Biblio

The confinement of light in three dimensions (3D) is an active research topic in Nanophotonics, since it allows for ultimate control over photons [1]. A powerful tool to this end is a 3D photonic band gap crystal with a tailored defect that acts as a cavity or even a waveguide [2]. When a one-dimensional array of cavities is coupled, an intricate waveguiding system appears, known as a CROW (coupled resonator optical waveguide) [3]. Remarkably, 3D superlattices of coupled cavities that resonate inside a 3D band gap have not been studied to date. Recently, theoretical work has predicted the occurrence of "Cartesian light", wherein light propagates by hopping only in high symmetry directions in space [4]. This represents the optical analog of the Anderson model for spins or electrons that is relevant for neuromorphic computing and may lead to intricate lasing [5].

Resource constrained devices such as sensors and RFIDs are utilized in many application areas to sense, store and transmit the sensitive data. This data must be encrypted to achieve confidentiality. The implementation of traditional public key encryption (PKE) techniques by these devices is always challenging as they possess very limited computational resources. Various encryption schemes based on identity-based encryption (IBE) and certificate-less public key encryption (CL-PKE) have been proposed to overcome limitations of PKI. However, many of these schemes involve the computationally expensive exponentiation and bilinear pairing operations on elliptic curve group to encrypt the messages. In this context, we propose a lightweight optimized CL-PKE scheme in which exponentiation and pairing operations are completely eliminated during encryption and only involves computation of cheaper addition and multiplication operations on elliptic curve. Implementation of the proposed scheme confirms its lightweight nature as compared to original CL-PKE scheme.

Over the last decade, a technology called Internet of Things (IoT) has been evolving at a rapid pace. It enables the development of endless applications in view of availability of affordable components which provide smart ecosystems. The IoT devices are constrained devices which are connected to the internet and perform sensing tasks. Each device is identified by their unique address and also makes use of the Constrained Application Protocol (CoAP) as one of the main web transfer protocols. It is an application layer protocol which does not maintain secure channels to transfer information. For authentication and end-to-end security, Datagram Transport Layer Security (DTLS) is one of the possible approaches to boost the security aspect of CoAP, in addition to which there are many suggested ways to protect the transmission of sensitive information. CoAP uses DTLS as a secure protocol and UDP as a transfer protocol. Therefore, the attacks on UDP or DTLS could be assigned as a CoAP attack. An attack on DTLS could possibly be launched in a single session and a strong authentication mechanism is needed. Man-In-The-Middle attack is one the peak security issues in CoAP as cited by Request For Comments(RFC) 7252, which encompasses attacks like Sniffing, Spoofing, Denial of Service (DoS), Hijacking, Cross-Protocol attacks and other attacks including Replay attacks and Relay attacks. In this work, a client-server architecture is setup, whose end devices communicate using CoAP. Also, a proxy system was installed across the client side to launch an active interception between the client and the server. The work will further be enhanced to provide solutions to mitigate these attacks.

Capturing the patterns in adversarial movement can present crucial insight into team dynamics and organization of cybercrimes. This information can be used for additional assessment and comparison of decision making approaches during cyberattacks. In this study, we propose a data-driven analysis based on time series analysis and social networks to identify patterns and alterations in time allocated to intrusion stages and adversarial movements. The results of this analysis on two case studies of collegiate cybersecurity exercises is provided as well as an analytical comparison of their behavioral trends and characteristics. This paper presents preliminary insight into complexities of individual and group level adversarial movement and decision-making as cyberattacks unfold.

Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.

-In wireless networks, Cooperative communication can be used to increase the strength of the communication by means of spatial diversity. Basic idea that exists behind Cooperative communication is, if the transmission from source to destination is not successful, a helping node called relay can be used to send the same information to the destination through independent paths. In order to improve the performance of such communication, channel coding techniques can be used which reduces the Bit Error Rate. Previous works on cooperative communication only concentrated on improving channel capacity through cooperation. Hence this paper presents different Channel coding methods such as Turbo coding, Convolutional coding, and low-density parity-check coding over Rayleigh fading channels in the presence of Additive white Gaussian noise. Performance of these Channel coding techniques are measured in terms of noise power spectral density (NO ) vs. Bit error rate.

