Biblio

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

The use of wearable devices (e.g., smartwatches) in two factor authentication (2FA) is fast emerging, as wearables promise better usability compared to smartphones. Still, the current deployments of wearable 2FA have significant usability and security issues. Specifically, one-time PIN-based wearable 2FA (PIN-2FA) requires noticeable user effort to open the app and copy random PINs from the wearable to the login terminal's (desktop/laptop) browser. An alternative approach, based on one-tap approvals via push notifications (Tap-2FA), relies upon user decision making to thwart attacks and is prone to skip-through. Both approaches are also vulnerable to traditional phishing attacks. To address this security-usability tension, we introduce a fundamentally different design of wearable 2FA, called SG-2FA, involving wrist-movement “seamless gestures” captured near transparently by the second factor wearable device while the user types a very short special sequence on the browser during the login process. The typing of the special sequence creates a wrist gesture that when identified correctly uniquely associates the login attempt with the device's owner. The special sequence can be fixed (e.g., “\$@\$@\$\$”), does not need to be a secret, and does not need to be memorized (could be simply displayed on the browser). This design improves usability over PIN-2FA since only this short sequence has to be typed as part of the login process (no interaction with or diversion of attention to the wearable and copying of random PINs is needed). It also greatly improves security compared to Tap-2FA since the attacker can not succeed in login unless the user's wrist is undergoing the exact same gesture at the exact same time. Moreover, the approach is phishing-resistant and privacy-preserving (unlike behavioral biometrics). Our results show that SG-2FA incurs only minimal errors in both benign and adversarial settings based on appropriate parameterizations.

Modern vehicles become increasingly digitalized with advanced information technology-based solutions like advanced driving assistance systems and vehicle-to-x communications. These systems are complex and interconnected. Rising complexity and increasing outside exposure has created a steadily rising demand for more cyber-secure systems. Thus, also standardization bodies and regulators issued standards and regulations to prescribe more secure development processes. This security, however, also has to be validated and verified. In order to keep pace with the need for more thorough, quicker and comparable testing, today's generally manual testing processes have to be structured and optimized. Based on existing and emerging standards for cybersecurity engineering, this paper therefore outlines a structured testing process for verifying and validating automotive cybersecurity, for which there is no standardized method so far. Despite presenting a commonly structured framework, the process is flexible in order to allow implementers to utilize their own, accustomed toolsets.

The fourth industrial revolution envisions industry manufacturing systems to be software driven where mundane manufacturing tasks can be automated. As software is perceived as an integral part of this vision, discovering vulnerabilities is of paramount of importance so that manufacturing systems are secure. A categorization of vulnerability discovery strategies can inform practitioners on how to identify undiscovered vulnerabilities in software. Recently researchers have investigated and identified vulnerability discovery strategies used in open source software (OSS) projects. The efficacy of the derived strategy needs to be validated by obtaining feedback from practitioners. Such feedback can be helpful to assess if identified strategies are useful for practitioners and possible directions the derived vulnerability discovery strategies can be improvised. We survey 51 practitioners to assess if four vulnerability discovery strategies: diagnostics, malicious payload construction, misconfiguration, and pernicious execution can be used to identify undiscovered vulnerabilities. Practitioners perceive the strategies to be useful: for example, we observe 88% of the surveyed practitioners to agree that diagnostics could be used to discover vulnerabilities. Our work provides evidence of usefulness for the identified strategies.

This prototype demonstrates the viability of manipulating both physical and virtual objects with the same tool in order to maintain object permanence across both modes of interaction. Using oppositional force feedback, provided by a servo, and an augmented visual interface, provided by the user’s smartphone, this tool simulates the look and feel of a physical object within an augmented environment. Additionally, the tool is also able to manipulate physical objects that are not part of the augmented reality, such as a physical nut. By integrating both modes of interaction into the same tool, users can fluidly move between these different modes of interaction, manipulating both physical and virtual objects as the need arises. By overlaying this kind of visual and haptic augmentation onto a common tool such as a pair of pliers, we hope to further explore scenarios for collaborative telepresence in future work.

