Biblio

Nowadays database outsourcing has become business owners' preferred option and they are benefiting from its flexibility, reliability, and low cost. However, because database service providers cannot always be fully trusted and data owners will no longer have a direct control over their own data, how to make the outsourced data secure becomes a hot research topic. From the data integrity protection aspect, the client wants to make sure the data returned is correct, complete, and up-to-date. Previous research work in literature put more efforts on data correctness, while data completeness is still a challenging problem to solve. There are some existing works that tried to protect the completeness of data. Unfortunately, these solutions were considered not fully solving the problem because of their high communication or computation overhead. The implementations and limitations of existing works will be further discussed in this paper. From the data confidentiality protection aspect, order-preserving encryption (OPE) is a widely used encryption scheme in protecting data confidentiality. It allows the client to perform range queries and some other operations such as GROUP BY and ORDER BY over the OPE encrypted data. Therefore, it is worthy to develop a solution that allows user to verify the query completeness for an OPE encrypted database so that both data confidentiality and completeness are both protected. Inspired by this motivation, we propose a new data completeness protecting scheme by inserting fake tuples into databases. Both the real and fake tuples are OPE encrypted and thus the cloud server cannot distinguish among them. While our new scheme is much more efficient than all existing approaches, the level of security protection remains the same.

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots (attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. Firstly, this paper analyzes how secure current audio captchas are from attacks using machine learning (ML) and deep learning (DL) models. Each audio captcha is made up of five, seven or ten random digits[0-9] spoken one after the other along with varying background noise throughout the length of the audio. If the ML or DL model is able to correctly identify all spoken digits and in the correct order of occurance in a single audio captcha, we consider that captcha to be broken and the attack to be successful. Throughout the paper, accuracy refers to the attack model's success at breaking audio captchas. The higher the attack accuracy, the more unsecure the audio captchas are. In our baseline experiments, we found that attack models could break audio captchas that had no background noise or medium background noise with any number of spoken digits with nearly 99% to 100% accuracy. Whereas, audio captchas with high background noise were relatively more secure with attack accuracy of 85%. Secondly, we propose that the concepts of adversarial examples algorithms can be used to create a new kind of audio captcha that is more resilient towards attacks. We found that even after retraining the models on the new adversarial audio data, the attack accuracy remained as low as 25% to 36% only. Lastly, we explore the benefits of creating adversarial audio captcha through different algorithms such as Basic Iterative Method (BIM) and deepFool. We found that as long as the attacker has less than 45% sample from each kinds of adversarial audio datasets, the defense will be successful at preventing attacks.

In this work, we will present a new hybrid cryptography method based on two hard problems: 1- The problem of the discrete logarithm on an elliptic curve defined on a finite local ring. 2- The closest vector problem in lattice and the conjugate problem on square matrices. At first, we will make the exchange of keys to the Diffie-Hellman. The encryption of a message is done with a bad basis of a lattice.

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

Scientific experiments and observations store massive amounts of data in various scientific file formats. Metadata, which describes the characteristics of the data, is commonly used to sift through massive datasets in order to locate data of interest to scientists. Several indexing data structures (such as hash tables, trie, self-balancing search trees, sparse array, etc.) have been developed as part of efforts to provide an efficient method for locating target data. However, efficient determination of an indexing data structure remains unclear in the context of scientific data management, due to the lack of investigation on metadata, metadata queries, and corresponding data structures. In this study, we perform a systematic study of the metadata search essentials in the context of scientific data management. We study a real-world astronomy observation dataset and explore the characteristics of the metadata in the dataset. We also study possible metadata queries based on the discovery of the metadata characteristics and evaluate different data structures for various types of metadata attributes. Our evaluation on real-world dataset suggests that trie is a suitable data structure when prefix/suffix query is required, otherwise hash table should be used. We conclude our study with a summary of our findings. These findings provide a guideline and offers insights in developing metadata indexing methodologies for scientific applications.

Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.