Wireless Sensor Networks (WSNs) utilizes many dedicated sensors for large scale networks in order to record and monitor the conditions over the environment. Cluster-Based Wireless Sensor Networks (CBWSNs) elucidates essential challenges like routing, load balancing, and lifetime of a network and so on. Conversely, security relies a major challenge in CBWSNs by limiting its resources or not forwarding the data to the other clusters. Wireless Sensor Networks utilize different security methods to offer secure information transmission. Encryption of information records transferred into various organizations thus utilizing a very few systems are the normal practices to encourage high information security. For the most part, such encoded data and also the recovery of unique data depend on symmetric or asymmetric key sets. Collectively with the evolution of security advances, unfruitful or unauthorized endeavors have been made by different illicit outsiders to snip the transmitted information and mystery keys deviously, bother the transmission procedure or misshape the transmitted information and keys. Sometimes, the limitations made in the correspondence channel, transmitting and receiving devices might weaken information security and discontinue a critical job to perform. Thus, in this paper we audit the current information security design and key management framework in WSN. Based on this audit and recent security holes, this paper recommends a plausible incorporated answer for secure transmission of information and mystery keys to address these confinements. Thus, consistent and secure clusters is required to guarantee appropriate working of CBWSNs.

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.

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.

Scalability is an important design factor for evaluating the performance of routing protocols as the network size or traffic load increases. One of the most appropriate design methods is to use geographic routing approach to ensure scalability. This paper describes a scalability study comparing Secure Region Based Geographic Routing (SRBGR) and Dynamic Window Secure Implicit Geographic Forwarding (DWSIGF) protocols in various network density scenarios based on an end-to-end delay performance metric. The simulation studies were conducted in MATLAB 2106b where the network densities were varied according to the network topology size with increasing traffic rates. The results showed that DWSIGF has a lower end-to-end delay as compared to SRBGR for both sparse (15.4%) and high density (63.3%) network scenarios.Despite SRBGR having good security features, there is a need to improve the performance of its end-to-end delay to fulfil the application requirements.

This paper investigates the secrecy outage performance of an energy harvesting-based dual-hop amplify-and-forward (AF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. A single-antenna source node (S) is considered, communicating with one legitimate destination node (D) with the aid of a multi-antenna AF relay (R) device. In this setup, the relay node receives the incoming signal from S via an RF link, which is subject to Nakagami-m fading, then performs maximal-ratio-combining (MRC) followed by a fixed-gain amplification, before transmitting it to the destination via a UOWC link, subject to mixture Exponential-Gamma fading. Assuming the presence of a malicious eavesdropper attempting to intercept the S- R hop, a tight approximate expression for the secrecy outage probability is retrieved. The derived results provide useful insights into the influence of key system parameters on the secrecy outage performance. Our analytical results are corroborated through computer simulations, which verifies their validity.

Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives.

Once an individual employs the use of the Internet for accessing information; carrying out transactions and sharing of data on the Cloud, they are connected to diverse computers on the network. As such, security of such transmitted data is most threatened and then potentially creating privacy risks of users on the federated identity management system in the Cloud. Usually, User's attributes or Personal Identifiable Information (PII) are needed to access Services on the Cloud from different Service Providers (SPs). Sometime these SPs may by themselves violate user's privacy by the reuse of user's attributes offered them for the release of services to the users without their consent and then carrying out activities that may appear malicious and then causing damage to the users. Similarly, it should be noted that sensitive user's attributes (e.g. first name, email, address and the likes) are received in their original form by needed SPs in plaintext. As a result of these problems, user's privacy is being violated. Since these SPs may reuse them or connive with other SPs to expose a user's identity in the cloud environment. This research is motivated to provide a protective and novel approach that shall no longer release original user's attributes to SPs but pseudonyms that shall prevent the SPs from violating user's privacy through connivance to expose the user's identity or other means. The paper introduces a conceptual framework for the proposed user's attributes privacy protection in a federated identity management system for the cloud. On the proposed system, the use of pseudonymous technique also called Privacy Token (PT) is employed. The pseudonymous technique ensures users' original attributes values are not sent directly to the SP but auto generated pseudo attributes values. The PT is composed of: Pseudo Attribute values, Timestamp and SPİD. These composition of the PT makes it difficult for the User's PII to be revealed and further preventing the SPs from being able to keep them or reuse them in the future without the user's consent for any purpose. Another important feature of the PT is its ability to forestall collusion among several collaborating service providers. This is due to the fact that each SP receives pseudo values that have no direct link to the identity of the user. The prototype was implemented with Java programming language and its performance tested on CloudAnalyst simulation.

