Biblio

Cloud Computing is one of the large and essential environment now a days to work for the storage collection and privacy preserve to that data. Cloud data security is most important and major concern for the client while use of the cloud services provided by the different service providers. There can be some major security concern and conflicts between the client and the service provider. To get out from those issues, a third party auditor uses as an auditor for assurance of data in the environment. Storage systems for the cloud has many fundamental challenges still today. All basic as well critical challenges among which storage space and security is generally the top concern in the cloud environment. To give the appropriate security issues we have proposed third party authentication system. The cloud not only for the simplified data storage but also secure data acquisition in cloud environment. At last we have perform different security analysis as well performance analysis. It give the results that proposed scheme has significant increases in efficiency for maintaining highly secure data storage and acquisition. The proposed method also helps to minimize the cost in environment and also increases communication efficiency in the cloud environment.

Online Social Networks exploit a lightweight process to identify their users so as to facilitate their fast adoption. However, such convenience comes at the price of making legitimate users subject to different threats created by fake accounts. Therefore, there is a crucial need to empower users with tools helping them in assigning a level of trust to whomever they interact with. To cope with this issue, in this paper we introduce a novel model, DIVa, that leverages on mining techniques to find correlations among user profile attributes. These correlations are discovered not from user population as a whole, but from individual communities, where the correlations are more pronounced. DIVa exploits a decentralized learning approach and ensures privacy preservation as each node in the OSN independently processes its local data and is required to know only its direct neighbors. Extensive experiments using real-world OSN datasets show that DIVa is able to extract fine-grained community-aware correlations among profile attributes with average improvements up to 50% than the global approach.

The new era of information communication and technology (ICT), everyone wants to store/share their Data or information in online media, like in cloud database, mobile database, grid database, drives etc. When the data is stored in online media the main problem is arises related to data is privacy because different types of hacker, attacker or crackers wants to disclose their private information as publically. Security is a continuous process of protecting the data or information from attacks. For securing that information from those kinds of unauthorized people we proposed and implement of one the technique based on the data modification concept with taking the iris database on weka tool. And this paper provides the high privacy in distributed clustered database environments.

With the growing observed success of big data use, many challenges appeared. Timeless, scalability and privacy are the main problems that researchers attempt to figure out. Privacy preserving is now a highly active domain of research, many works and concepts had seen the light within this theme. One of these concepts is the de-identification techniques. De-identification is a specific area that consists of finding and removing sensitive information either by replacing it, encrypting it or adding a noise to it using several techniques such as cryptography and data mining. In this report, we present a new model of de-identification of textual data using a specific Immune System algorithm known as CLONALG.

Cyber-physical systems (CPSs), due to their direct influence on the physical world, have to meet extended security and dependability requirements. This is particularly true for CPS that operate in close proximity to humans or that control resources that, when tampered with, put all our lives at stake. In this paper, we review the challenges and some early solutions that arise at the architectural and operating-system level when we require cyber-physical systems and CPS infrastructure to withstand advanced and persistent threats. We found that although some of the challenges we identified are already matched by rudimentary solutions, further research is required to ensure sustainable and dependable operation of physically exposed CPS infrastructure and, more importantly, to guarantee graceful degradation in case of malfunction or attack.

In our previous work [1], we presented a study of using performance escalation to automatic detect Distributed Denial of Service (DDoS) types of attacks. We propose to enhance the work of security threat detection by using mobile phones as the detector to identify outliers of normal traffic patterns as threats. The mobile solution makes detection portable to any services. This paper also shows that the same detection method works for advanced persistent threats.

Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

Physical perturbations are performed against embedded systems that can contain valuable data. Such devices and in particular smart cards are targeted because potential attackers hold them. The embedded system security must hold against intentional hardware failures that can result in software errors. In a malicious purpose, an attacker could exploit such errors to find out secret data or disrupt a transaction. Simulation techniques help to point out fault injection vulnerabilities and come at an early stage in the development process. This paper proposes a generic fault injection simulation tool that has the particularity to embed the injection mechanism into the smart card source code. By its embedded nature, the Embedded Fault Simulator (EFS) allows us to perform fault injection simulations and side-channel analyses simultaneously. It makes it possible to achieve combined attacks, multiple fault attacks and to perform backward analyses. We appraise our approach on real, modern and complex smart card systems under data and control flow fault models. We illustrate the EFS capacities by performing a practical combined attack on an Advanced Encryption Standard (AES) implementation.

