Biblio

With the popularity of smart devices and the widespread use of the Wi-Fi-based indoor localization, edge computing is becoming the mainstream paradigm of processing massive sensing data to acquire indoor localization service. However, these data which were conveyed to train the localization model unintentionally contain some sensitive information of users/devices, and were released without any protection may cause serious privacy leakage. To solve this issue, we propose a lightweight differential privacy-preserving mechanism for the edge computing environment. We extend ε-differential privacy theory to a mature machine learning localization technology to achieve privacy protection while training the localization model. Experimental results on multiple real-world datasets show that, compared with the original localization technology without privacy-preserving, our proposed scheme can achieve high accuracy of indoor localization while providing differential privacy guarantee. Through regulating the value of ε, the data quality loss of our method can be controlled up to 8.9% and the time consumption can be almost negligible. Therefore, our scheme can be efficiently applied in the edge networks and provides some guidance on indoor localization privacy protection in the edge computing.

With the popularity of Internet applications and rapid development, data using and sharing process may lead to the sensitive information divulgence. To deal with the privacy protection issue more effectively, in this paper, we propose the associated data sets protection model based on game theory from the point of view of realizing benefits from the access of privacy is about happen, quantify the extent to which visitors gain sensitive information, then compares the tolerance of the sensitive information owner and finally decides whether to allow the visitor to make an access request.

More and more security and privacy issues are arising as new technologies, such as big data and cloud computing, are widely applied in nowadays. For decreasing the privacy breaches in access control system under opening and cross-domain environment. In this paper, we suggest a game and risk based access model for privacy preserving by employing Shannon information and game theory. After defining the notions of Privacy Risk and Privacy Violation Access, a high-level framework of game theoretical risk based access control is proposed. Further, we present formulas for estimating the risk value of access request and user, construct and analyze the game model of the proposed access control by using a multi-stage two player game. There exists sub-game perfect Nash equilibrium each stage in the risk based access control and it's suitable to protect the privacy by limiting the privacy violation access requests.

Modern Internet of Things networks are often proprietary, although based on open standards, or are built on the basis of conventional Wi-Fi network, which does not allow the use of energy-saving modes and limits the range of solutions used. The paper is devoted to the study and comparison of two solutions based on Wi-Fi and Bluetooth with the functions of a self-organizing network and switching between transmission channels. The power consumption in relation to specific actions and volumes of transmitted data is investigated; a conclusion is drawn on the conditions for the application of a particular technology.

{In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances' intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol}.

Bluetooth enables excellent mobility in Brain Computer Interface (BCI) research and other use cases including ambulatory care, telemedicine, fitness tracking and mindfulness training. Although significant research exists for an all-encompassing BCI performance rating, almost all the literature addresses performance in terms of brain state or brain function classification accuracy. For the few published experiments that address BCI hardware performance, they too, focused on improving classification accuracy. This paper explores some of the more recent studies and proposes a trusted performance rating for BCI applications based on the enhanced privacy, yet reduced bandwidth needs of mobile EEG-based BCI applications. This paper proposes a set of Bluetooth operating parameters required to meet the performance, usability and privacy requirements of reliable and secure mobile neuro-feedback applications. It presents a rating model, "Trusted Mobile BCI", based on those operating parameters, and validated the model with studies that leveraged mobile BCI technology.

Manufacturers often buy and/or license communication ICs from third-party suppliers. These communication ICs are then integrated into a complex computational system, resulting in a wide range of potential hardware-software security issues. This work proposes a compact supervisory circuit to classify the Bluetooth profile operation of a Bluetooth System-on-Chip (SoC) at low frequencies by monitoring the radio frequency (RF) output power of the Bluetooth SoC. The idea is to inexpensively manufacture an RF envelope detector to monitor the RF output power and a profile classification algorithm on a custom low-frequency integrated circuit in a low-cost legacy technology. When the supervisory circuit observes unexpected behavior, it can shut off power to the Bluetooth SoC. In this preliminary work, we proto-type the supervisory circuit using off-the-shelf components to collect a sufficient data set to train 11 different Machine Learning models. We extract smart descriptive time-domain features from the envelope of the RF output signal. Then, we train the machine learning models to classify three different Bluetooth operation profiles: sensor, hands-free, and headset. Our results demonstrate 100% classification accuracy with low computational complexity.

BLE is used to transmit and receive data between sensors and devices. Most of the IOT devices employ BLE for wireless communication because it suits their requirements such as less energy constraints. The major security vulnerabilities in BLE protocol can be used by attacker to perform MITM attacks and hence violating confidentiality and integrity of data. Although BLE 4.2 prevents most of the attacks by employing elliptic-curve diffie-Hellman to generate LTK and encrypt the data, still there are many devices in the market that are using BLE 4.0, 4.1 which are vulnerable to attacks. This paper shows the simple demonstration of possible attacks on BLE devices that use various existing tools to perform spoofing, MITM and firmware attacks. We also discussed the security, privacy and its importance in BLE devices.