Adhoc On-demand Distance Vector (AODV) is the well-known reactive routing protocol of Mobile Adhoc Network (MANET). Absence of security mechanism in AODV disturbs the routing because of misbehavior of attack and hence, degrades MANET's performance. Secure and efficient routing is a need of various commercial and non-commercial applications of MANET including military and war, disaster and earthquake, and riot control. This paper presents a design of important network layer attacks include black-hole (BH), gray-hole (GH) and worm-hole (WH) attacks. The performance analysis of AODV protocol is carried out under the influence of each designed attack by using the network simulator, NetSim. Simulation results show that, the network layer attacks affect packet delivery ability of AODV protocol with low energy consumption and in short time. Design of attacks helps to understand attack's behavior and hence, to develop security mechanism in AODV.

Cloud computing enables users and organizations to conveniently store and share data in large volumes and to enjoy on-demand services. Security and the protection of big data sharing from various attacks is the most challenging issue. Proxy re-encryption (PRE) is an effective method to improve the security of data sharing in the cloud environment. However, in PRE schemes, offloading big data for re-encryption will impose a heavy computational burden on the cloud proxy server, resulting in an increased computation delay and response time for the users. In this paper, we propose a novel parallel PRE workload distribution scheme to dynamically route the big data re-encryption process into the fog of the network. Moreover, this paper proposes a dynamic load balancing technique to avoid an excessive workload for the fog nodes. It also uses lightweight asymmetric cryptography to provide end-to-end security for the big data sharing between users. Within the proposed scheme, the offloading overhead on the centralized cloud server is effectively mitigated. Meanwhile, the processing delay incurred by the big data re-encryption process is efficiently improved.

Attacks on mobile devices have gained a significant amount of attention lately. This is because more and more individuals are switching to smartphones from traditional non-smartphones. Therefore, attackers or cybercriminals are now getting on the bandwagon to have an opportunity at obtaining information stored on smartphones. In this paper, we present an Android mobile application that will aid to minimize data exfiltration from attacks, such as, Acoustic Side-Channel Attack, Clipboard Jacking, Permission Misuse and Malicious Apps. This paper will commence its inception with an introduction explaining the current issues in general and how attacks such as side-channel attacks and clipboard jacking paved the way for data exfiltration. We will also discuss a few already existing solutions that try to mitigate these problems. Moving on to the methodology we will emphasize how we came about the solution and what methods we followed to achieve the end goal of securing the smartphone. In the final section, we will discuss the outcomes of the project and conclude what needs to be done in the future to enhance this project so that this mobile application will continue to keep the user's data safe from the criminals' grasps.

The construction of the power Internet of Things has led various terminals to access the corporate network on a large scale. The internal and external business interaction and data exchange are more extensive. The current security protection system is based on border isolation protection. This is difficult to meet the needs of the power Internet of Things connection and open shared services. This paper studies the application scheme of the ``zero trust'' typical business scenario of the power Internet of Things with ``Continuous Identity Authentication and Dynamic Access Control'' as the core, and designs the power internet security protection architecture based on zero trust.

Zero Trust security model permits to secure cloud native applications while encrypting all network communication, authenticating, and authorizing every request. The service mesh can enable Zero Trust using a side-car proxy without changes to the application code. To the best of our knowledge, no previous work has provided a performance analysis of Zero Trust in a multi-cloud environment. This paper proposes a multi-cloud framework and a testing workflow to analyse performance of the data plane under load and the impact on the control plane, when Zero Trust is enabled. The results of preliminary tests show that Istio has reduced latency variability in responding to sequential HTTP requests. Results also reveal that the overall CPU and memory usage can increase based on service mesh configuration and the cloud environment.

This paper proposes an anonymity based mechanism for providing privacy in IoT environment. Proposed scheme allows IoT entities to anonymously interacting and authenticating with each other, or even proving that they have trustworthy relationship without disclosing their identities. Authentication is based on an anonymous certificates mechanism where interacting IoT entities could unlinkably prove possession of a valid certificate without revealing any incorporated identity-related information, thereby preserving their privacy and thwarting tracking and profiling attacks. Through a security analysis, we demonstrate the reliability of our solution.

Particle filtering is an indispensable method for non-Gaussian state estimation, but it has some problems, such as particle degradation and requiring a large number of particles to ensure accuracy. Biota intelligence algorithms led by Cuckoo (CS) and Firefly (FA) have achieved certain results after introducing particle filtering, respectively. This paper respectively in the two kinds of bionic algorithm convergence factor and adaptive step length and random mobile innovation, seized the cuckoo algorithm (CS) in the construction of the initial value and the firefly algorithm (FA) in the iteration convergence advantages, using the improved after the update mechanism of cuckoo algorithm optimizing the initial population, and will be updated after optimization way of firefly algorithm combined with particle filter. Experimental results show that this method can ensure the diversity of particles and greatly reduce the number of particles needed for prediction while improving the filtering accuracy.

The two-factor authentication (2FA) has drawn increasingly attention as the mobile devices become more prevalent. For example, the user's possession of the enrolled phone could be used by the 2FA system as the second proof to protect his/her online accounts. Existing 2FA solutions mainly require some form of user-device interaction, which may severely affect user experience and creates extra burdens to users. In this work, we propose Proximity-Echo, a secure 2FA system utilizing the proximity of a user's enrolled phone and the login device as the second proof without requiring the user's interactions or pre-constructed device fingerprints. The basic idea of Proximity-Echo is to derive location signatures based on acoustic beep signals emitted alternately by both devices and sensing the echoes with microphones, and compare the extracted signatures for proximity detection. Given the received beep signal, our system designs a period selection scheme to identify two sound segments accurately: the chirp period is the sound segment propagating directly from the speaker to the microphone whereas the echo period is the sound segment reflected back by surrounding objects. To achieve an accurate proximity detection, we develop a new energy loss compensation extraction scheme by utilizing the extracted chirp periods to estimate the intrinsic differences of energy loss between microphones of the enrolled phone and the login device. Our proximity detection component then conducts the similarity comparison between the identified two echo periods after the energy loss compensation to effectively determine whether the enrolled phone and the login device are in proximity for 2FA. Our experimental results show that our Proximity-Echo is accurate in providing 2FA and robust to both man-in-the-middle (MiM) and co-located attacks across different scenarios and device models.

Load profiling is to cluster power consumption data to generate load patterns showing typical behaviors of consumers, and thus it has enormous potential applications in smart grid. However, short-interval readings would generate massive smart meter data. Although cloud computing provides an excellent choice to analyze such big data, it also brings significant privacy concerns since the cloud is not fully trustworthy. In this paper, based on a modified vector homomorphic encryption (VHE), we propose a privacy-preserving and outsourced k-means clustering scheme (PPOk M) for secure load profiling over encrypted meter data. In particular, we design a similarity-measuring method that effectively and non-interactively performs encrypted distance metrics. Besides, we present an integrity verification technique to detect the sloppy cloud server, which intends to stop iterations early to save computational cost. In addition, extensive experiments and analysis show that PPOk M achieves high accuracy and performance while preserving convergence and privacy.

Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.

The reliable distributed data storage system based on the Redundant Residue Number System (RRNS) is developed. The structure of the system, data splitting and recovery algorithms based on RRNS are developed. A study of the total time and time spent on converting ASCII-encoded data into a RRNS for files of various sizes is conducted. The research of data recovery time is conducted for the inverse transformation from RRNS to ASCII codes.

In line with the rapid development of the Internet of Things (IoT) network, the challenges faced are ensuring the network performance is capable to support the communication of these IoT devices. As a result, the routing protocols can provide fast route discovery and network maintenance by considering the IoT network's resource constraints. This paper's main contributions are to identify compatible IoT routing protocol using qualitative method and factor that affect network performance. Routing Protocol for Low Power and Lossy Networks (RPL) is a proactive distance- vector routing protocol designed as a proposed standard to meet the requirements of the Low Power and Lossy Networks (LLN). In this project, four influential factors on the performance of RPL in Contiki OS are examined using the Cooja simulator and then RPL performance is assessed in terms of Packet Delivery Ratio (PDR), Energy consumption and Overhead control message for the network. The project provides an insight into the implications of traffic patterns, transmission ranges, network size and node mobility for different scenarios. The results of the simulation show that the PDR and overhead ratio increases proportional to transmission distances increases but decreases while radio interference is increased. From the mobility aspect, PDR decreases by an average of 19.5% when the mobility nodes expand. On the other hand, energy consumption increases by an average of 63.7% and control message size increased up to 213% when the network consists of 40 percent of mobility nodes.

