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Conference Paper
Yang, B., Liu, F., Yuan, L., Zhang, Y..  2020.  6LoWPAN Protocol Based Infrared Sensor Network Human Target Locating System. 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). :1773–1779.
This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.
Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Kakanakov, N., Shopov, M..  2017.  Adaptive models for security and data protection in IoT with Cloud technologies. 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). :1001–1004.

The paper presents an example Sensor-cloud architecture that integrates security as its native ingredient. It is based on the multi-layer client-server model with separation of physical and virtual instances of sensors, gateways, application servers and data storage. It proposes the application of virtualised sensor nodes as a prerequisite for increasing security, privacy, reliability and data protection. All main concerns in Sensor-Cloud security are addressed: from secure association, authentication and authorization to privacy and data integrity and protection. The main concept is that securing the virtual instances is easier to implement, manage and audit and the only bottleneck is the physical interaction between real sensor and its virtual reflection.

Ding, Q., Peng, X., Zhang, X., Hu, X., Zhong, X..  2017.  Adaptive observer-based fault diagnosis for sensor in a class of MIMO nonlinear system. 2017 36th Chinese Control Conference (CCC). :7051–7058.

This paper presents a novel sensor parameter fault diagnosis method for generally multiple-input multiple-output (MIMO) affine nonlinear systems based on adaptive observer. Firstly, the affine nonlinear systems are transformed into the particular systems via diffeomorphic transformation using Lie derivative. Then, based on the techniques of high-gain observer and adaptive estimation, an adaptive observer structure is designed with simple method for jointly estimating the states and the unknown parameters in the output equation of the nonlinear systems. And an algorithm of the fault estimation is derived. The global exponential convergence of the proposed observer is proved succinctly. Also the proposed method can be applied to the fault diagnosis of generally affine nonlinear systems directly by the reversibility of aforementioned coordinate transformation. Finally, a numerical example is presented to illustrate the efficiency of the proposed fault diagnosis scheme.

Thapliyal, H., Ratajczak, N., Wendroth, O., Labrado, C..  2018.  Amazon Echo Enabled IoT Home Security System for Smart Home Environment. 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). :31–36.

Ever-driven by technological innovation, the Internet of Things (IoT) is continuing its exceptional evolution and growth into the common consumer space. In the wake of these developments, this paper proposes a framework for an IoT home security system that is secure, expandable, and accessible. Congruent with the ideals of the IoT, we are proposing a system utilizing an ultra-low-power wireless sensor network which would interface with a central hub via Bluetooth 4, commonly referred to as Bluetooth Low Energy (BLE), to monitor the home. Additionally, the system would interface with an Amazon Echo to accept user voice commands. The aforementioned central hub would also act as a web server and host an internet accessible configuration page from which users could monitor and customize their system. An internet-connected system would carry the capability to notify the users of system alarms via SMS or email. Finally, this proof of concept is intended to demonstrate expandability into other areas of home automation or building monitoring functions in general.

Sohu, Izhar Ahmed, Ahmed Rahimoon, Asif, Junejo, Amjad Ali, Ahmed Sohu, Arsalan, Junejo, Sadam Hussain.  2019.  Analogous Study of Security Threats in Cognitive Radio. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). :1-4.

Utilization of Wireless sensor network is growing with the development in modern technologies. On other side electromagnetic spectrum is limited resources. Application of wireless communication is expanding day by day which directly threaten electromagnetic spectrum band to become congested. Cognitive Radio solves this issue by implementation of unused frequency bands as "White Space". There is another important factor that gets attention in cognitive model i.e: Wireless Security. One of the famous causes of security threat is malicious node in cognitive radio wireless sensor networks (CRWSN). The goal of this paper is to focus on security issues which are related to CRWSN as Fusion techniques, Co-operative Spectrum sensing along with two dangerous attacks in CR: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF).

Adnan, S. F. S., Isa, M. A. M., Hashim, H..  2017.  Analysis of asymmetric encryption scheme, AA \#x03B2; Performance on Arm Microcontroller. 2017 IEEE Symposium on Computer Applications Industrial Electronics (ISCAIE). :146–151.

