the stray flux under starting yields characteristic time-frequency signatures of the fault components that can be used to reliably determine the condition of the damper. Moreover, the analysis of the stray flux at steady-state operation under asynchronous mode could give useful information to this end. The paper also analyses the influence of the remanent magnetism in the rotor of some synchronous motors, which can make the damper cage diagnosis more difficult; some solutions to this problem are also suggested in the paper.
2021-08-18
Sravya, G., Kumar, Manchalla. O.V.P., Sudarsana Reddy, Y., Jamal, K., Mannem, Kiran.  2020.  The Ideal Block Ciphers - Correlation of AES and PRESENT in Cryptography. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). :1107—1113.
In this digital era, the usage of technology has increased rapidly and led to the deployment of more innovative technologies for storing and transferring the generated data. The most important aspect of the emerging communication technologies is to ensure the safety and security of the generated huge amount of data. Hence, cryptography is considered as a pathway that can securely transfer and save the data. Cryptography comprises of ciphers that act like an algorithm, where the data is encrypted at the source and decrypted at the destination. This paper comprises of two ciphers namely PRESENT and AES ciphers. In the real-time applications, AES is no more relevant especially for segmenting the organizations that leverage RFID, Sensors and IoT devices. In order to overcome the strategic issues faced by these organization, PRESENT ciphers work appropriately with its super lightweight block figure, which has the equivalent significance to both security and equipment arrangements. This paper compares the AES (Advance encryption standard) symmetric block cipher with PRESENT symmetric block cipher to leverage in the industries mentioned earlier, where the huge consumption of resources becomes a significant factor. For the comparison of different ciphers, the results of area, timing analysis and the waveforms are taken into consideration.
2021-08-17
Primo, Abena.  2020.  A Comparison of Blockchain-Based Wireless Sensor Network Protocols. 2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0793—0799.
Wireless sensors are often deployed in environments where it is difficult for them to discern friend from enemy. An example case is a military tactical scenario, where sensors are deployed to map the location of an item but where some of the nodes have been compromised or where there are other malicious nodes present. In this scenario, sharing data with other network nodes may present a critical security risk to the sensor nodes. Blockchain technology, with its ability to house a secure distributed ledger, offers a possible solution. However, blockchain applications for Wireless Sensor Networks suffer from poor latency in block propagation which in turn decreases throughput and network scalability. Several researchers have proposed solutions for improved network throughput. In this work, a comparison of these existing works is performed leading to a taxonomy of existing algorithms. Characteristics consistently found in algorithms reporting improved throughput are presented and, later, these characteristics are used in the development of a new algorithm for improving throughput. The proposed algorithm utilizes a proof-of- authority consensus algorithm with a node trust-based scheme. The proposed algorithm shows strong results over the base case algorithm and was evaluated with blockchain network simulations of up to 20000 nodes.
MUTAR, AHMED IRMAYYIDH, KURNAZ, Sefer, Mohammed, Alaa Hamid.  2020.  Wireless Sensor Networks Mutual Policy For Position Protection. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). :1—4.
The usage of K-anonymity to preserve location privacy for wireless sensor network (WSN) monitoring systems, where sensor nodes operate together to notify a server with anonymous shared positions. That k-anonymous position is a coated region with at least k people. However, we identify an attack model to show that overlapping aggregate locations remain privacy-risk because the enemy can infer certain overlapping areas with persons under k who violate the privacy requirement for anonymity. Within this paper we suggest a mutual WSN privacy protocol (REAL). Actual needs sensor nodes to arrange their sensing areas separately into a variety of non-overlapping, extremely precise anonymous aggregate positions. We also developed a state transfer framework, a locking mechanism and a time delay mechanism to address the three main REAL challenges, namely self-organisation, shared assets and high precision. We equate REAL's output with current protocols through virtual experiments. The findings demonstrate that REAL preserves the privacy of sites, offers more precise question answers and decreases connectivity and device expense.
2021-08-12
Karie, Nickson M., Sahri, Nor Masri, Haskell-Dowland, Paul.  2020.  IoT Threat Detection Advances, Challenges and Future Directions. 2020 Workshop on Emerging Technologies for Security in IoT (ETSecIoT). :22—29.
It is predicted that, the number of connected Internet of Things (IoT) devices will rise to 38.6 billion by 2025 and an estimated 50 billion by 2030. The increased deployment of IoT devices into diverse areas of our life has provided us with significant benefits such as improved quality of life and task automation. However, each time a new IoT device is deployed, new and unique security threats emerge or are introduced into the environment under which the device must operate. Instantaneous detection and mitigation of every security threat introduced by different IoT devices deployed can be very challenging. This is because many of the IoT devices are manufactured with no consideration of their security implications. In this paper therefore, we review existing literature and present IoT threat detection research advances with a focus on the various IoT security challenges as well as the current developments towards combating cyber security threats in IoT networks. However, this paper also highlights several future research directions in the IoT domain.
2021-08-11
Gaikwad, Nikhil B., Ugale, Hrishikesh, Keskar, Avinash, Shivaprakash, N. C..  2020.  The Internet-of-Battlefield-Things (IoBT)-Based Enemy Localization Using Soldiers Location and Gunshot Direction. IEEE Internet of Things Journal. 7:11725–11734.
The real-time information of enemy locations is capable to transform the outcome of combat operations. Such information gathered using connected soldiers on the Internet of Battlefield Things (IoBT) is highly beneficial to create situational awareness (SA) and to plan an effective war strategy. This article presents the novel enemy localization method that uses the soldier's own locations and their gunshot direction. The hardware prototype has been developed that uses a triangulation for an enemy localization in two soldiers and a single enemy scenario. 4.24±1.77 m of average localization error and ±4° of gunshot direction error has been observed during this prototype testing. This basic model is further extended using three-stage software simulation for multiple soldiers and multiple enemy scenarios with the necessary assumptions. The effective algorithm has been proposed, which differentiates between the ghost and true predictions by analyzing the groups of subsequent shooting intents (i.e., frames). Four different complex scenarios are tested in the first stage of the simulation, around three to six frames are required for the accurate enemy localization in the relatively simple cases, and nine frames are required for the complex cases. The random error within ±4° in gunshot direction is included in the second stage of the simulation which required almost double the number of frames for similar four cases. As the number of frames increases, the accuracy of the proposed algorithm improves and better ghost point elimination is observed. In the third stage, two conventional clustering algorithms are implemented to validate the presented work. The comparative analysis shows that the proposed algorithm is faster, computationally simple, consistent, and reliable compared with others. Detailed analysis of hardware and software results for various scenarios has been discussed in this article.
2021-08-05
Ren, Xiaoli, Li, Xiaoyong, Deng, Kefeng, Ren, Kaijun, Zhou, Aolong, Song, Junqiang.  2020.  Bringing Semantics to Support Ocean FAIR Data Services with Ontologies. 2020 IEEE International Conference on Services Computing (SCC). :30—37.
With the increasing attention to ocean and the development of data-intensive sciences, a large amount of ocean data has been acquired by various observing platforms and sensors, which poses new challenges to data management and utilization. Typically, nowadays we target to move ocean data management toward the FAIR principles of being findable, accessible, interoperable, and reusable. However, the data produced and managed by different organizations with wide diversity, various structures and increasing volume make it hard to be FAIR, and one of the most critical reason is the lack of unified data representation and publication methods. In this paper, we propose novel techniques to try to solve the problem by introducing semantics with ontologies. Specifically, we first propose a unified semantic model named OEDO to represent ocean data by defining the concepts of ocean observing field, specifying the relations between the concepts, and describing the properties with ocean metadata. Then, we further optimize the state-of-the-art quick service query list (QSQL) data structure, by extending the domain concepts with WordNet to improve data discovery. Moreover, based on the OEDO model and the optimized QSQL, we propose an ocean data service publishing method called DOLP to improve data discovery and data access. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposals.
2021-08-02
Castilho, Sergio D., Godoy, Eduardo P., Salmen, Fadir.  2020.  Implementing Security and Trust in IoT/M2M using Middleware. 2020 International Conference on Information Networking (ICOIN). :726—731.
Machine to Machine (M2M) a sub area of Internet of Things (IoT) will link billions of devices or things distributed around the world using the Internet. These devices when connected exchange information obtained from the environment such as temperature or humidity from industrial or residential control process. Information Security (IS) and Trust are one of the fundamental points for users and the industry to accept the use of these devices with Confidentiality, Integrity, Availability and Authenticity. The key reason is that most of these devices use wireless media especially in residential and smart city environments. The overall goal of this work is to implement a Middleware Security to improve Safety and Security between the control network devices used in IoT/M2M and the Internet for residential or industrial environments. This implementation has been tested with different protocols as CoAP and MQTT, a microcomputer with free Real-Time Operating System (RTOS) implemented in a Raspberry Pi Gateway Access Point (RGAP), Network Address Translator (NAT), IPTable firewall and encryption is part of this implementation for secure data transmission
Billah, Mohammad Masum, Khan, Niaz Ahmed, Ullah, Mohammad Woli, Shahriar, Faisal, Rashid, Syed Zahidur, Ahmed, Md Razu.  2020.  Developing a Secured and Reliable Vehicular Communication System and Its Performance Evaluation. 2020 IEEE Region 10 Symposium (TENSYMP). :60–65.
The Ad-hoc Vehicular networks (VANET) was developed through the implementation of the concepts of ad-hoc mobile networks(MANET), which is swiftly maturing, promising, emerging wireless communication technology nowadays. Vehicular communication enables us to communicate with other vehicles and Roadside Infrastructure Units (RSU) to share information pertaining to the safety system, traffic analysis, Authentication, privacy, etc. As VANETs operate in an open wireless connectivity system, it increases permeable of variant type's security issues. Security concerns, however, which are either generally seen in ad-hoc networks or utterly unique to VANET, present significant challenges. Access Control List (ACL) can be an efficient feature to solve such security issues by permitting statements to access registered specific IP addresses in the network and deny statement unregistered IP addresses in the system. To establish such secured VANETs, the License number of the vehicle will be the Identity Number, which will be assigned via a DNS server by the Traffic Certification Authority (TCA). TCA allows registered vehicles to access the nearest two or more regions. For special vehicles, public access should be restricted by configuring ACL on a specific IP. Smart-card given by TCA can be used to authenticate a subscriber by checking previous records during entry to a new network area. After in-depth analysis of Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average Delay, and Handover Delay, this research offers more secure and reliable communication in VANETs.
2021-07-08
Su, Yishan, Zhang, Ting, Jin, Zhigang, Guo, Lei.  2020.  An Anti-Attack Trust Mechanism Based on Collaborative Spectrum Sensing for Underwater Acoustic Sensor Networks. Global Oceans 2020: Singapore – U.S. Gulf Coast. :1—5.
The main method for long-distance underwater communication is underwater acoustic communication(UAC). The bandwidth of UAC channel is narrow and the frequency band resources are scarce. Therefore, it is important to improve the frequency band utilization of UAC system. Cognitive underwater acoustic (CUA) technology is an important method. CUA network can share spectrum resources with the primary network. Spectrum sensing (SS) technology is the premise of realizing CUA. Therefore, improving the accuracy of spectral sensing is the main purpose of this paper. However, the realization of underwater SS technology still faces many difficulties. First, underwater energy supplies are scarce, making it difficult to apply complex algorithms. Second, and more seriously, CUA network can sometimes be attacked and exploited by hostile forces, which will not only lead to data leakage, but also greatly affect the accuracy of SS. In order to improve the utilization of underwater spectrum and avoid attack, an underwater spectrum sensing model based on the two-threshold energy detection method and K of M fusion decision method is established. Then, the trust mechanism based on beta function and XOR operation are proposed to combat individual attack and multi-user joint attack (MUJA) respectively. Finally, simulation result shows the effectiveness of these methods.
2021-07-07
Aski, Vidyadhar, Dhaka, Vijaypal Singh, Kumar, Sunil, Parashar, Anubha, Ladagi, Akshata.  2020.  A Multi-Factor Access Control and Ownership Transfer Framework for Future Generation Healthcare Systems. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). :93–98.
The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.
2021-06-30
Xu, Yue, Ni, Ming, Ying, Fei, Zhang, Jingwen.  2020.  Security Optimization Based on Mimic Common Operating Environment for the Internet of Vehicles. 2020 2nd International Conference on Computer Communication and the Internet (ICCCI). :18—23.
The increasing vehicles have brought convenience to people as well as many traffic problems. The Internet of Vehicles (IoV) is an extension of the intelligent transportation system based on the Internet of Things (IoT), which is the omnibearing network connection among “Vehicles, Loads, Clouds”. However, IoV also faces threats from various known and unknown security vulnerabilities. Traditional security defense methods can only deal with known attacks, while there is no effective way to deal with unknown attacks. In this paper, we show an IoV system deployed on a Mimic Common Operating Environment (MCOE). At the sensing layer, we introduce a lightweight cryptographic algorithm, LBlock, to encrypt the data collected by the hardware. Thus, we can prevent malicious tampering of information such as vehicle conditions. At the application layer, we firstly put the IoV system platform into MCOE to make it dynamic, heterogeneous and redundant. Extensive experiments prove that the sensing layer can encrypt data reliably and energy-efficiently. And we prove the feasibility and security of the Internet of Vehicles system platform on MCOE.
ur Rahman, Hafiz, Duan, Guihua, Wang, Guojun, Bhuiyan, Md Zakirul Alam, Chen, Jianer.  2020.  Trustworthy Data Acquisition and Faulty Sensor Detection using Gray Code in Cyber-Physical System. 2020 IEEE 23rd International Conference on Computational Science and Engineering (CSE). :58—65.
Due to environmental influence and technology limitation, a wireless sensor/sensors module can neither store or process all raw data locally nor reliably forward it to a destination in heterogeneous IoT environment. As a result, the data collected by the IoT's sensors are inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities. Therefore, it is highly recommended and desirable to acquire trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for trustworthy data acquisition and faulty sensor detection in the IoT environment. The key idea of IGTDC is to allow a sensor's module to examine locally whether the raw data is trustworthy before transmitting towards upstream nodes. It further distinguishes whether the acquired data can be trusted or not before data aggregation at the sink/edge node. Besides, IGTDC helps to recognize a faulty or compromised sensor. For a reliable data collection, we use collaborative IoT technique, gate-level modeling, and programmable logic device (PLD) to ensure that the acquired data is reliable before transmitting towards upstream nodes/cloud. We use a hardware-based technique called “Gray Code” to detect a faulty sensor. Through simulations we reveal that the acquired data in IGTDC framework is reliable that can make a trustworthy data collection for event detection, and assist to distinguish a faulty sensor.
2021-06-28
Yao, Manting, Yuan, Weina, Wang, Nan, Zhang, Zeyu, Qiu, Yuan, Liu, Yichuan.  2020.  SS3: Security-Aware Vendor-Constrained Task Scheduling for Heterogeneous Multiprocessor System-on-Chips. 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC). :1–6.
Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.
Sharnagat, Lekhchand, Babu, Rajesh, Adhikari, Jayant.  2020.  Trust Evaluation for Securing Compromised data Aggregation against the Collusion Attack in WSN. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). :1–5.
With a storage space limit on the sensors, WSN has some drawbacks related to bandwidth and computational skills. This limited resources would reduce the amount of data transmitted across the network. For this reason, data aggregation is considered as a new process. Iterative filtration (IF) algorithms, which provide trust assessment to the various sources from which the data aggregation has been performed, are efficient in the present data aggregation algorithms. Trust assessment is done with weights from the simple average method to aggregation, which treats attack susceptibility. Iteration filter algorithms are stronger than the ordinary average, but they do not handle the current advanced attack that takes advantage of false information with many compromise nodes. Iterative filters are strengthened by an initial confidence estimate to track new and complex attacks, improving the solidity and accuracy of the IF algorithm. The new method is mainly concerned with attacks against the clusters and not against the aggregator. In this process, if an aggregator is attacked, the current system fails, and the information is eventually transmitted to the aggregator by the cluster members. This problem can be detected when both cluster members and aggregators are being targeted. It is proposed to choose an aggregator which chooses a new aggregator according to the remaining maximum energy and distance to the base station when an aggregator attack is detected. It also save time and energy compared to the current program against the corrupted aggregator node.
P N, Renjith, K, Ramesh.  2020.  Trust based Security framework for IoT data. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). :1–5.
With an incredible growth in MEMS and Internet, IoT has developed to an inevitable invention and resource for human needs. IoT reframes the communication and created a new way of machine to machine communication. IoT utilizes smart sensor to monitor and track environmental changes in any area of interest. The high volume of sensed information is processed, formulated and presented to the user for decision making. In this paper a model is designed to perform trust evaluation and data aggregation with confidential transmission of secured information in to the network and enables higher secure and reliable data transmission for effective analysis and decision making. The Sensors in IoT devices, senses the same information and forwards redundant data in to the network. This results in higher network congestion and causes transmission overhead. This could be control by introducing data aggregation. A gateway sensor node can act as aggregator and a forward unique information to the base station. However, when the network is adulterated with malicious node, these malicious nodes tend to injects false data in to the network. In this paper, a trust based malicious node detection technique has been introduced to isolate the malicious node from forwarding false information into the network. Simulation results proves the proposed protocol can be used to reduce malicious attack with increased throughput and performance.