Biblio

In this work, a digitally tunable Frequency Selec-tive Surface (FSS) for use in Physical Layer Security (PLS) systems is presented. The design of a unit cell is described, which is optimized by simulations for the frequency range of 5 GHz indoor Wi-Fi. Based on the developed unit cell, a prototype with 64 binary switchable elements is set up. The performance of the surface is demonstrated by measurements.

Nowadays the use of Wi-Fi has been widely used in public places, such as in restaurants. The use of Wi-Fi in public places has a very large security vulnerability because it is used by a wide variety of visitors. Therefore, this study was conducted to evaluate the security of the WLAN network in restaurants. The methods used are Vulnerability Assessment and Penetration Testing. Penetration Testing is done by conducting several attack tests such as Deauthentication Attack, Evil Twin Attack with Captive Portal, Evil Twin Attack with Sniffing and SSL stripping, and Unauthorized Access.

The major energy-hungry components in today's battery-operated embedded devices are mostly peripheral modules like LTE, WiFi, GPS, etc. Inefficient use of these modules causes energy hotspots, namely segments of the embedded software in which the module wastes energy. We study two such hotspots in the current paper, and provide the corresponding detection and removal algorithms based on static analysis techniques. The program code hotspots occur due to unnecessary releasing and re-acquiring of a module (which puts the module in power saving mode for a while) and misplaced acquiring of the module (which makes the module or processor to waste energy in idle mode). The detections are performed according to some relation between extreme (worst-case/best-case) execution times of some program segments and time/energy specifications of the module. The experimental results on our benchmarks show about 28 percent of energy reduction after the hotspot removals.

This paper explores a practical approach to securing large wireless networks by applying Physical Layer Security (PLS). To date, PLS has mostly been seen as an information theory concept with few practical implementations. We present an Access Point (AP) selection algorithm that uses PLS to find an AP that offers the highest secrecy capacity to a legitimate user. We then propose an implementation of this algorithm using the novel concept of spectrum programming which extends Software-Defined Networking to the physical and data-link layers and makes wireless network management and control more flexible and scalable than traditional platforms. Our Wi-Fi network evaluation results show that our approach outperforms conventional solutions in terms of security, but at the expense of communication capacity, thus identifying a trade-off between security and performance. These results encourage implementation and extension to further wireless technologies.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

A Visible Light Communication (VLC) is a fast growing technology became ubiquitous in the Optical wireless communication domain. It has the benefits of high security, high bandwidth, less power consumption, free from Electro Magnetic radiation hazards. VLC can help to address the looming spectrum crunch problem with secure communication in an unlimited spectrum. VLC provides extensive wireless connectivity with larger data densities than Wi-Fi along with added security features that annihilate unwanted external network invasion. The problem such as energy consumption and infrastructure complexity has been reduced by integrating the illumination and data services. The objective is to provide fast data communication with uninterrupted network connectivity and high accuracy to the user. In this paper, a proposed visible light communication system for transmitting text information using amplitude shift keying modulation (ASK) has been presented. Testing of transmitter and receiver block based on frequency, power and distance has been analyzed. The results show that the receiver is capable of receiving input data with minimum length under direct communication with the transmitter.

As the demand for smart devices in homes increases, more and more manufacturers have been launching these devices on a mass scale. But what they are missing out on is taking care of the security part of these IoT devices which results in a more vulnerable system. This paper presents an idea through a small-scale working model and the studies that made the same possible. IoT devices face numerous threats these days with the ease of access to powerful hacking tools such as aircrack-ng which provides services like monitoring, attacking and cracking Wifi networks. The essential thought of the proposed system is to give an idea of how some common attacks are carried out, how these attacks work and to device some form of prevention as an additional security layer for IoT devices in general. The system proposed here prevents most forms of attacks that target the victim IoT device using their MAC addresses. These include DoS and DDoS attacks, both of which are the main focus of this paper. This paper also points out some of the future research work that can be followed up.

The solution of using existing WiFi devices for measurement and maintenance, and establishing a WiFi fingerprint database for precise localization has become a popular method for indoor localization. The traditional WiFi fingerprint privacy protection scheme increases the calculation amount of the client, but cannot completely protect the security of the client and the fingerprint database. In this paper, we make use of WiFi devices to present a Practical Privacy Protection Scheme In WiFi Fingerprint-based Localization PPWFL. In PPWFL, the localization server establishes a pre-partition in the fingerprint database through the E-M clustering algorithm, we divide the entire fingerprint database into several partitions. The server uses WiFi fingerprint entries with partitions as training data and trains a machine learning model. This model can accurately predict the client's partition based on fingerprint entries. The client uses the trained machine learning model to obtain its partition location accurately, picks up WiFi fingerprint entries in its partition, and calculates its geographic location with the localization server through secure multi-party computing. Compared with the traditional solution, our solution only uses the WiFi fingerprint entries in the client's partition rather than the entire fingerprint database. PPWFL can reduce not only unnecessary calculations but also avoid accidental errors (Unexpected errors in fingerprint similarity between non-adjacent locations due to multipath effects of electromagnetic waves during the propagation of complex indoor environments) in fingerprint distance calculation. In particular, due to the use of Secure Multi-Party Computation, most of the calculations are performed in the local offline phase, the client only exchanges data with the localization server during the distance calculation phase. No additional equipment is needed; our solution uses only existing WiFi devices in the building to achieve fast localization based on privacy protection. We prove that PPWFL is secure under the honest but curious attacker. Experiments show that PPWFL achieves efficiency and accuracy than the traditional WiFi fingerprint localization scheme.