This paper considers the two-user interference relay channel where each source wishes to communicate to its destination a message that is confidential from the other destination. Furthermore, the relay, that is the enabler of communication, due to the absence of direct links, is untrusted. Thus, the messages from both sources need to be kept secret from the relay as well. We provide an achievable secure rate region for this network. The achievability scheme utilizes structured codes for message transmission, cooperative jamming and scaled compute-and-forward. In particular, the sources use nested lattice codes and stochastic encoding, while the destinations jam using lattice points. The relay decodes two integer combinations of the received lattice points and forwards, using Gaussian codewords, to both destinations. The achievability technique provides the insight that we can utilize the untrusted relay node as an encryption block in a two-hop interference relay channel with confidential messages.

Turbo code has been one of the important subjects in coding theory since 1993. This code has low Bit Error Rate (BER) but decoding complexity and delay are big challenges. On the other hand, considering the complexity and delay of separate blocks for coding and encryption, if these processes are combined, the security and reliability of communication system are guaranteed. In this paper a secure decoding algorithm in parallel on General-Purpose Graphics Processing Units (GPGPU) is proposed. This is the first prototype of a fast and parallel Joint Channel-Security Coding (JCSC) system. Despite of encryption process, this algorithm maintains desired BER and increases decoding speed. We considered several techniques for parallelism: (1) distribute decoding load of a code word between multiple cores, (2) simultaneous decoding of several code words, (3) using protection techniques to prevent performance degradation. We also propose two kinds of optimizations to increase the decoding speed: (1) memory access improvement, (2) the use of new GPU properties such as concurrent kernel execution and advanced atomics to compensate buffering latency.

Security in mobile handsets of telecommunication standards such as GSM, Project 25 and TETRA is very important, especially when governments and military forces use handsets and telecommunication devices. Although telecommunication could be quite secure by using encryption, coding, tunneling and exclusive channel, attackers create new ways to bypass them without the knowledge of the legitimate user. In this paper we introduce a new, simple and economical circuit to warn the user in cases where the message is not encrypted because of manipulation by attackers or accidental damage. This circuit not only consumes very low power but also is created to sustain telecommunication devices in aspect of security and using friendly. Warning to user causes the best practices of telecommunication devices without wasting time and energy for fault detection.

Secret key establishment is considered to be one of the main challenging issues in cryptography. Many security algorithms are implemented in practice using complicated mathematical methods to exchange secret keys, but those methods are not desirable in power limited terminals such as cellular and sensor networks. In this paper, we propose a physical layer method for exchanging secret key bits in precoding based multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed method uniquely relates the key bits to the indices of the precoding matrix used for MIMO channel precoding. The basic idea of the technique is to utilize a MIMO-OFDM precoding codebook. Comparative analysis with respect to the average number of mismatch bits, named key error rate (KER), shows an interesting lead for the new method relative to existing work. In addition, it will be shown that the proposed technique requires lower computation per byte per secret key.

Demand ResponseManagement (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists.We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies.We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.

The migration of many current critical infrastructures, such as power grids and transportations systems, into open publicnetworks has posed many challenges in control systems. Modern control systems face uncertainties not only from the physical world but also from the cyber space. In this paper, we propose a hybrid game-theoretic approach to investigate the coupling between cyber security policy and robust control design. We study in detail the case of cascading failures in industrial control systems and provide a set of coupled optimality criteria in the linear-quadratic case. This approach can be further extended to more general cases of parallel cascading failures.

Phishing is a social engineering tactic used to trick people into revealing personal information [Zielinska, Tembe, Hong, Ge, Murphy-Hill, & Mayhorn 2014]. As phishing emails continue to infiltrate users' mailboxes, what social engineering techniques are the phishers using to successfully persuade victims into releasing sensitive information?

Cialdini's [2007] six principles of persuasion (authority, social proof, liking/similarity, commitment/consistency, scarcity, and reciprocation) have been linked to elements of phishing emails [Akbar 2014; Ferreira, & Lenzini 2015]; however, the findings have been conflicting. Authority and scarcity were found as the most common persuasion principles in 207 emails obtained from a Netherlands database [Akbar 2014], while liking/similarity was the most common principle in 52 personal emails available in Luxemborg and England [Ferreira et al. 2015]. The purpose of this study was to examine the persuasion principles present in emails available in the United States over a period of five years.

Two reviewers assessed eight hundred eighty-seven phishing emails from Arizona State University, Brown University, and Cornell University for Cialdini's six principles of persuasion. Each email was evaluated using a questionnaire adapted from the Ferreira et al. [2015] study. There was an average agreement of 87% per item between the two raters.

Spearman's Rho correlations were used to compare email characteristics over time. During the five year period under consideration (2010--2015), the persuasion principles of commitment/consistency and scarcity have increased over time, while the principles of reciprocation and social proof have decreased over time. Authority and liking/similarity revealed mixed results with certain characteristics increasing and others decreasing.

The commitment/consistency principle could be seen in the increase of emails referring to elements outside the email to look more reliable, such as Google Docs or Adobe Reader (rs(850) = .12, p =.001), while the scarcity principle could be seen in urgent elements that could encourage users to act quickly and may have had success in eliciting a response from users (rs(850) = .09, p =.01). Reciprocation elements, such as a requested reply, decreased over time (rs(850) = -.12, p =.001). Additionally, the social proof principle present in emails by referring to actions performed by other users also decreased (rs(850) = -.10, p =.01).

Two persuasion principles exhibited both an increase and decrease in their presence in emails over time: authority and liking/similarity. These principles could increase phishing rate success if used appropriately, but could also raise suspicions in users and decrease compliance if used incorrectly. Specifically, the source of the email, which corresponds to the authority principle, displayed an increase over time in educational institutes (rs(850) = .21, p <.001), but a decrease in financial institutions (rs(850) = -.18, p <.001). Similarly, the liking/similarity principle revealed an increase over time of logos present in emails (rs(850) = .18, p <.001) and decrease in service details, such as payment information (rs(850) = -.16, p <.001).

The results from this study offer a different perspective regarding phishing. Previous research has focused on the user aspect; however, few studies have examined the phisher perspective and the social psychological techniques they are implementing. Additionally, they have yet to look at the success of the social psychology techniques. Results from this study can be used to help to predict future trends and inform training programs, as well as machine learning programs used to identify phishing messages.

Hadoop has become increasingly popular as it rapidly processes data in parallel. Cloud computing gives reliability, flexibility, scalability, elasticity and cost saving to cloud users. Deploying Hadoop in cloud can benefit Hadoop users. Our evaluation exhibits that various internal cloud attacks can bypass current Hadoop security mechanisms, and compromised Hadoop components can be used to threaten overall Hadoop. It is urgent to improve compromise resilience, Hadoop can maintain a relative high security level when parts of Hadoop are compromised. Hadoop has two vulnerabilities that can dramatically impact its compromise resilience. The vulnerabilities are the overloaded authentication key, and the lack of fine-grained access control at the data access level. We developed a security enhancement for a public cloud-based Hadoop, named SEHadoop, to improve the compromise resilience through enhancing isolation among Hadoop components and enforcing least access privilege for Hadoop processes. We have implemented the SEHadoop model, and demonstrated that SEHadoop fixes the above vulnerabilities with minimal or no run-time overhead, and effectively resists related attacks.

Session management in distributed Internet services is traditionally based on username and password, explicit logouts and mechanisms of user session expiration using classic timeouts. Emerging biometric solutions allow substituting username and password with biometric data during session establishment, but in such an approach still a single verification is deemed sufficient, and the identity of a user is considered immutable during the entire session. Additionally, the length of the session timeout may impact on the usability of the service and consequent client satisfaction. This paper explores promising alternatives offered by applying biometrics in the management of sessions. A secure protocol is defined for perpetual authentication through continuous user verification. The protocol determines adaptive timeouts based on the quality, frequency and type of biometric data transparently acquired from the user. The functional behavior of the protocol is illustrated through Matlab simulations, while model-based quantitative analysis is carried out to assess the ability of the protocol to contrast security attacks exercised by different kinds of attackers. Finally, the current prototype for PCs and Android smartphones is discussed.
 

