Biblio

Radio frequency identification (RFID) is one of the key technologies of Internet of Things, which have many security issues in an open environment. In order to solve the communication problem between RFID tags and readers, security protocols has been improved constantly as the first choice. But the form of attack is also changing constantly with the development of technology. In this paper we classify the security protocols and introduce some problems in the recent security protocols.

Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention. However, these kinds of prevention also make recognition of characters by human beings difficult. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. We evaluated our CAPTCHA with deep learning which is attracting the most attention since deep learning is faster and more accurate than existing methods for recognition with computers. In this paper, we add jagged lines to edges of characters since edges are one of the most important parts for recognition in deep learning. In this paper, we also evaluate that how much the jagged lines decrease recognition of human beings and how much they prevent computers from the recognition. We confirm the effects of our method to deep learning.

Using shape context descriptors in the distance uneven grouping and its more extensive description of the shape feature, so this descriptor has the target contour point set deformation invariance. However, the twisted adhesions verification code have more outliers and more serious noise, the above-mentioned invariance of the shape context will become very bad, in order to solve the above descriptors' limitations, this article raise a new algorithm based on the relative shape context and point pattern matching to identify codes. And also experimented on the CSDN site's verification code, the result is that the recognition rate is higher than the traditional shape context and the response time is shorter.

We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user [1]. The fact is that segmentations of different types of CAPTCHAs are not always the same, which means one of CAPTCHA's bottleneck is the segmentation. Once we could accurately split the character, the problem could be solved much easier. Unfortunately, the best way to divide them is still case by case, which is to say there is no universal way to achieve it. In this paper, we present a novel algorithm to achieve state-of-the-art performance, what was more, we also constructed a new convolutional neural network as an add-on recognition part to stabilize our state-of-the-art performance of the whole CAPTCHA system. The CAPTCHA datasets we are using is from the State Administration for Industry& Commerce of the People's Republic of China. In this datasets, there are totally 33 entrances of CAPTCHAs. In this experiments, we assume that each of the entrance is known. Results are provided showing how our algorithms work well towards these CAPTCHAs.

We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where we develop a novel Captcha-breaking architecture and train it using synthetic data, demonstrating both state-of-the-art performance and a way of computing task-specific posterior uncertainty. Using a neural network trained this way, we also demonstrate successful breaking of real-world Captchas currently used by Facebook and Wikipedia. Reasoning from these empirical results and drawing connections with Bayesian modeling, we discuss the robustness of synthetic data results and suggest important considerations for ensuring good neural network generalization when training with synthetic data.

To address increasing problems caused by cyber attacks, we leverage Software Defined networks and Network Function Virtualisation governed by a SARNET-agent to enable autonomous response and attack mitigation. A Secure Autonomous Response Network (SARNET) uses a control loop to constantly assess the security state of the network by means of observables. Using a prototype we introduce the metrics impact and effectiveness and show how they can be used to compare and evaluate countermeasures. These metrics become building blocks for self learning SARNET which exhibit true autonomous response.

Cloud computing is a remarkable model for permitting on-demand network access to an elastic collection of configurable adaptive resources and features including storage, software, infrastructure, and platform. However, there are major concerns about security-related issues. A very critical security function is user authentication using passwords. Although many flaws have been discovered in password-based authentication, it remains the most convenient approach that people continue to utilize. Several schemes have been proposed to strengthen its effectiveness such as salted hashes, one-time password (OTP), single-sign-on (SSO) and multi-factor authentication (MFA). This study proposes a new authentication mechanism by combining user's password and modified characters of CAPTCHA to generate a passkey. The modification of the CAPTCHA depends on a secret agreed upon between the cloud provider and the user to employ different characters for some characters in the CAPTCHA. This scheme prevents various attacks including short-password attack, dictionary attack, keylogger, phishing, and social engineering. Moreover, it can resolve the issue of password guessing and the use of a single password for different cloud providers.

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

Tor is the most widely used anonymity network, currently serving millions of users each day. However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks. For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users. To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor. TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users. Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse. We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.