In recent years, the application of Bluetooth has always been the highly debated topic among the researches. Through the Bluetooth protocol, Bluetooth can implement the data switching in short distance between various devices. Nevertheless, BlueBorne Attack makes the seemingly stable Bluetooth protocols full of vulnerabilities. Our research will concentrate on predicting the BlueBorne Attack with the following directions: the working mechanism, the working methods and effective range of BlueBorne. Based on the comprehensive review of recent peer-reviewed researches, this project provides a new model based on application layer to solve the security problem of BlueBorne. The paper asserts that compared with the previous research, the unique model has better consequence with highly stability.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

Wireless Mesh Networks (WMN) are becoming inevitable in this world of high technology as it provides low cost access to broadband services. Moreover, the technologists are doing research to make WMN more reliable and secure. Subsequently, among wireless ad-hoc networking technologies, Bluetooth Low Energy (BLE) is gaining high degree of importance among researchers due to its easy availability in the gadgets and low power consumption. BLE started its journey from version 4.0 and announced the latest version 5 with mesh support capability. BLE being a low power and mesh supported technology is nowadays among the hot research topics for the researchers. Many of the researchers are working on BLE mesh technology to make it more efficient and smart. Apart from other variables of efficiency, like all communication networks, mesh network security is also of a great concern. In view of the aforesaid, this paper provides a comprehensive review on several works associated to the security in WMN and BLE mesh networks and the research related to the BLE security protocols. Moreover, after the detailed research on related works, this paper has discussed the pros and cons of the present developed mesh security mechanisms. Also, at the end after extracting the curx from the present research on WMN and BLE mesh security, this research study has devised some solutions as how to mitigate the BLE mesh network security lapses.

Internet of Things devices have spread to near ubiquity this decade. All around us now lies an invisible mesh of communication from devices embedded in seemingly everything. Inevitably some of that communication flying around our heads will contain data that must be protected or otherwise shielded from tampering. The responsibility to protect this sensitive information from malicious actors as it travels through the air then falls upon the standards used to communicate this data. Bluetooth Low Energy (BLE) is one of these standards, the aim of this paper is to put its security standards to test. By attempting to exploit its vulnerabilities we can see how secure this standard really is. In this paper, we present steps for analyzing the security of BLE devices using open-source hardware and software.

IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

As the power grid becomes more interconnected the attack surface increases and determining the causes of anomalies becomes more complex. Automated responses are a mechanism which can provide resilience in a power system by responding to anomalies. An automated response system can make intelligent decisions when paired with an automated health assessment system which includes a human in the loop for making critical decisions. Effective responses can be determined by developing a matrix which considers the likely impacts on resilience if a response is taken. A testbed assists to analyze these responses and determine their effects on system resilience.

Cloud-assisted Internet of Vehicles (IoV)which merges the advantages of both cloud computing and Internet of Things that can provide numerous online services, and bring lots of benefits and conveniences to the connected vehicles. However, the security and privacy issues such as confidentiality, access control and driver privacy may prevent it from being widely utilized for message dissemination. Existing attribute-based message encryption schemes still bring high computational cost to the lightweight vehicles. In this paper, we introduce a secure and privacy-preserving dissemination scheme for warning message in cloud-assisted IoV. Firstly, we adopt attribute-based encryption to protect the disseminated warning message, and present a verifiable encryption and decryption outsourcing construction to reduce the computational overhead on vehicles. Secondly, we present a conditional privacy preservation mechanism which utilizes anonymous identity-based signature technique to ensure anonymous vehicle authentication and message integrity checking, and also allows the trusted authority to trace the real identity of malicious vehicle. We further achieve batch verification to improve the authentication efficiency. The analysis indicate that our scheme gains more security properties and reduces the computational overhead on the vehicles.

Innovation and creativity have played an important role in the development of every field of life, relatively less but it has created several problems too. Intrusion detection is one of those problems which became difficult with the advancement in computer networks, multiple researchers with multiple techniques have come forward to solve this crucial issue, but network security is still a challenge. In our research, we have come across an idea to detect intrusion using a deep learning algorithm in combination with statistical data analysis of KDD cup 99 datasets. Firstly, we have applied statistical analysis on the given data set to generate a simplified form of data, so that a less complex binary classification model of artificial neural network could apply for data classification. Our system has decreased the complexity of the system and has improved the response time.

Deep neural networks (DNNs) provide good performance for image recognition, speech recognition, and pattern recognition. However, a poisoning attack is a serious threat to DNN's security. The poisoning attack is a method to reduce the accuracy of DNN by adding malicious training data during DNN training process. In some situations such as a military, it may be necessary to drop only a chosen class of accuracy in the model. For example, if an attacker does not allow only nuclear facilities to be selectively recognized, it may be necessary to intentionally prevent UAV from correctly recognizing nuclear-related facilities. In this paper, we propose a selective poisoning attack that reduces the accuracy of only chosen class in the model. The proposed method reduces the accuracy of a chosen class in the model by training malicious training data corresponding to a chosen class, while maintaining the accuracy of the remaining classes. For experiment, we used tensorflow as a machine learning library and MNIST and CIFAR10 as datasets. Experimental results show that the proposed method can reduce the accuracy of the chosen class to 43.2% and 55.3% in MNIST and CIFAR10, while maintaining the accuracy of the remaining classes.

Nowadays, biometric data is the most common used data in the field of security. Audio signals are one of these biometric data. Voice signals have used frequently in cases such as identification, banking systems, and forensic cases solution. The aim of this study is to determine the gender of voice signals. In the study, many different methods were used to determine the gender of voice signals. Firstly, Mel Frequency kepstrum coefficients were used to extract the feature from the audio signal. Subsequently, these attributes were classified with support vector machines, k-nearest neighborhood method and artificial neural networks. At the other stage of the study, it is aimed to determine gender from audio signals without using feature extraction method. For this, recurrent neural networks (RNN) was used. The performance analyzes of the methods used were made and the results were given. The best accuracy, precision, recall, f-score in the study has found to be 87.04%, 86.32%, 88.58%, 87.43% using K-Nearest-Neighbor algorithm.

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.

This paper investigates the physical layer (PHY-layer) authentication that exploits channel state information (CSI) to enhance multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system security by detecting spoofing attacks in wireless networks. A multi-user authentication system is proposed using convolutional neural networks (CNNs) which also can distinguish spoofers effectively. In addition, the mini batch scheme is used to train the neural networks and accelerate the training speed. Meanwhile, L1 regularization is adopted to prevent over-fitting and improve the authentication accuracy. The convolutional-neural-network-based (CNN-based) approach can authenticate legitimate users and detect attackers by CSIs with higher performances comparing to traditional hypothesis test based methods.

In this paper, we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution.

Technologies such as Industry 4.0 or assisted/autonomous driving are relying on highly customized cyber-physical realtime systems. Those systems are designed to match functional safety regulations and requirements such as EN ISO 13849, EN IEC 62061 or ISO 26262. However, as systems - especially vehicles - are becoming more connected and autonomous, they become more likely to suffer from new attack vectors. New features may meet the corresponding safety requirements but they do not consider adversaries intruding through security holes with the purpose of bringing vehicles into unsafe states. As research goal, we want to bridge the gap between security and safety in cyber-physical real-time systems by investigating real-time-aware intrusion-tolerant architectures for automotive use-cases.

The dependability of Cyber Physical Systems (CPS) solely lies in the secure and reliable functionality of their backbone, the computing platform. Security of this platform is not only threatened by the vulnerabilities in the software peripherals, but also by the vulnerabilities in the hardware internals. Such threats can arise from malicious modifications to the integrated circuits (IC) based computing hardware, which can disable the system, leak information or produce malfunctions. Such modifications to computing hardware are made possible by the globalization of the IC industry, where a computing chip can be manufactured anywhere in the world. In the complex computing environment of CPS such modifications can be stealthier and undetectable. Under such circumstances, design of these malicious modifications, and eventually their detection, will be tied to the functionality and operation of the CPS. So it is imperative to address such threats by incorporating security awareness in the computing hardware design in a comprehensive manner taking the entire system into consideration. In this paper, we present a study in the influence of hardware Trojans on closed-loop systems, which form the basis of CPS, and establish threat models. Using these models, we perform a case study on a critical CPS application, gas pipeline based SCADA system. Through this process, we establish a completely virtual simulation platform along with a hardware-in-the-loop based simulation platform for implementation and testing.

The smart grid is a complex cyber-physical system (CPS) that poses challenges related to scale, integration, interoperability, processes, governance, and human elements. The US National Institute of Standards and Technology (NIST) and its government, university and industry collaborators, developed an approach, called CPS Framework, to reasoning about CPS across multiple levels of concern and competency, including trustworthiness, privacy, reliability, and regulatory. The approach uses ontology and reasoning techniques to achieve a greater understanding of the interdependencies among the elements of the CPS Framework model applied to use cases. This paper demonstrates that the approach extends naturally to automated and manual decision-making for smart grids: we apply it to smart grid use cases, and illustrate how it can be used to analyze grid topologies and address concerns about the smart grid. Smart grid stakeholders, whose decision making may be assisted by this approach, include planners, designers and operators.