Security protection is a concern for the Internet of Things (IoT) which performs data exchange autonomously over the internet for remote monitoring, automation and other applications. IoT implementations has raised concerns over its security and various research has been conducted to find an effective solution for this. Thus, this work focus on the analysis of an asymmetric encryption scheme, AA-Beta (AAβ) on a platform constrained in terms of processor capability, storage and random access Memory (RAM). For this work, the platform focused is ARM Cortex-M7 microcontroller. The encryption and decryption's performance on the embedded microcontroller is realized and time executed is measured. By enabled the I-Cache (Instruction cache) and D-Cache (Data Cache), the performances are 50% faster compared to disabled the D-Cache and I-Cache. The performance is then compared to our previous work on System on Chip (SoC). This is to analyze the gap of the SoC that has utilized the full GNU Multiple Precision Arithmetic Library (GMP) package versus ARM Cortex-M7 that using the mini-gmp package in term of the footprint and the actual performance.

Yadav, S., Howells, G..  2017.  Analysis of ICMetrics Features/Technology for Wearable Devices IOT Sensors. 2017 Seventh International Conference on Emerging Security Technologies (EST). :175–178.

This paper investigates the suitability of employing various measurable features derived from multiple wearable devices (Apple Watch), for the generation of unique authentication and encryption keys related to the user. This technique is termed as ICMetrics. The ICMetrics technology requires identifying the suitable features in an environment for key generation most useful for online services. This paper presents an evaluation of the feasibility of identifying a unique user based on desirable feature set and activity data collected over short and long term and explores how the number of samples being factored into the ICMetrics system affects uniqueness of the key.

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.
Onaolapo, A.K., Akindeji, K.T..  2019.  Application of Artificial Neural Network for Fault Recognition and Classification in Distribution Network. 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). :299–304.
Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB® 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.
Agadakos, I., Ciocarlie, G. F., Copos, B., Emmi, M., George, J., Leslie, N., Michaelis, J..  2019.  Application of Trust Assessment Techniques to IoBT Systems. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :833—840.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, current operational conditions necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a collection of prior-developed cybersecurity techniques is reviewed for applicability to conditions presented by IoBT operational environments (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) through use of supporting case study examples. The research techniques covered focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) ensuring continued trust of known IoT assets and IoBT systems.

A. Bekan, M. Mohorcic, J. Cinkelj, C. Fortuna.  2015.  "An Architecture for Fully Reconfigurable Plug-and-Play Wireless Sensor Network Testbed". 2015 IEEE Global Communications Conference (GLOBECOM). :1-7.

In this paper we propose an architecture for fully-reconfigurable, plug-and-play wireless sensor network testbed. The proposed architecture is able to reconfigure and support easy experimentation and testing of standard protocol stacks (i.e. uIPv4 and uIPv6) as well as non-standardized clean-slate protocol stacks (e.g. configured using RIME). The parameters of the protocol stacks can be remotely reconfigured through an easy to use RESTful API. Additionally, we are able to fully reconfigure clean-slate protocol stacks at run-time. The architecture enables easy set-up of the network - plug - by using a protocol that automatically sets up a multi-hop network (i.e. RPL protocol) and it enables reconfiguration and experimentation - play - by using a simple, RESTful interaction with each node individually. The reference implementation of the architecture uses a dual-stack Contiki OS with the ProtoStack tool for dynamic composition of services.

Ádám, Norbert, Madoš, Branislav, Baláž, Anton, Pavlik, Tomáš.  2017.  Artificial Neural Network Based IDS. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). :000159–000164.

The Network Intrusion Detection Systems (NIDS) are either signature based or anomaly based. In this paper presented NIDS system belongs to anomaly based Neural Network Intrusion Detection System (NNIDS). The proposed NNIDS is able to successfully recognize learned malicious activities in a network environment. It was tested for the SYN flood attack, UDP flood attack, nMap scanning attack, and also for non-malicious communication.