The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.

Nowadays, smartphones are everywhere. They play an indispensable role in our lives and makes people convenient to communicate, pay, socialize, etc. However, they also bring a lot of security and privacy risks. Keystroke operations of numeric keypad are often required when users input password to perform mobile payment or input other privacy-sensitive information. Different keystrokes may cause different finger movements that will bring different interference to WiFi signal, which may be reflected by channel state information (CSI). In this paper, we propose WiPass, a password-keystroke recognition system for numerical keypad input on smartphones, which especially occurs frequently in mobile payment APPs. Based on only a public WiFi hotspot deployed in the victim payment scenario, WiPass would extracts and analyzes the CSI data generated by the password-keystroke operation of the smartphone user, and infers the user's payment password by comparing the CSI waveforms of different keystrokes. We implemented the WiPass system by using COTS WiFi AP devices and smartphones. The average keystroke segmentation accuracy was 80.45%, and the average keystroke recognition accuracy was 74.24%.

This article presents the design and development of a relay switch (RS) to handle electrical loads up to 20A using WiFi technology. The hardware design and the implementation methodology are explained, both for the power supply and for the wireless communication that are embedded in the same small printed circuit board. In the same way, the design of the implemented firmware to operate the developed RS is shown. An ESP-12E module is used to achieve wireless communication of the RS, which can be manipulated through a web page using an MQTT protocol or via and iOS or Arduino app. The developed RS presents at least three differentiators in relation to other similar devices on the market: it can handle a higher electrical load, has a design in accordance with national and international security standards and can use different cybersecurity strategies for wireless communication with the purpose of safe and reliable use. Experimental results using a lamp and a single-phase motor as electrical loads demonstrate an excellent performance and reliability of the developed relay switch.

Mobile data offloading using opportunistic network has recently gained its significance to increase mobile data needs. Such offloaders need to be properly incentivized to encourage more and more users to act as helpers in such networks. The extent of help offered by mobile data offloading alternatives using appropriate incentive mechanisms is significant in such scenarios. The limitation of existing incentive mechanisms is that they are partial in implementation while most of them use third party intervention based derivation. However, none of the papers considers trust as an essential factor for incentive distribution. Although few works contribute to the trust analysis, but the implementation is limited to offloading determination only while the incentive is independent of trust. We try to investigate if trust could be related to the Nash equilibrium based incentive evaluation. Our analysis results show that trust-based incentive distribution encourages more than 50% offloaders to act positively and contribute successfully towards efficient mobile data offloading. We compare the performance of our algorithm with literature based salary-bonus scheme implementation and get optimum incentive beyond 20% dependence over trust-based output.

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor is something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In the existing 2FA system, a user is required to act in order to implement the second layer of authentication which is not very user-friendly. Smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cellphone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In the proposed research, a new factor is used - something that is in the user's environment with minimal user involvement. Data from these broadcast messages is utilized to implement a 2FA scheme by determining whether two devices are proximate or not to ensure that they belong to the same user.

Regardless of the setting, edge computing has drawn much attention from both the academic and industrial communities. For edge computing, content delivery networks are both a concrete and production deployable use case. While viable at the WAN or telco edge scale, it is unclear if this extends to others, such as in home WiFi routers, as has been assumed by some. In this work-in-progress, we present an initial study on the viability of using smart edge WiFi routers as a caching location. We describe the simulator we created to test this, as well as the analysis of the results obtained. We use 1 day of e-commerce web log traffic from a public data set, as well as a sampled subset of our own site - part of an ecosystem of over 111 million users. We show that in the best case scenario, smart edge routers are inappropriate for e-commerce web caching.

