Biblio

Fog computing is a new distributed computing paradigm that extends the cloud to the network edge. Fog computing aims at improving quality of service, data access, networking, computation and storage. However, the security and privacy issues persist, even if many cloud solutions were proposed. Indeed, Fog computing introduces new challenges in terms of security and privacy, due to its specific features such as mobility, geo-distribution and heterogeneity etc. Blockchain is an emergent concept bringing efficiency in many fields. In this paper, we propose a new access control scheme based on blockchain technology for the fog computing with fault tolerance in the context of the Internet of Things. Blockchain is used to provide secure management authentication and access process to IoT devices. Each network entity authenticates in the blockchain via the wallet, which allows a secure communication in decentralized environment, hence it achieves the security objectives. In addition, we propose to establish a secure connection between the users and the IoT devices, if their attributes satisfy the policy stored in the blockchain by smart contract. We also address the blockchain transparency problem by the encryption of the users attributes both in the policy and in the request. An authorization token is generated if the encrypted attributes are identical. Moreover, our proposition offers higher scalability, availability and fault tolerance in Fog nodes due to the implementation of load balancing through the Min-Min algorithm.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.

The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multi-shot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by 3 % when compared to state-of-the-art, while providing high template security.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

Mobile peer-to-peer network (MP2P) attracts increasing attentions due to the ubiquitous use of mobile communication and huge success of peer-to-peer (P2P) mode. However, open p2p mode makes nodes tend to be selfish, and the scarcity of resources in mobile nodes aggravates this problem, thus the nodes easily express a non-complete cooperative (NCC) attitude. Therefore, an identification of non-complete cooperative nodes and a corresponding trust routing scheme are proposed for MP2P in this paper. The concept of octant is firstly introduced to build a trust model which analyzes nodes from three dimensions, namely direct trust, internal state and recommendation reliability, and then the individual non-complete cooperative (INCC) nodes can be identified by the division of different octants. The direct trust monitors nodes' external behaviors, and the consideration of internal state and recommendation reliability contributes to differentiate the subjective and objective non-cooperation, and mitigate the attacks about direct trust values respectively. Thus, the trust model can identify various INCC nodes accurately. On the basis of identification of INCC nodes, cosine similarity method is applied to identify collusive non-complete cooperate (CNCC) nodes. Moreover, a trust routing scheme based on the identification of NCC nodes is presented to reasonably deal with different kinds of NCC nodes. Results from extensive simulation experiments demonstrate that this proposed identification and routing scheme have better performances, in terms of identification precision and packet delivery fraction than current schemes respectively.

The privacy issue in data publication is critical and has been extensively studied. However, most of the existing works assume the data to be published is independent, i.e., the correlation among data is neglected. The correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, behavioral, and genetic relationships. In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively. Specifically, (ε,δ)-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy. It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy. Then, we explore the effects of deterministic correlations on privacy disclosure. For deterministic correlations, it is shown that the successful disclosure rate with correlations increases compared to the one without knowing the correlation. Meanwhile, a closed-form solution of the optimal disclosure probability and the strict bound of privacy disclosure gain are derived. Extensive simulations on a real dataset verify our analytical results.

Experts often design security and privacy technology with specific use cases and threat models in mind. In practice however, end users are not aware of these threats and potential countermeasures. Furthermore, mis-conceptions about the benefits and limitations of security and privacy technology inhibit large-scale adoption by end users. In this paper, we address this challenge and contribute a qualitative study on end users' and security experts' perceptions of threat models and potential countermeasures. We follow an inductive research approach to explore perceptions and mental models of both security experts and end users. We conducted semi-structured interviews with 8 security experts and 13 end users. Our results suggest that in contrast to security experts, end users neglect acquaintances and friends as attackers in their threat models. Our findings highlight that experts value technical countermeasures whereas end users try to implement trust-based defensive methods.

As the hottest frontier technology in the field of artificial intelligence, machine learning is subverting various industries step by step. In the future, it will penetrate all aspects of our lives and become an indispensable technology around us. Among them, network security is an area where machine learning can show off its strengths. Among many network security problems, privacy protection is a more difficult problem, so it needs more introduction of new technologies, new methods and new ideas such as machine learning to help solve some problems. The research contents for this include four parts: an overview of machine learning, the significance of machine learning in network security, the application process of machine learning in network security research, and the application of machine learning in privacy protection. It focuses on the issues related to privacy protection and proposes to combine the most advanced matching algorithm in deep learning methods with information theory data protection technology, so as to introduce it into biometric authentication. While ensuring that the loss of matching accuracy is minimal, a high-standard privacy protection algorithm is concluded, which enables businesses, government entities, and end users to more widely accept privacy protection technology.