IPTV is capable of providing recommendations for upcoming TV programs based on consumer feedback. With the increasing popularity and performance of recommender systems, risks of user privacy breach emerge. Although several works about privacy-preserving designs of recommender systems exist in the literature, a detailed analysis of the current state-of-the-art regarding privacy as well as an investigation of the usability aspects of such systems, so far, have not received consideration. In this paper, we survey current approaches for recommender systems by studying their privacy and usability properties in the context of IPTV.

This paper provides a proof of concept for using SRAM based Physically Unclonable Functions (PUFs) to generate private keys for IoT devices. PUFs are utilized, as there is inadequate protection for secret keys stored in the memory of the IoT devices. We utilize a custom-made Arduino mega shield to extract the fingerprint from SRAM chip on demand. We utilize the concepts of ternary states to exclude the cells which are easily prone to flip, allowing us to extract stable bits from the fingerprint of the SRAM. Using the custom-made software for our SRAM device, we can control the error rate of the PUF to achieve an adjustable memory-based PUF for key generation. We utilize several fuzzy extractor techniques based on using different error correction coding methods to generate secret keys from the SRAM PUF, and study the trade-off between the false authentication rate and false rejection rate of the PUF.

Mobile ad hoc network is a group of mobile nodes which have no centralized administrator. MANETs have dynamic topology since the nodes are moving. For this reason it is more prone to attacks that any node may be a misbehaving node. Every node acts as a router thereby it may lead the network with wrong routing. For these reasons MANETs have to be more protected than the wired networks. The mobile nodes will lavishly consume energy and so a security scheme that consumes less energy still provides ample protection to the messages have to be introduced. Here we propose an encryption scheme for the messages passing through MANET. The security scheme is based on selective encryption that is very robust, simple and with less computational capability.

In the literature, many chaotic systems have been used in the design of image encryption algorithms. In this study, an application of fractional order chaotic systems is investigated. The aim of the study is to improve the disadvantageous aspects of existing methods based on discrete and continuous time chaotic systems by utilizing the features of fractional order chaotic systems. The most important advantage of the study compared to the literature is that the proposed encryption algorithm is designed with a provable security approach. Analyses results have been shown that the proposed method can be used successfully in many information security applications.

Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution.

This paper introduces a security analysis scheme for the most famous secure hash algorithms SHA-1 and SHA-2. Both algorithms follow Merkle Damgård structure to compute the corresponding hash function. The randomness of the output hash reflects the strength and security of the generated hash. Therefore, the randomness of the internal rounds of the SHA-1 and SHA-2 hash functions is analyzed using Bayesian and odd ratio tests. Moreover, a proper replacement for both algorithms is proposed, which produces a hash output with more randomness level. The experiments were conducted using a high performance computing testbed and CUDA parallel computing platform.

Modern cybercrimes have exponentially grown over the last one decade. Ransomware is one of the types of malware which is the result of sophisticated attempt to compromise the modern computer systems. The governments and large corporations are investing heavily to combat this cyber threat against their critical infrastructure. It has been observed that over the last few years that Industrial Control Systems (ICS) have become the main target of Ransomware due to the sensitive operations involved in the day to day processes of these industries. As the technology is evolving, more and more traditional industrial systems are replaced with advanced industry methods involving advanced technologies such as Internet of Things (IoT). These technology shift help improve business productivity and keep the company's global competitive in an overflowing competitive market. However, the systems involved need secure measures to protect integrity and availability which will help avoid any malfunctioning to their operations due to the cyber-attacks. There have been several cyber-attack incidents on healthcare, pharmaceutical, water cleaning and energy sector. These ICS' s are operated by remote control facilities and variety of other devices such as programmable logic controllers (PLC) and sensors to make a network. Cyber criminals are exploring vulnerabilities in the design of these ICS's to take the command and control of these systems and disrupt daily operations until ransomware is paid. This paper will provide critical analysis of the impact of Ransomware threat on SCADA systems.