In MANET's (MOBILE Adhoc Network), the origin node will communicate the target node with the help of in-between nodes by Multi-hop communication to save power [1]. Thus, the main objective in MANETs is to identify the feasible route such that the parcels of the data can be done in an organized manner. So, the nodes in the selected route are honest and reliable. However, bad behavior nodes may affect routing performance. This work aims to discover the route does not have egotistic nodes, i.e., nodes which having honest & energy are less not considered for the route between origin and target. The proposed procedure holds the input from the end-user and results in the boundary values i.e. avg. throughput, appropriateness and drop fraction of egotistic nodes were stored in a result location. After the simulation, the discovered route by using the proposed protocol improves the overall network performance for output parameters.

The growing complexity of modern electronic systems often leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains, despite the fact that PDNs are not part of the functional design. Recent work have demonstrated that exploitation of the side channel can compromise the system security (i.e. information leakage and fault injection). In this work, we systematically investigate the PDN-based side channel as well as the countermeasures. To facilitate our goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in literature through simulation results. Further, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23dB at mid-frequency and inter-chip leakage by an average of 31.68dB at mid- and high-frequencies. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control.

A frequency domain (FD) time-reversal (TR) pre-coder is proposed to perform physical layer security (PLS) in single-input single-output (SISO) system using orthogonal frequency-division multiplexing (OFDM). To maximize the secrecy of the communication, the design of an artificial noise (AN) signal well-suited to the proposed FD TR-based OFDM SISO system is derived. This new scheme guarantees the secrecy of a communication toward a legitimate user when the channel state information (CSI) of a potential eavesdropper is not known. In particular, we derive an AN signal that does not corrupt the data transmission to the legitimate receiver but degrades the decoding performance of the eavesdropper. A closed-form approximation of the AN energy to inject is defined in order to maximize the secrecy rate (SR) of the communication. Simulation results are presented to demonstrate the security performance of the proposed secure FD TR SISO OFDM system.

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.

Today, the online review system cannot fully support the business since there are fraudulent activities inside. The companies that get low score reviews are induced to raise their score for the market competition capability by paying to the platform for deleting or editing the posted reviews. Moreover, the automatic filtration system of a platform removes some reviews without the awareness of the users. The low transparency platform causes low credibility toward the reviews. Blockchain technology provides exceptionally high transparency since every action can be traced publicly. However, there are some tradeoffs that need to be considered, such as cost and response time. This work tends to find the potential of using Blockchain technology in the online review system by testing four implementation approaches of the Ethereum Smart Contract. The result illustrates that using IPFS to store the data is a practical way of reducing transaction costs. Besides, preventing using Smart Contract states can significantly reduce costs too. The response time for using the Blockchain and IPFS system is slower than the centralized system. However, posting a review does not need a fast response. Thus, it is worthy of trading response time with transparency and cost. In the business view, the review posting with cost causes more difficulty to generate fake reviews. Moreover, there are other advantages over the centralized system, such as the reward system, bogus review voting, and global database. Thus, credibility improvement for a consumer online review system is a potential application of Blockchain technology.

The article is devoted to the choice of personal means of the 5G defense in dependence of hard- and software available to the user. The universal module MS 127.04 and its software compatible unit can be universally configured for use. An intelligent hardware and software platform is proposed for multi-core setting of policies for the automatic encryption of confidential data and selective blocking related to the implementation of computing security and confidentiality of data transfer, using such additional specially. A platform that resists the external influences is described. The platform is based on a universal module MS 127.05 (produced in Russia), that is a heterogeneous multiprocessor system on a chip), the system features 16 processor cores (NeuroMatrix Core 4) and five ARM Cortex-A5 units (ULSI 1879VM8Ya.

Over the last few decades, and thanks to the advancement of embedded systems and wireless technologies, the wireless sensors network (WSN) are increasingly used in many fields. Many researches are being done on the use of WSN in Wireless body Area Network (WBAN) systems to facilitate and improve the quality of care and remote patient monitoring.The broadcast nature of wireless communications makes it difficult to hide transmitted signals from unauthorized users. To this end, Physical layer security is emerging as a promising paradigm to protect wireless communications against eavesdropping attacks. The primary contribution of this paper is achieving a minimum secrecy outage probability by using the jamming technique which can be used by the legitimate communication partner to increase the noise level of the eavesdropper and ensure higher secure communication rate. We also evaluate the effect of additional jammers on the security of the WBAN system.