In this paper, an E-band MIMO radar with 1 transmit and 4 receive channels is designed. The signal bandwidth is 2GHz at 77GHz, the max power of transmitted signal which is Frequency-modulated continuous-wave (FMCW) is 13dBm. This radar consists of two cascade parts: RF frond-end and digital signal process block. The RF front-end part includes antenna array, millimeter wave transceiver chips, and the digital signal process part includes FPGA, DSP and power supply circuits. It could be used in foreign object detection (FOD), landing assistance of helicopter and security checking.

This article investigates the beamforming method selection in artificial-noise-aid (AN-aid) multiuser multiple-input-single-output (MU-MISO) broadcast wiretap systems in slow fading channel environment. We adopt beamforming pre-coding matrix with artificial noise to achieve secure multiuser communication and optimize system performance, and compare the secure transmission performance of two beamforming methods. To overcome the complexity of this model, a novel optimization scheme expressed using semi-closed-form expressions and Monte Carlo method is employed to derive the relationship between transmission parameters and secure transmission performance. This scheme would help us to analyses performance of different beamforming methods.

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.

Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: a two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-the-art techniques.

We design a third-party DNS service named T2DNS. T2DNS serves client DNS queries with the following features: protecting clients from channel and server attackers, providing trustworthiness proof to clients, being compatible with the existing Internet infrastructure, and introducing bounded overhead. T2DNS's privacy preservation is achieved by a hybrid protocol of encryption and obfuscation, and its service proxy is implemented on Intel SGX. We overcome the challenges of scaling the initialization process, bounding the obfuscation overhead, and tuning practical system parameters. We prototype T2DNS, and experiment results show that T2DNS is fully functional, has acceptable overhead in comparison with other solutions, and is scalable to the number of clients.

Technology is advancing rapidly and with this advancement, it has become apparent that it is nearly impossible to not leave a digital trace when committing a crime. As evidenced by multiple cases handled by law enforcement, Fitbit data has proved to be useful when determining the validity of alibis and in piecing together the timeline of a crime scene. In our paper, experiments testing the accuracy and reliability of GPS-tracked activities logged by the Fitbit Alta tracker and Ionic smartwatch are conducted. Potential indicators of manipulated or altered GPS-tracked activities are identified to help guide digital forensic investigators when handling such Fitbit data as evidence.

In cyber threat information sharing, secure transfer and protecting privacy are very important. In this paper we solve these issues by suggesting a platform based on private permissioned Blockchain, which provides us with access control as well. The platform is called Anon-ISAC and is built on the Enhanced Privacy ID (EPID) zero-knowledge proof scheme. It makes use of permissioned Blockchain as a way to keep identity anonymous. Organizations can share their information on incidents or other artifacts among trusted parties, while they keep their identity hidden. This will save them from unwanted consequences of exposure of sensitive security information.

Cloud forensics investigates the crime committed over cloud infrastructures like SLA-violations and storage privacy. Cloud storage forensics is the process of recording the history of the creation and operations performed on a cloud data object and investing it. Secure data provenance in the Cloud is crucial for data accountability, forensics, and privacy. Towards this, we present a Cloud-based data provenance framework using Blockchain, which traces data record operations and generates provenance data. Initially, we design a dropbox like application using AWS S3 storage. The application creates a cloud storage application for the students and faculty of the university, thereby making the storage and sharing of work and resources efficient. Later, we design a data provenance mechanism for confidential files of users using Ethereum blockchain. We also evaluate the proposed system using performance parameters like query and transaction latency by varying the load and number of nodes of the blockchain network.

To our best knowledge, the p-sensitive k-anonymity model is a sophisticated model to resist linking attacks and homogeneous attacks in data publishing. However, if the distribution of sensitive values is skew, the model is difficult to defend against skew attacks and even faces sensitive attacks. In practice, the privacy requirements of different sensitive values are not always identical. The “one size fits all” unified privacy protection level may cause unnecessary information loss. To address these problems, the paper quantifies privacy requirements with the concept of IDF and concerns more about sensitive groups. Two enhanced anonymous models with personalized protection characteristic, that is, (p,αisg) -sensitive k-anonymity model and (pi,αisg)-sensitive k-anonymity model, are then proposed to resist skew attacks and sensitive attacks. Furthermore, two clustering algorithms with global search and local search are designed to implement our models. Experimental results show that the two enhanced models have outstanding advantages in better privacy at the expense of a little data utility.

Analyzing multi-dimensional geospatial data is difficult and immersive analytics systems are used to visualize geospatial data and models. There is little previous work evaluating when immersive and non-immersive visualizations are the most suitable for data analysis and more research is needed.

In this paper, the privacy preserving average consensus problem of multi-agent systems with strongly connected and weight balanced graph is considered. In most existing consensus algorithms, the agents need to exchange their state information, which leads to the disclosure of their initial states. This might be undesirable because agents' initial states may contain some important and sensitive information. To solve the problem, we propose a novel distributed algorithm, which can guarantee average consensus and meanwhile preserve the agents' privacy. This algorithm assigns some additive perturbation signals on the communication edges and these perturbations signals will be added to original true states for information exchanging. This ensures that direct disclosure of initial states can be avoided. Then a rigid analysis of our algorithm's privacy preserving performance is provided. For any individual agent in the network, we present a necessary and sufficient condition under which its privacy is preserved. The effectiveness of our algorithm is demonstrated by a numerical simulation.

Wireless Sensor Networks promises the wireless network tools that does not require any stable infrastructure. Routing is the most important effect of network operation for the extended data rates within the network. Route discovery and route search sent the required packets from the target node source. However, good data transmission is also a threatening task in networks that provide efficient and energy-efficient routing. Various research activities focus on the topology control, source location privacy optimization and effective routing improvement in WSN. Wherein the existing security solutions both routing protocols and source location solutions disrupt the self-organizing nature of wireless sensor networks. Therefore, large overhead signatures are displayed and digitally verified by the requesting node. The cloud-based and routing based schemes have provided efficient security but there are a lot of obstacles for source data and travel path information security in the WSN network. This study is dedicated to calculate the desired number of deployments for sensor nodes in a given area once the selected metric achieves a certain level of coverage, while maintaining wireless connectivity in the network. A trusted node authentication scheme in wireless sensor network reduces the communication between nodes in a secure data transmission network, where shared cryptography is established all adjacent to the sensor node. Route discovery and retransmission increases the network overhead and increases the average end-to-end delay of the network in the conventional systems. This results in higher time complexity, communication overhead and less security of constrained sensor network resources.

In order to realize the security authentication of information transmission between vehicle nodes in the vehicular ad hoc network, based on the certificateless public key cryptosystem and aggregate signature, a privacy-protected certificateless aggregate signature scheme is proposed, which eliminates the complicated certificate maintenance cost. This solution also solves the key escrow problem. By Communicating with surrounding nodes through the pseudonym of the vehicle, the privacy protection of vehicle users is realized. The signature scheme satisfies the unforgeability of an adaptive selective message attack under a random prophetic machine. The scheme meets message authentication, identity privacy protection, resistance to reply attacks.

Vulnerabilities in privileged software layers have been exploited with severe consequences. Recently, Trusted Execution Environments (TEEs) based technologies have emerged as a promising approach since they claim strong confidentiality and integrity guarantees regardless of the trustworthiness of the underlying system software. In this paper, we consider one of the most prominent TEE technologies, referred to as Intel Software Guard Extensions (SGX). Despite many formal approaches, there is still a lack of formal proof of some critical processes of Intel SGX, such as remote attestation. To fill this gap, we propose a fully automated, rigorous, and sound formal approach to specify and verify the Enhanced Privacy ID (EPID)-based remote attestation in Intel SGX under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. The evaluation indicates that the confidentiality of attestation keys is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX attestation during formal specification.

In this paper, we propose two new attacks: the Adversarial Universal False Positive (UFP) Attack and the Adversarial Universal False Negative (UFN) Attack. The objective of this research is to introduce a new class of attack using only feature vector information. The results show the potential weaknesses of five machine learning (ML) classifiers. These classifiers include k-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forrest (RF), a Support Vector Machine (SVM) with a Radial Basis Function (RBF) Kernel, and XGBoost (XGB).

Honeypots are the information system resources used for capturing and analysis of cyber attacks. Highinteraction Honeypots are capable of capturing attacks in their totality and hence are an ideal choice for capturing multi-stage cyber attacks. The term multi-stage attack is an abstraction that refers to a class of cyber attacks consisting of multiple attack stages. These attack stages are executed either by malicious codes, scripts or sometimes even inbuilt system tools. In the work presented in this paper we have proposed a framework for capturing, analysis and modelling of multi-stage cyber attacks. The objective of our work is to devise an effective mechanism for the classification of multi-stage cyber attacks. The proposed framework comprise of a network of high interaction honeypots augmented with an attack analysis engine. The analysis engine performs rule based labeling of captured honeypot data. The labeling engine labels the attack data as generic events. These events are further fused to generate attack graphs. The hence generated attack graphs are used to characterize and later classify the multi-stage cyber attacks.