Security has been a challenging aspect recently in the field of Web Development. A failure to obtain security in web applications may lead to complete destruction of the web application or may cause some loss to the user or the owner. To tackle this, a huge research on how to secure a web app has been going on for quite some time, yet to achieve security in today's modern era is a very difficult and no less than a challenge for web applications. All these things lead only to a vulnerable/faulty source code, formulated in coding such as PHP. Static Source Code analysis (SCSA) tools tend to give a solution to detect vulnerabilities, but they tend to detect vulnerabilities which actually are false positives, which leads to excess code reexamination. The proposed system will tackle the current situation of SCSA. This will be achieved by two additional modules to SCSA i.e. Taint analysis with False Positive Predictor which will detect and segregate the true vulnerable code from false positives respectively. The proposed system will be used by the Web Application programmers during testing of web application.

Cyber-Physical Systems (CPSs) interconnect the physical world with digital computers and networks in order to automate production and distribution processes. Nowadays, most CPSs do not work in isolation, but their digital part is connected to the Internet in order to enable remote monitoring, control and configuration. Such a connection may offer entry-points enabling attackers to gain control silently and exploit access to the physical world at the right time to cause service disruption and possibly damage to the surrounding environment. Prevention and monitoring measures can reduce the risk brought by cyber attacks, but the residual risk can still be unacceptably high in critical infrastructures or services. Resilience - i.e., the ability of a system to withstand adverse events while maintaining an acceptable functionality - is therefore a key property for such systems. In our research, we seek a model-free, quantitative, and general-purpose evaluation methodology to extract resilience indexes from, e.g., system logs and process data. While a number of resilience metrics have already been put forward, little experimental evidence is available when it comes to the cyber security of CPSs. By using the model of a real wastewater treatment plant, and simulating attacks that tamper with a critical feedback control loop, we provide a comparison between four resilience indexes selected through a thorough literature review involving over 40 papers. Our results show that the selected indexes differ in terms of behavior and sensitivity with respect to specific attacks, but they can all summarize and extract meaningful information from bulky system logs. Our evaluation includes an approach for extracting performance indicators from observed variables which does not require knowledge of system dynamics; and a discussion about combining resilience indexes into a single system-wide measure is included. 11The authors wish to thank Leonardo S.p.A. for its financial support. The research herein presented is partially supported by project NEFERIS awarded by the Italian Ministry of Defense to Leonardo S.p.A. in partnership with the University of Genoa. This work received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 830892 for project SPARTA.

Smart Farming is driven by the emergence of precise positioning systems and Internet of Things technologies which have already enabled site-specific applications, sustainable resource management, and interconnected machinery. Nowadays, so-called Farm Management Information Systems (FMISs) enable farm-internal interconnection of agricultural machines and implements and, thereby, allow in-field data exchange and the orchestration of collaborative agricultural processes. Machine data is often directly logged during task execution. Moreover, interconnection of farms, agricultural contractors, and marketplaces ease the collaboration. However, current FMISs lack in security and particularly in user authentication. In this paper, we present a security architecture for a decentralized, manufacturer-independent, and open-source FMIS. Special attention is turned on the Radio Frequency Identification (RFID)-based continuous user authentication which greatly improves security and credibility of automated documentation, while at the same time preserves usability in practice.

In this work, robust readout of the data (232 English characters) stored in high-security structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGB-pixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, cross-module and cross-channel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above 200) at some typical regions of the images with different lighting conditions, their inconsistencies (below 100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to 300-pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial low-pass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.

Authentication is the fundamental security service used in almost all remote applications. All such sensitive applications over an open network need authentication mechanism that should be delivered in a trusted way. In this paper, we design an RSA based authentication system for smart IoT environment over the air network using state-of-the-art industry standards. Our system provide security services including X.509 certificate, RSA based Public Key Infrastructure (PKI), challenge/response protocols with the help of proxy induced security service provider. We describe an innovative system model, protocol design, system architecture and evaluation against known threats. Also the implemented solution designed as an add on service for multiple other sensitive applications (smart city apps, cyber physical systems etc.) which needs the support of X.509 certificate based on hard tokens to populate other security services including confidentiality, integrity, non-repudiation, privacy and anonymity of the identities. The proposed scheme is evaluated against known vulnerabilities and given detail comparisons with popular known authentication schemes. The result shows that our proposed scheme mitigate all the known security risks and provide highest level assurance to smart gadgets.