This paper reports the definition, setup and obtained results of the Fed4FIRE + medium experiment ERASER, aimed to evaluate the actual Quality of Service (QoS) guarantees that the clean-slate Recursive InterNetwork Architecture (RINA) can deliver to heterogeneous applications at large-scale. To this goal, a 37-Node 5G metro/regional RINA network scenario, spanning from the end-user to the server where applications run in a datacenter has been configured in the Virtual Wall experimentation facility. This scenario has initially been loaded with synthetic application traffic flows, with diverse QoS requirements, thus reproducing different network load conditions. Next,their experienced QoS metrics end-to-end have been measured with two different QTA-Mux (i.e., the most accepted candidate scheduling policy for providing RINA with its QoS support) deployment scenarios. Moreover, on this RINA network scenario loaded with synthetic application traffic flows, a real HD (1080p) video streaming demonstration has also been conducted, setting up video streaming sessions to end-users at different network locations, illustrating the perceived Quality of Experience (QoE). Obtained results in ERASER disclose that, by appropriately deploying and configuring QTA-Mux, RINA can yield effective QoS support, which has provided perfect QoE in almost all locations in our demo when assigning video traffic flows the highest (i.e., Gold) QoS Cube.

We propose an efficient and secure two-server password-only remote user authentication protocol for consumer electronic devices, such as smartphones and laptops. Our protocol works on-top of any existing trust model, like Secure Sockets Layer protocol (SSL). The proposed protocol is secure against dictionary and impersonation attacks.

A noble multi-factor authentication (MFA) algorithm is developed for the security enhancement of the Cryptocurrency (CR). The main goal of MFA is to set up extra layer of safeguard while seeking access to a targets such as physical location, computing device, network or database. MFA security scheme requires more than one method for the validation from commutative family of credentials to verify the user for a transaction. MFA can reduce the risk of using single level password authentication by introducing additional factors of authentication. MFA can prevent hackers from gaining access to a particular account even if the password is compromised. The superfluous layer of security introduced by MFA offers additional security to a user. MFA is implemented by using time-based onetime password (TOTP) technique. For logging to any entity with MFA enabled, the user first needs username and password, as a second factor, the user then needs the MFA token to virtually generate a TOTP. It is found that MFA can provide a better means of secured transaction of CR.

Being the revolutionary future networking architecture, information-centric networking (ICN) conducts network distribution based on content, which is ideally suitable for Internet of things (IoT). With the rapid growth of network traffic, compared to the conventional IoT, information-centric Internet of things (IC-IoT) is expected to provide users with the better satisfaction of the network quality of service (QoS). However, due to IC-IoT requirements of low latency, large data volume, marginalization, and intelligent processing, it urgently needs an efficient content distribution system. In this paper, we propose an edge learning based green content distribution scheme for IC-IoT. We implement intelligent path selection based on decision tree and edge calculation. Moreover, we apply distributed coding based content transmission to enhance the speed and recovery capability of content. Meanwhile, we have verified the effectiveness and performance of this scheme based on a large number of simulation experiments. The work of this paper is of great significance to improve the efficiency and flexibility of content distribution in IC-IoT.

Resilience and security of infrastructures depend not only on their constituent systems but also on interdependencies among them. This paper studies how these interdependencies in infrastructures affect the defense effort needed to counter external attacks, by formulating a simultaneous game between a service provider (i.e., defender) and an attacker. Effects of interdependencies in three basic topological structures, namely, bus, star and ring, are considered and compared in terms of the game-theoretic defense strategy. Results show that in a star topology, the attacker's and defender's pure strategies at Nash Equilibrium (NE) are sensitive to interdependency levels whereas in a bus structure, the interdependencies show little impact on both defender's and attacker's pure strategies. The sensitivity estimates of defense and attack strategies at NE with respect to target valuation and unit cost are also presented. The results provide insights into infrastructure design and resource allocation for reinforcement of constituent systems.