Samudrala, A. N., Blum, R. S..  2017.  Asymptotic Analysis of a New Low Complexity Encryption Approach for the Internet of Things, Smart Cities and Smart Grid. 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC). :200–204.

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.

Leong, F. H..  2015.  Automatic detection of frustration of novice programmers from contextual and keystroke logs. 2015 10th International Conference on Computer Science Education (ICCSE). :373–377.

Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this paper, contextual and keystroke features of the students within a Java tutoring system are used to detect frustration of student within a programming exercise session. As compared to psychological sensors used in other studies, the use of contextual and keystroke logs are less obtrusive and the equipment used (keyboard) is ubiquitous in most learning environment. The technique of logistic regression with lasso regularization is utilized for the modeling to prevent over-fitting. The results showed that a model that uses only contextual and keystroke features achieved a prediction accuracy level of 0.67 and a recall measure of 0.833. Thus, we conclude that it is possible to detect frustration of a student from distilling both the contextual and keystroke logs within the tutoring system with an adequate level of accuracy.

Chandrasekaran, Selvamani, Ramachandran, K.I., Adarsh, S., Puranik, Ashish Kumar.  2020.  Avoidance of Replay attack in CAN protocol using Authenticated Encryption. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—6.
Controller Area Network is the prominent communication protocol in automotive systems. Its salient features of arbitration, message filtering, error detection, data consistency and fault confinement provide robust and reliable architecture. Despite of this, it lacks security features and is vulnerable to many attacks. One of the common attacks over the CAN communication is the replay attack. It can happen even after the implementation of encryption or authentication. This paper proposes a methodology of supressing the replay attacks by implementing authenticated encryption embedded with timestamp and pre-shared initialisation vector as a primary key. The major advantage of this system is its flexibility and configurability nature where in each layer can be chosen with the help of cryptographic algorithms to up to the entire size of the keys.
Ronczka, J..  2016.  Backchanneling Quantum Bit (Qubit) 'Shuffling': Quantum Bit (Qubit) 'Shuffling' as Added Security by Slipstreaming Q-Morse. 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). :106–115.

A fresh look at the way secure communications is currently being done has been undertaken as a consequence of the large hacking's that have taken place recently. A plausible option maybe a return to the future via Morse code using how a quantum bit (Qubit) reacts when entangled to suggest a cypher. This quantum cyphers uses multiple properties of unique entities that have many random radicals which makes hacking more difficult that traditional 'Rivest-Shamir-Adleman' (RSA), 'Digital Signature Algorithm' (DSA) or 'Elliptic Curve Digital Signature Algorithm' (ECDSA). Additional security is likely by Backchannelling (slipstreaming) Quantum Morse code (Q-Morse) keys composed of living and non-living entities. This means Blockchain ledger history (forwards-backwards) is audited during an active session. Verification keys are Backchannelling (slipstreaming) during the session (e.g. train driver must incrementally activate a switch otherwise the train stops) using predicted-expected sender-receiver properties as well as their past history of disconformities to random radicals encountered. In summary, Quantum Morse code (Q-Morse) plausibly is the enabler to additional security by Backchannelling (slipstreaming) during a communications session.

[Anonymous].  2020.  B-DCT based Watermarking Algorithm for Patient Data Protection in IoMT. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :1—4.
Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.
Mowla, Nishat I, Doh, Inshil, Chae, Kijoon.  2019.  Binarized Multi-Factor Cognitive Detection of Bio-Modality Spoofing in Fog Based Medical Cyber-Physical System. 2019 International Conference on Information Networking (ICOIN). :43–48.
Bio-modalities are ideal for user authentication in Medical Cyber-Physical Systems. Various forms of bio-modalities, such as the face, iris, fingerprint, are commonly used for secure user authentication. Concurrently, various spoofing approaches have also been developed over time which can fail traditional bio-modality detection systems. Image synthesis with play-doh, gelatin, ecoflex etc. are some of the ways used in spoofing bio-identifiable property. Since the bio-modality detection sensors are small and resource constrained, heavy-weight detection mechanisms are not suitable for these sensors. Recently, Fog based architectures are proposed to support sensor management in the Medical Cyber-Physical Systems (MCPS). A thin software client running in these resource-constrained sensors can enable communication with fog nodes for better management and analysis. Therefore, we propose a fog-based security application to detect bio-modality spoofing in a Fog based MCPS. In this regard, we propose a machine learning based security algorithm run as an application at the fog node using a binarized multi-factor boosted ensemble learner algorithm coupled with feature selection. Our proposal is verified on real datasets provided by the Replay Attack, Warsaw and LiveDet 2015 Crossmatch benchmark for face, iris and fingerprint modality spoofing detection used for authentication in an MCPS. The experimental analysis shows that our approach achieves significant performance gain over the state-of-the-art approaches.
Guibene, K., Ayaida, M., Khoukhi, L., MESSAI, N..  2020.  Black-box System Identification of CPS Protected by a Watermark-based Detector. 2020 IEEE 45th Conference on Local Computer Networks (LCN). :341–344.

The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.

Polyzos, G. C., Fotiou, N..  2017.  Blockchain-Assisted Information Distribution for the Internet of Things. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :75–78.

The Internet of Things (IoT) is envisioned to include billions of pervasive and mission-critical sensors and actuators connected to the (public) Internet. This network of smart devices is expected to generate and have access to vast amounts of information, creating unique opportunities for novel applications but, at the same time raising significant privacy and security concerns that impede its further adoption and development. In this paper, we explore the potential of a blockchain-assisted information distribution system for the IoT. We identify key security requirements of such a system and we discuss how they can be satisfied using blockchains and smart contracts. Furthermore, we present a preliminary design of the system and we identify enabling technologies.

Tosh, D. K., Shetty, S., Foytik, P., Njilla, L., Kamhoua, C. A..  2018.  Blockchain-Empowered Secure Internet -of- Battlefield Things (IoBT) Architecture. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :593—598.

Internet of Things (IoT) technology is emerging to advance the modern defense and warfare applications because the battlefield things, such as combat equipment, warfighters, and vehicles, can sense and disseminate information from the battlefield to enable real-time decision making on military operations and enhance autonomy in the battlefield. Since this Internet-of-Battlefield Things (IoBT) environment is highly heterogeneous in terms of devices, network standards, platforms, connectivity, and so on, it introduces trust, security, and privacy challenges when battlefield entities exchange information with each other. To address these issues, we propose a Blockchain-empowered auditable platform for IoBT and describe its architectural components, such as battlefield-sensing layer, network layer, and consensus and service layer, in depth. In addition to the proposed layered architecture, this paper also presents several open research challenges involved in each layer to realize the Blockchain-enabled IoBT platform.

Yamaguchi, S..  2020.  Botnet Defense System and Its Basic Strategy Against Malicious Botnet. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). :1—2.

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.

Kosmyna, Nataliya.  2019.  Brain-Computer Interfaces in the Wild: Lessons Learned from a Large-Scale Deployment. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :4161–4168.
We present data from detailed observations of a “controlled in-the-wild” study of Brain-Computer Interface (BCI) system. During 10 days of demonstration at seven nonspecialized public events, 1563 people learned about the system in various social configurations. Observations of audience behavior revealed recurring behavioral patterns. From these observations a framework of interaction with BCI systems was deduced. It describes the phases of passing by an installation, viewing and reacting, passive and active interaction, group interactions, and follow-up actions. We also conducted semi-structured interviews with the people who interacted with the system. The interviews revealed the barriers and several directions for further research on BCIs. Our findings can be useful for designing the BCIs foxr everyday adoption by a wide range of people.
Rumez, Marcel, Dürrwang, Jürgen, Brecht, Tim, Steinshorn, Timo, Neugebauer, Peter, Kriesten, Reiner, Sax, Eric.  2019.  CAN Radar: Sensing Physical Devices in CAN Networks based on Time Domain Reflectometry. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.