Biblio

This article discusses the vulnerabilities and complexity of designing secure IoT-solutions, and then presents proven approaches to protecting devices and gateways. Specifically, security mechanisms such as device authentication (including certificate-based authentication), device authentication, and application a verification of identification are described. The authors consider a protocol of message queue telemetry transport for speech and sensor networks on the Internet, its features, application variants, and characteristic procedures. The principle of "publishersubscriber" is considered. An analysis of information elements and messages is carried out. The urgency of the theme is due to the rapid development of "publisher-subscriber" architecture, for which the protocol is most characteristic.

Wireless Sensor Network (WSN) faces critical security challenges due to malware(worm, virus, malicious code etc.) attack. When a single node gets compromised by malware then start to spread in entire sensor network through neighboring sensor nodes. To understand the dynamics of malware propagation in WSN proposed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model. This model used the concept of epidemiology. The model focused on early detection of malicious signals presence in the network and accordingly application of security mechanism for its removal. The early detection method helps in controlling of malware spread and reduce battery consumption of sensor nodes. In this paper study the dynamics of malware propagation and stability analysis of the system. In epidemiology basic reproduction number is a crucial parameter which is used for the determination of malware status in the system. The expression of basic reproduction number has been obtained. Analyze the propagation dynamics and compared with previous model. The proposed model provides improved security mechanism in comparison to previous one. The extensive simulation results conform the analytical investigation and accuracy of proposed model.

Nowadays with the huge technological development, the reliance on technology has become enormous. Wireless Sensor Networks (WSN) is an example of using the Internet and communication between the patient and the hospital. Easy use of such networks helps to increase the quality of communication between patient and hospital. With the development of technology increased risk in use. Any change in this data between the patient and the hospital may cause false data that may harm the patient. In this paper, a secure protocol is designed to ensure the confidentiality, integrity, and availability of data transfer between the hospital and the patient, depending on the AES and RC4 algorithms.

The need for sensors to deliver, communicate, collect, alert, and share information in various applications has made wireless sensor networks very popular. However, due to its limited resources in terms of computation power, battery life and memory storage of the sensor nodes, it is challenging to add security features to provide the confidentiality, integrity, and availability. Blockchain technology ensures security and avoids the need of any trusted third party. However, applying Blockchain in a resource-constrained wireless sensor network is a challenging task because Blockchain is power, computation, and memory hungry in nature and demands heavy bandwidth due to control overheads. In this paper, a new routing and a private communication Blockchain framework is designed and tested with Constant Bit rate (CBR). The proposed Load Balancing Multi-Hop (LBMH) routing shares and enhances the battery life of the Cluster Heads and reduce control overhead during Block updates, but due to limited storage and energy of the sensor nodes, Blockchain in sensor networks may never become a reality unless computation, storage and battery life are readily available at low cost.

Many routing mechanisms of the wireless sensor network have been suggested in the literature, but there has not been a successful one that was designed with security. In this paper, we discuss the vulnerabilities of wireless sensor networks, how attackers can exploit these vulnerabilities, and the solutions to defend against these attacks. Furthermore, we will suggest solutions and measures secure routing mechanisms in sensor networks and study how it will affect it positively.

With the rapid development of the popularity of wireless networks, there are also increasing security threats that follow, and wireless network security issues are becoming increasingly important. Radio frequency fingerprints generated by device tolerance in wireless device transmitters have physical characteristics that are difficult to clone, and can be used for identity authentication of wireless devices. In this paper, we propose a radio frequency fingerprint extraction method based on fractional Fourier transform for transient signals. After getting the features of the signal, we use RPCA to reduce the dimension of the features, and then use KNN to classify them. The results show that when the SNR is 20dB, the recognition rate of this method is close to 100%.