Wireless security has been an active research area since the last decade. A lot of studies of wireless security use cryptographic tools, but traditional cryptographic tools are normally based on computational assumptions, which may turn out to be invalid in the future. Consequently, it is very desirable to build cryptographic tools that do not rely on computational assumptions. In this paper, we focus on a crucial cryptographic tool, namely 1-out-of-2 oblivious transfer. This tool plays a central role in cryptography because we can build a cryptographic protocol for any polynomial-time computable function using this tool. We present a novel 1-out-of-2 oblivious transfer protocol based on wireless channel characteristics, which does not rely on any computational assumption. We also illustrate the potential broad applications of this protocol by giving two applications, one on private communications and the other on privacy preserving password verification. We have fully implemented this protocol on wireless devices and conducted experiments in real environments to evaluate the protocol. Our experimental results demonstrate that it has reasonable efficiency.
 

Remote data integrity checking is of crucial importance in cloud storage. It can make the clients verify whether their outsourced data is kept intact without downloading the whole data. In some application scenarios, the clients have to store their data on multicloud servers. At the same time, the integrity checking protocol must be efficient in order to save the verifier's cost. From the two points, we propose a novel remote data integrity checking model: ID-DPDP (identity-based distributed provable data possession) in multicloud storage. The formal system model and security model are given. Based on the bilinear pairings, a concrete ID-DPDP protocol is designed. The proposed ID-DPDP protocol is provably secure under the hardness assumption of the standard CDH (computational Diffie-Hellman) problem. In addition to the structural advantage of elimination of certificate management, our ID-DPDP protocol is also efficient and flexible. Based on the client's authorization, the proposed ID-DPDP protocol can realize private verification, delegated verification, and public verification.
 

For efficient deployment of sensor nodes required in many logistic applications, it's necessary to build security mechanisms for a secure wireless communication. End-to-end security plays a crucial role for the communication in these networks. This provides the confidentiality, the authentication and mostly the prevention from many attacks at high level. In this paper, we propose a lightweight key exchange protocol WSKE (Wireless Sensor Key Exchange) for IP-based wireless sensor networks. This protocol proposes techniques that allows to adapt IKEv2 (Internet Key Exchange version 2) mechanisms of IPSEC/6LoWPAN networks. In order to check these security properties, we have used a formal verification tools called AVISPA.
 

The concept of smart cities envisions services that provide distraction-free support for citizens. To realize this vision, the services must adapt to the citizens' situations, behaviors and intents at runtime. This requires services to gather and process the context of their users. Mobile devices provide a promising basis for determining context in an automated manner on a large scale. However, despite the wide availability of versatile programmable mobile platforms such as Android and iOS, there are only few examples of smart city applications. One reason for this is that existing software platforms primarily focus on low-level resource management which requires application developers to repeatedly tackle many challenging tasks. Examples include efficient data acquisition, secure and privacy-preserving data distribution as well as interoperable data integration. In this paper, we describe the GAMBAS middleware which tries to simplify the development of smart city applications. To do this, GAMBAS introduces a Java-based runtime system with an associated software development kit (SDK). To clarify how the runtime system and the SDK can be used for application development, we describe two simple applications that highlight different middleware functions.

This paper proposes a service operator-aware trust scheme (SOTS) for resource matchmaking across multiple clouds. Through analyzing the built-in relationship between the users, the broker, and the service resources, this paper proposes a middleware framework of trust management that can effectively reduces user burden and improve system dependability. Based on multidimensional resource service operators, we model the problem of trust evaluation as a process of multi-attribute decision-making, and develop an adaptive trust evaluation approach based on information entropy theory. This adaptive approach can overcome the limitations of traditional trust schemes, whereby the trusted operators are weighted manually or subjectively. As a result, using SOTS, the broker can efficiently and accurately prepare the most trusted resources in advance, and thus provide more dependable resources to users. Our experiments yield interesting and meaningful observations that can facilitate the effective utilization of SOTS in a large-scale multi-cloud environment.

Mobile Apps running on smartphones and tablet pes offer a new possibility to enhance the work of engineers because they provide an easy-to-use, touchscreen-based handling and can be used anytime and anywhere. Introducing mobile apps in the engineering domain is difficult because the IT environment is heterogeneous and engineering-specific challenges in the app development arise e. g., large amount of data and high security requirements. There is a need for an engineering-specific middleware to facilitate and standardize the app development. However, such a middleware does not yet exist as well as a holistic set of requirements for the development. Therefore, we propose a design method which offers a systematic procedure to develop Mobile Engineering-Application Middleware.

Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.