Audio-CAPTCHA prevents malicious bots from attacking Web services and provides Web accessibility for visually-impaired persons. Most of the conventional methods employ statistical noise to distort sounds and let users remember and spell the words, which are difficult and laborious work for humans. In this paper, we utilize the difficulty on speaker-independent recognition for ASR machines instead of distortion with statistical noise. Our scheme synthesizes various voices by changing voice speed, pitch and native language of speakers. Moreover, we employ semantic identification problems between random phoneme sequences and meaningful words to release users from remembering and spelling words, so it improves the accuracy of humans and usability. We also evaluated our scheme in several experiments.

CAPTCHA is a type of challenge-response test to ensure that the response is only generated by humans and not by computerized robots. CAPTCHA are getting harder as because usage of latest advanced pattern recognition and machine learning algorithms are capable of solving simpler CAPTCHA. However, some enhancement procedures make the CAPTCHAs too difficult to be recognized by the human. This paper resolves the problem by next generation human-friendly mini game-CAPTCHA for quantifying the usability of CAPTCHAs.

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

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

The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.

Wireless Sensor Network (WSN) are spread everywhere throughout the world and are ordinarily used to gather physical data from the encompassing scene. WSN play a focal part in the Internet of Things (IoT) vision. WSN is rising as a noticeable component in the middleware connecting together the Internet of Things (IoT) and the Web of Things (WoT). But the integration of WSN to WoT brings new challenges that cannot be solved in a satisfactory way with traditional layer of security. This paper examined the security issue of integration between WSN and WoT, aiming to shed light on how the WSN and WoT security issue are understood and applied, both in academia and industries. This paper introduces security perfective of integration WSN to WoT which offers capabilities to identify and connect worldwide physical objects into a unified system. As a part of the integration, serious concerns are raised over access of personal information pertaining to device (smart thing) and individual privacy. The motivation of this paper is to summarizes the security threats of the integration and suggestion to mitigate the threat.

Internet of Things (IoT) is a growing topic of interest along with 5G. Billions of IoT devices are expected to connect to the Internet in the near future. These devices differ from the traditional devices operated in the Internet. We observe that Information Centric Networking (ICN), is a more suitable architecture for the IoT compared to the prevailing IP basednetwork. However, we observe that recent works that propose to use ICN for IoT, either do not cover the need to integrate Sensor Networks with the Internet to realize IoT or do so inefficiently. Fog computing is a promising technology that has many benefits to offer especially for IoT. In this work, we discover a need to integrate various heterogeneous Sensor Networks with the Internet to realize IoT and propose FOGG: A Fog Computing Based Gateway to Integrate Sensor Networks to Internet. FOGG uses a dedicated device to function as an IoT gateway. FOGG provides the needed integration along with additional services like name/protocol translation, security and controller functionalities.

Traditionally, utility crews have used faulted circuit indicators (FCIs) to locate faulted line sections. FCIs monitor current and provide a local visual indication of recent fault activity. When a fault occurs, the FCIs operate, triggering a visual indication that is either a mechanical target (flag) or LED. There are also enhanced FCIs with communications capability, providing fault status to the outage management system (OMS) or supervisory control and data acquisition (SCADA) system. Such quickly communicated information results in faster service restoration and reduced outage times. For distribution system protection, protection devices (such as recloser controls) must coordinate with downstream devices (such as fuses or other recloser controls) to clear faults. Furthermore, if there are laterals on a feeder that are protected by a recloser control, it is desirable to communicate to the recloser control which lateral had the fault in order to enhance tripping schemes. Because line sensors are typically placed along distribution feeders, they are capable of sensing fault status and characteristics closer to the fault. If such information can be communicated quickly to upstream protection devices, at protection speeds, the protection devices can use this information to securely speed up distribution protection scheme operation. With recent advances in low-power electronics, wireless communications, and small-footprint sensor transducers, wireless line sensors can now provide fault information to the protection devices with low latencies that support protection speeds. This paper describes the components of a wireless protection sensor (WPS) system, its integration with protection devices, and how the fault information can be transmitted to such devices. Additionally, this paper discusses how the protection devices use this received fault information to securely speed up the operation speed of and improve the selectivity of distribution protection schemes, in add- tion to locating faulted line sections.

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack.

This article is about study of honeypots. In this work, we use some honeypot sensors deployment and analysis to identify, currently, what are the main attacks and security breaches explored by attackers to compromise systems. For example, a common server or service exposed to the Internet can receive a million of hits per day, but sometimes would not be easy to identify the difference between legitimate access and an attacker trying to scan, and then, interrupt the service. Finally, the objective of this research is to investigate the efficiency of the honeypots sensors to identify possible safety gaps and new ways of attacks. This research aims to propose some guidelines to avoid or minimize the damage caused by these attacks in real systems.