In recent years, cross-domain roaming through Wi-Fi is ubiquitous, and the number of roaming users has increased dramatically. It is essential to authenticate users belonging to different institutes to ensure network privacy and security. Existing systems, such as eduroam, have centralized and hierarchical structure on indorse accounts that create privacy and security issues. We have proposed UniRoam, a blockchain-based cross-domain authentication scheme that provides accountability and anonymity without any trusted authority. Unlike traditional centralized approaches, UniRoam provides access authentication for its servers and users to provide anonymity and accountability without any privacy leakage issues efficiently. By using the sovrin identifier as an anonymous identity, we integrate our system with Hyperledger and Intel SGX to authenticate users that preserves both anonymity and trust when the user connects to the network. Therefore, UniRoam is highly “faulted-tolerant” to deal with different attacks and provides an effective solution that can be deployed easily in different environments.

Recently, there has been a dramatic increase in cyber attacks around the globe. Hundreds of 0day vulnerabilities on different platforms are discovered by security researchers worldwide. The attack vectors are becoming more and more difficult to be discovered by any anti threat detection engine. Inorder to bypass these smart detection mechanisms, attackers now started carrying out attacks at extremely low level where no threat inspection units are present. This makes the attack more stealthy with increased success rate and almost zero detection rate. A best case example for this scenario would be attacks like Meltdown and Spectre that targeted the modern processors to steal information by exploiting out-of-order execution feature in modern processors. These types of attacks are incredibly hard to detect and patch. Even if a patch is released, a wide range of normal audience are unaware of this both the vulnerability and the patch. This paper describes one such low level attacks that involves the process of reverse engineering firmwares and manually backdooring them with several linux utilities. Also, compromising a real world WiFi router with the manually backdoored firmware and attaining reverse shell from the router is discussed. The WiFi routers are almost everywhere especially in public places. Firmwares are responsible for controlling the routers. If the attacker manipulates the firmware and gains control over the firmware installed in the router, then the attacker can get a hold of the network and perform various MITM attacks inside the network with the help of the router.

In today's era, the probability of the wireless devices getting hacked has grown extensively. Due to the various WLAN vulnerabilities, hackers can break into the system. There is a lack of awareness among the people about security mechanisms. From the past experiences, the study reveals that router security encrypted protocol is often cracked using several ways like dictionary attack and brute force attack. The identified methods are costly, require extensive hardware, are not reliable and do not detect all the vulnerabilities of the system. This system aims to test all router protocols which are WEP, WPA, WPA2, WPS and detect the vulnerabilities of the system. Kali Linux version number 2.0 is being used over here and therefore the tools like airodump-ng, aircrack-ng are used to acquire access point pin which gives prevention methods for detected credulity and aims in testing various security protocols to make sure that there's no flaw which will be exploited.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

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.

Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.

This paper describes a realisation of a ResNet face recognition method through Zigbee-based wireless protocol. The system uses a CC2530 Zigbee-based radio frequency chip with connected VC0706 camera on it. The Arduino Nano had been used for organisation of data compression and effective division of Zigbee packets. The proposed solution also simplifies a data transmission within a strict bandwidth of Zigbee protocol and reliable packet forwarding in case of frequency distortion. The following investigation model uses Raspberry Pi 3 with connected Zigbee End Device (ZED) for successful receiving of important images and acceleration of deep learning interfaces. The model is integrated into a smart security system based on Zigbee modules, MySQL database, Android application and works in the background by using daemons procedures. To protect data, all wireless connections had been encrypted by the 128-bit Advanced Encryption Standard (AES-128) algorithm. Experimental results show a possibility to implement complex systems under restricted requirements of available transmission protocols.

This paper deals with the design and development of a Li-Fi (light fidelity) simplex communication system for data exchange between Android mobile devices. Li-Fi is an up-to-date technology in the modern world, since it uses visible light for data exchange, allowing for high-speed communication. The paper includes a brief review of Li-Fi technology, a review of the literature used, and a study of technological methods for implementing such systems, based on scientific sources. We propose the algorithms for data exchange, packet formation, and encryption-decryption. The paper presents the developed mobile application and the transceiver device, the development results, as well as experiments with the developed prototype. The results show that Li-Fi technology is workable and is a good alternative to existing communication methods.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

With the popularization and application of Internet of Things technology, the degree of intelligence of the home system is getting higher and higher. As an important part of the smart home, the security system plays an important role in protecting against accidents such as flammable gas leakage, fire, and burglary that may occur in the home environment. This design focuses on sensor signal acquisition and processing, wireless access, and cloud applications, and integrates Cypress’s new generation of PSoC 6 MCU, CYW4343W Wi-Fi and Bluetooth dual-module chips, and Amazon’s AWS cloud into smart home security System designing. First, through the designed air conditioning and refrigeration module, fire warning processing module, lighting control module, ventilation fan control module, combustible gas and smoke detection and warning module, important parameter information in the home environment is obtained. Then, the hardware system is connected to the AWS cloud platform through Wi-Fi; finally, a WEB interface is built in the AWS cloud to realize remote monitoring of the smart home environment. This design has a good reference for the design of future smart home security systems.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.