Given a code used to send a message to two receivers through a degraded discrete memoryless broadcast channel (DM-BC), the sender wishes to alter the codewords to achieve the following goals: (i) the original broadcast communication continues to take place, possibly at the expense of a tolerable increase of the decoding error probability; and (ii) an additional covert message can be transmitted to the stronger receiver such that the weaker receiver cannot detect the existence of this message. The main results are: (a) feasibility of covert communications is proven by using a random coding argument for general DM-BCs; and (b) necessary conditions for establishing covert communications are described and an impossibility (converse) result is presented for a particular class of DM-BCs. Together, these results characterize the asymptotic fundamental limits of covert communications for this particular class of DM-BCs within an arbitrarily small gap.

How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. This evaluation technique requires the aggregation of software code metrics into project metrics. Statistical value methods, metric distribution methods, and econometric methods are commonly-used aggregation methods. What are the differences between these methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability assessment model we have established are our concerns. In view of these concerns, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects. We find the distribution of code metrics for the projects under study. Using these distribution laws, we optimize the aggregation method of code metrics and improve the accuracy of the software reliability evaluation model.

Recently, several health care wearable devices which can intervene in health and collect personal health data have emerged in the medical market. Although health care wearable devices promote the integration of multi-layer medical resources and bring new ways of health applications for users, it is inevitable that some problems will be brought. This is mainly manifested in the safety protection of medical and health data and the protection of user's privacy. From the users' point of view, the irrational use of medical and health data may bring psychological and physical negative effects to users. From the government's perspective, it may be sold by private businesses in the international arena and threaten national security. The most direct precaution against the problem is users' initiative. For better understanding, a research model is designed by the following five aspects: Security knowledge (SK), Security attitude (SAT), Security practice (SP), Security awareness (SAW) and Security conduct (SC). To verify the model, structural equation analysis which is an empirical approach was applied to examine the validity and all the results showed that SK, SAT, SP, SAW and SC are important factors affecting users' data security and privacy protection awareness.

We propose an approach for allowing data owners to trade their data in digital data market scenarios, while keeping control over them. Our solution is based on a combination of selective encryption and smart contracts deployed on a blockchain, and ensures that only authorized users who paid an agreed amount can access a data item. We propose a safe interaction protocol for regulating the interplay between a data owner and subjects wishing to purchase (a subset of) her data, and an audit process for counteracting possible misbehaviors by any of the interacting parties. Our solution aims to make a step towards the realization of data market platforms where owners can benefit from trading their data while maintaining control.

Nowadays mobile devices have become the majority terminals used by people for social activities so that carrying business data and private information in them have become normal. Accordingly, the risk of data related cyber attacks has become one of the most critical security concerns. The main purpose of this work is to mitigate the risk of data breaches and damages caused by malware and the lost of mobile devices. In this paper, we propose an encrypted QR code based optical challenge-response authentication by mobile devices for mounting concealed file systems. The concealed file system is basically invisible to the users unless being successfully mounted. The proposed authentication scheme practically applies cryptography and QR code technologies to challenge-response scheme in order to secure the concealed file system. The key contribution of this work is to clarify a possibility of a mounting authentication scheme involving two mobile devices using a special optical communication way (QR code exchanges) which can be realizable without involving any network accesses. We implemented a prototype system and based on the preliminary feature evaluations results we confirmed that encrypted QR code based optical challenge-response is possible between a laptop and a smart phone and it can be applied to authentication for mounting concealed file systems.

Automatic emotion recognition using computer vision is significant for many real-world applications like photojournalism, virtual reality, sign language recognition, and Human Robot Interaction (HRI) etc., Psychological research findings advocate that humans depend on the collective visual conduits of face and body to comprehend human emotional behaviour. Plethora of studies have been done to analyse human emotions using facial expressions, EEG signals and speech etc., Most of the work done was based on single modality. Our objective is to efficiently integrate emotions recognized from facial expressions and upper body pose of humans using images. Our work on bimodal emotion recognition provides the benefits of the accuracy of both the modalities.