A Software-Defined Wireless Sensor Network (SD-WSN) is a recently developed model which is expected to play a large role not only in the development of the Internet of Things (IoT) paradigm but also as a platform for other applications such as smart water management. This model makes use of a Software-Defined Networking (SDN) approach to manage a Wireless Sensor Network (WSN) in order to solve most of the inherent issues surrounding WSNs. One of the most important aspects of any network, is security. This is an area that has received little attention within the development of SDWSNs, as most research addresses security concerns within SDN and WSNs independently. There is a need for research into the security of SDWSN. Some concepts from both SDN and WSN security can be adjusted to suit the SDWSN model while others cannot. Further research is needed into consolidating SDN and WSN security measures to consider security in SDWSN. Threats, challenges and potential solutions to securing SDWSN are presented by considering both the WSN and SDN paradigms.

A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiment results indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% sensor nodes are misbehaving.

Internet of Things (IoT) and its various application domains are radically changing the lives of people, providing smart services which will ultimately constitute integral components of the living environment. The services of IoT operate based on the data flows collected from the different sensors and actuators. In this respect, the correctness and security of the sensor data transported over the IoT system is a crucial factor in ensuring the correct functioning of the IoT services. In this work, we present a method that can detect abnormal sensor events based on “apriori” knowledge of the behavior of the monitored process. The main advantage of the proposed methodology is that it builds on well-established theoretical works, while delivering a practical technique with low computational requirements. As a result, the developed technique can be hosted on various components of an IoT system. The developed approach is evaluated through real-world use-cases.

The inherent nature of unattended sensors makes these devices most vulnerable to detection, exploitation, and denial in contested environments. Physical access is often cited as the easiest way to compromise any device or network. A new mechanism for mitigating these types of attacks developed under the Assistant Secretary of Defense for Research and Engineering, ASD(R&E) project, “Smoke Screen in Cyberspace”, was previously demonstrated in a live, over-the-air experiment. Smoke Screen encrypts, slices up, and disburses redundant fragments of files throughout the network. This paper describes enhancements to the disbursement of the file fragments routing improving the efficiency and time to completion of fragment distribution by defining the exact route, fragments should take to the destination. This is the first step in defining a custom protocol for the discovery of participating nodes and the efficient distribution of fragments in a mobile network. Future work will focus on the movement of fragments to avoid traffic analysis and avoid the collection of the entire fragment set that would enable an adversary to reconstruct the original piece of data.

In the present paper, a networked control system under both cyber and physical attacks Is considered. An adapted formulation of the problem under physical attacks, data deception and false data injection attacks, is used for controller synthesis. Based on the classical fault tolerant detection (FTD) tools, a residual generator for attack/fault detection based on observers is proposed. An event-triggered and Bilinear Matrix Inequality (BMI) implementation is proposed in order to achieve novel and better security strategy. The purpose in using this implementation would be to reduce (limit) the total number of transmissions to only instances when the networked control system (NCS) needs attention. It is important to note that the main contribution of this paper is to establish the adequate event-triggered and BMI-based methodology so that the particular structure of the mixed attacked/faulty structure can be re-formulated within the classical FTD paradigm. Experimental results are given to illustrate the developed approach efficiency on a pilot three-tank system. The plant model is presented and the proposed control design is applied to the system.

As a plethora of wearable devices are being introduced, significant concerns exist on the privacy and security of personal data stored on these devices. Expanding on recent works of using electrocardiogram (ECG) as a modality for biometric authentication, in this work, we investigate the possibility of using personal ECG signals as the individually unique source for physical unclonable function (PUF), which eventually can be used as the key for encryption and decryption engines. We present new signal processing and machine learning algorithms that learn and extract maximally different ECG features for different individuals and minimally different ECG features for the same individual over time. Experimental results with a large 741-subject in-house ECG database show that the distributions of the intra-subject (same person) Hamming distance of extracted ECG features and the inter-subject Hamming distance have minimal overlap. 256-b random numbers generated from the ECG features of 648 (out of 741) subjects pass the NIST randomness tests.