This Wireless Sensor Network is a network of devices that communicates the information gathered from a monitored field through wireless links. Small size sensor nodes constitute wireless sensor networks. A Sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. Applications of wireless sensor networks include home automation, street lighting, military, healthcare and industrial process monitoring. As wireless sensor networks are distributed across large geographical area, these are vulnerable to various security threats. This affects the performance of the wireless sensor networks. The impact of security issues will become more critical if the network is used for mission-critical applications like tactical battlefield. In real life deployment scenarios, the probability of failure of nodes is more. As a result of resource constraints in the sensor nodes, traditional methods which involve large overhead computation and communication are not feasible in WSNs. Hence, design and deployment of secured WSNs is a challenging task. Attacks on WSNs include attack on confidentiality, integrity and availability. There are various types of architectures that are used to deploy WSNs. Some of them are data centric, hierarchical, location based, mobility based etc. This work discusses the security issue of hierarchical architecture and proposes a solution. In hierarchical architectures, sensor nodes are grouped to form clusters. Intra-cluster communication happens through cluster heads. Cluster heads also facilitate inter-cluster communication with other cluster heads. Aggregation of data generated by sensor nodes is done by cluster heads. Aggregated data also get transferred to base through multi-hop approach in most cases. Cluster heads are vulnerable to various malicious attacks and this greatly affects the performance of the wireless sensor network. The proposed solution identifies attacked cluster head and changes the CH by identifying the fittest node using genetic algorithm based search.

Wireless sensor networks are called wireless networks consisting of low-cost sensor nodes that use limited resources, collect and distribute data. Wireless sensor networks make observation and control of physical environments from distance easier. They are used in a variety of areas, such as environmental surveillance, military purposes, and the collection of information in specific areas. While the low cost of sensor nodes allows it to spread and increase it's quantitative, battery and computational constraints, noise and manipulation threats from the environment cause various challenges in wireless sensor applications. To overcome these challenges, researches have conducted a lot of researches on various fields like power consumption, use of resources and security approaches. In these studies, routing, placement algorithms and system designs are generally examined for efficient energy consumption. In this article, the relationship between the security of sensor networks and efficient resource usage and various scenarios are presented.

Wireless Sensor Networks (WSNs) are important and becoming more important as we integrate wireless sensor networks and the internet with different things, which has changed our life, and it is affected everywhere in our life like shopping, storage, live monitoring, smart home etc., called Internet of Things (IoT), as any use of the network physical devices that included in electronics, software, sensors, actuators, and connectivity which makes available these things to connect, collect and exchange data, and the most importantly thing is the accuracy of the data that has been collected in the Internet of Things, detecting sensor data with faulty readings is an important issue of secure communication and power consumption. So, requirement of energy-efficiency and integrity of information is mandatory.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

The timely exchange of information on new threats and vulnerabilities has become a cornerstone of effective cyber defence in recent years. Especially national authorities increasingly assume their role as information brokers through national cyber security centres and distribute warnings on new attack vectors and vital recommendations on how to mitigate them. Although many of these initiatives are effective to some degree, they also suffer from severe limitations. Many steps in the exchange process require extensive human involvement to manually review, vet, enrich, analyse and distribute security information. Some countries have therefore started to adopt distributed cyber security sensor networks to enable the automatic collection, analysis and preparation of security data and thus effectively overcome limiting scalability factors. The basic idea of IoC-centric cyber security sensor networks is that the national authorities distribute Indicators of Compromise (IoCs) to organizations and receive sightings in return. This effectively helps them to estimate the spreading of malware, anticipate further trends of spreading and derive vital findings for decision makers. While this application case seems quite simple, there are some tough questions to be answered in advance, which steer the further design decisions: How much can the monitored organization be trusted to be a partner in the search for malware? How much control of the scanning process should be delegated to the organization? What is the right level of search depth? How to deal with confidential indicators? What can be derived from encrypted traffic? How are new indicators distributed, prioritized, and scan targets selected in a scalable manner? What is a good strategy to re-schedule scans to derive meaningful data on trends, such as rate of spreading? This paper suggests a blueprint for a sensor network and raises related questions, outlines design principles, and discusses lessons learned from small-scale pilots.

As chips become more and more connected, they are more exposed (both to network and to physical attacks). Therefore one shall ensure they enjoy a sufficient protection level. Security within chips is accordingly becoming a hot topic. Incident detection and reporting is one novel function expected from chips. In this talk, we explain why it is worthwhile to resort to Artificial Intelligence (AI) for security event handling. Drivers are the need to aggregate multiple and heterogeneous security sensors, the need to digest this information quickly to produce exploitable information, and so while maintaining a low false positive detection rate. Key features are adequate learning procedures and fast and secure classification accelerated by hardware. A challenge is to embed such security-oriented AI logic, while not compromising chip power budget and silicon area. This talk accounts for the opportunities permitted by the symbiotic encounter between chip security and AI.

With the rapid development of Internet of Things applications, the power Internet of Things technologies and applications covering the various production links of the power grid "transmission, transmission, transformation, distribution and use" are becoming more and more popular, and the terminal, network and application security risks brought by them are receiving more and more attention. Combined with the architecture and risk of power Internet of Things, this paper first proposes the overall security protection technology system and strategy for power Internet of Things; then analyzes terminal identity authentication and authority control, edge area autonomy and data transmission protection, and application layer cloud fog security management. And the whole process real-time security monitoring; Finally, through the analysis of security risks and protection, the technical difficulties and directions for the security protection of the Internet of Things are proposed.

The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.

Recently, as the age of the Internet of Things is approaching, there are more and more devices that communicate data with each other by incorporating sensors and communication functions in various objects. If the IoT is miniaturized, it can be regarded as a sensor having only the sensing ability and the low performance communication ability. Low-performance sensors are difficult to use high-quality communication, and wireless security used in expensive wireless communication devices cannot be applied. Therefore, this paper proposes authentication and key Agreement that can be applied in sensor networks using communication with speed less than 1 Kbps and has limited performances.

The Internet of Things (IoT) is the network where physical devices, sensors, appliances and other different objects can communicate with each other without the need for human intervention. Wireless Sensor Networks (WSNs) are main building blocks of the IoT. Both the IoT and WSNs have many critical and non-critical applications that touch almost every aspect of our modern life. Unfortunately, these networks are prone to various types of security threats. Therefore, the security of IoT and WSNs became crucial. Furthermore, the resource limitations of the devices used in these networks complicate the problem. One of the most recent and effective approaches to address such challenges is machine learning. Machine learning inspires many solutions to secure the IoT and WSNs. In this paper, we survey the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them.

Centralized systems in the Internet of Things—be it local middleware or cloud-based services—fail to fundamentally address privacy of the collected data. We propose an architecture featuring secure multiparty computation at its core in order to realize data processing systems which already incorporate support for privacy protection in the architecture.

Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.

Post-quantum secure communication has attracted much interest in recent years. Known computationally secure post-quantum key agreement protocols are resource intensive for small devices. These devices may need to securely send frequent short messages, for example to report the measurement of a sensor. Secure communication using physical assumptions provides information-theoretic security (and so quantum-safe) with small computational over-head. Security and efficiency analysis of these systems however is asymptotic. In this poster we consider two secure message communication systems, and derive and compare their security and efficiency for finite length messages. Our results show that these systems indeed provide an attractive alternative for post-quantum security.

Cryptographic applications need high-quality random number generator (RNG) for strong security and privacy measures. This paper presents RNG based on a hydrogen gas sensor that is fabricated by using microfabrication techniques. The proposed approach extracts the thermal noise information as an entropy source from the gas sensor that is non-deterministic during its operation and using hash function SHA-256 as post processing. This non-deterministic noise is then processed to acquire a random number set fulfilling the NIST 800-22 statistical randomness test suite and it demonstrates that a gas sensor based RNG can provide high-quality random numbers. Secure data transfer is possible by having this method directly without any other hardware where hydrogen gas sensor needs to be used such as petrochemical field, fuel cells, and nuclear reactors.

Physical attacks are a serious threat for embedded devices. Since these attacks are based on physical interaction, sensing technology is a key aspect in detecting them. For highest security levels devices in need of protection are placed into tamper-resistant enclosures. In this paper we present a capacitive multi-channel security sensor IC in a 350 nm CMOS technology. This IC measures more than 128 capacitive sensor nodes of such an enclosure with an SNR of 94.6 dB across a 16×16 electrode matrix in just 19.7 ms. The theoretical sensitivity is 35 aF which is practically limited by noise to 460 aF. While this is similar to capacitive touch technology, it outperforms available solutions of this domain with respect to precision and speed.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.

Embedded and mobile devices forming part of the Internet-of-Things (IoT) need new authentication technologies and techniques. This requirement is due to the increase in effort and time attackers will use to compromise a device, often remote, based on the possibility of a significant monetary return. This paper proposes exploiting a device's accelerometers in-built functionality to implement multi-factor authentication. An experimental embedded system designed to emulate a typical mobile device is used to implement the ideas and investigated as proof-of-concept.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.