The early discovery of security bugs in JavaScript (JS) engines is crucial for protecting Internet users from adversaries abusing zero-day vulnerabilities. Browser vendors, bug bounty hunters, and security researchers have been eager to find such security bugs by leveraging state-of-the-art fuzzers as well as their domain expertise. They report a bug when observing a crash after executing their JS test since a crash is an early indicator of a potential bug. However, it is difficult to identify whether such a crash indeed invokes security bugs in JS engines. Thus, unskilled bug reporters are unable to assess the security severity of their new bugs with JS engine crashes. Today, this classification of a reported security bug is completely manual, depending on the verdicts from JS engine vendors. We investigated the feasibility of applying various machine learning classifiers to determine whether an observed crash triggers a security bug. We designed and implemented CRScope, which classifies security and non-security bugs from given crash-dump files. Our experimental results on 766 crash instances demonstrate that CRScope achieved 0.85, 0.89, and 0.93 Area Under Curve (AUC) for Chakra, V8, and SpiderMonkey crashes, respectively. CRScope also achieved 0.84, 0.89, and 0.95 precision for Chakra, V8, and SpiderMonkey crashes, respectively. This outperforms the previous study and existing tools including Exploitable and AddressSanitizer. CRScope is capable of learning domain-specific expertise from the past verdicts on reported bugs and automatically classifying JS engine security bugs, which helps improve the scalable classification of security bugs.

In recent years, cyber attack techniques are increasingly sophisticated, and blocking the attack is more and more difficult, even if a kind of counter measure or another is taken. In order for a successful handling of this situation, it is crucial to have a prediction of cyber attacks, appropriate precautions, and effective utilization of cyber intelligence that enables these actions. Malicious hackers share various kinds of information through particular communities such as the dark web, indicating that a great deal of intelligence exists in cyberspace. This paper focuses on forums on the dark web and proposes an approach to extract forums which include important information or intelligence from huge amounts of forums and identify traits of each forum using methodologies such as machine learning, natural language processing and so on. This approach will allow us to grasp the emerging threats in cyberspace and take appropriate measures against malicious activities.

The increasing integration of information and communication technologies has undoubtedly boosted the efficiency of Critical Infrastructures (CI). However, the first wave of IoT devices, together with the management of enormous amount of data generated by modern CIs, has created serious architectural issues. While the emerging Fog and Multi-Access Edge Computing (FMEC) paradigms can provide a viable solution, they also bring inherent security issues, that can cause dire consequences in the context of CIs. In this paper, we analyze the applications of FMEC solutions in the context of CIs, with a specific focus on related security issues and threats for the specific while broad scenarios: a smart airport, a smart port, and a smart offshore oil and gas extraction field. Leveraging these scenarios, a set of general security requirements for FMEC is derived, together with crucial research challenges whose further investigation is cornerstone for a successful adoption of FMEC in CIs.

In recent years, users may store their Personal Identifiable Information (PII) in the Cloud environment so that Cloud services may access and use it on demand. When users do not store personal data in their local machines, but in the Cloud, they may be interested in questions such as where their data are, who access it except themselves. Even if Cloud services specify privacy policies, we cannot guarantee that they will follow their policies and will not transfer user data to another party. In the past 10 years, many efforts have been taken in protecting PII. They target certain issues but still have limitations. For instance, users require interacting with the services over the frontend, they do not protect identity propagation between intermediaries and against an untrusted host, or they require Cloud services to accept a new protocol. In this paper, we propose a broader approach that covers all the above issues. We prove that our solution is efficient: the implementation can be easily adapted to existing Identity Management systems and the performance is fast. Most importantly, our approach is compliant with the General Data Protection Regulation from the European Union.

The paper considers, proposes and describes possibilities and ways for application, design and implementation of energy efficient microprocessor based embedded systems for LED lighting control in the traffic. Using LED lighting technology and appropriate designed embedded systems it is possible to implement very efficient and smart systems for very wide range of applications in the traffic. This type of systems can be widely used in many places in the traffic where there is needed quality lighting and low energy consumption. Application of such systems enables to increase energy consumption efficiency, quality of lighting and security of traffic and to decrease total costs for the lighting. Way of design and use of such digital embedded system to effectively increase functionality and efficiency of lighting in the traffic is proposed and described. It is also proposed and described one practically designed and implemented simple and universal embedded system for LED lighting control for many applications in the traffic.

Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.

Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.

The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient.