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Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? Best ieee project center chennai | Best ieee project center chennai | Best ieee project center chennai
Large-scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in-packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.
Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.
This paper considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial top-k queries which ask for the POIs in a certain region and with the highest k ratings for an interested POI attribute. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. This paper presents three novel schemes for users to detect fake spatial snapshot and moving top-k query results as an effort to foster the practical deployment and use of the proposed system. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.
Most anomaly detection systems rely on machine learning algorithms to derive a model of normality that is later used to detect suspicious events. Some works conducted over the last years have pointed out that such algorithms are generally susceptible to deception, notably in the form of attacks carefully constructed to evade detection. Various learning schemes have been proposed to overcome this weakness. One such system is Keyed IDS (KIDS), introduced at DIMVA “10. KIDS” core idea is akin to the functioning of some cryptographic primitives, namely to introduce a secret element (the key) into the scheme so that some operations are infeasible without knowing it. In KIDS the learned model and the computation of the anomaly score are both key-dependent, a fact which presumably prevents an attacker from creating evasion attacks. In this work we show that recovering the key is extremely simple provided that the attacker can interact with KIDS and get feedback about probing requests.
In this paper, we present a security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive components. SPE allows access to these components in a way that allows the framework to ensure the application is truthful in its declared intent and ensure that the user’s policy is enforced. In our evaluation we verify the correctness of the framework and the computing impact on the device. Additionally, we discovered security and privacy issues in several open source applications by utilizing the SPE Framework. From our findings, if SPE is adopted by mobile operating systems producers, it would provide consumers and businesses the additional privacy and security controls they demand and allow users to be more aware of security and privacy issues with applications on their devices.
We address the problem of jamming-resistant broadcast communications under an internal threat model. We propose a time–delayed broadcast scheme (TDBS), which implements the broadcastoperation as a series of unicast transmissions distributed in frequency and time. TDBS does not rely on commonly shared secrets, or the existence of jamming-immune control channels for coordinatingbroadcasts. Instead, each node follows a unique pseudo-noise (PN) frequency hopping sequence. Contrary to conventional PN sequences designed for multi-access systems, the PN sequences in TDBS exhibit correlation to enable broadcast. Moreover, they are designed to limit the information leakage due to the exposure of a subset of sequences by compromised nodes. We map the problem of constructing such PN sequences to the 1-factorization problem for complete graphs. We further accommodate dynamic broadcast groups by mapping the problem of updating the assigned PN sequences to the problem of constructing rainbow paths in proper edge-colored graphs.
K-anonymity has been used to protect location privacy for location monitoring services in wirelesssensor networks (WSNs), where sensor nodes work together to report k-anonymized aggregatelocations to a server. Each k-anonymized aggregate location is a cloaked area that contains at least k persons. However, we identify an attack model to show that overlapping aggregate locations still poseprivacy risks because an adversary can infer some overlapping areas with less than k persons that violates the k-anonymity privacy requirement. In this paper, we propose a reciprocal protocol forlocation privacy (REAL) in WSNs. In REAL, sensor nodes are required to autonomously organize their sensing areas into a set of non-overlapping and highly accurate k-anonymized aggregate locations. To confront the three key challenges in REAL, namely, self-organization, reciprocity property and high accuracy, we design a state transition process, a locking mechanism and a time delay mechanism, respectively. We compare the performance of REAL with current protocols through simulated experiments. The results show that REAL protects location privacy, provides more accurate query answers, and reduces communication and computational costs.
Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine–grained multi–keyword search schemes over encryptedcloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi–keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub–dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.
Development of authorization mechanisms for secure information access by a large community ofusers in an open environment is an important problem in the ever-growing Internet world. In this paper we propose a computational dynamic trust model for user authorization, rooted in findings from social science. Unlike most existing computational trust models, this model distinguishes trusting belief in integrity from that in competence in different contexts and accounts for subjectivity in the evaluation of a particular trustee by different trusters. Simulation studies were conducted to compare the performance of the proposed integrity belief model with other trust models from the literature for different userbehavior patterns. Experiments show that the proposed model achieves higher performance than othermodels especially in predicting the behavior of unstable users.
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 theuser. 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.
With rapid technological advancements, cloud marketplace witnessed frequent emergence of newservice providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services tostore and process their clients’ data. If loss of data occurs due to an outage, the customer’s business gets affected. Therefore, the major challenge for a customer is to select an appropriate service providerto ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence toestimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider‘s SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.
Anonymous fingerprint has been suggested as a convenient solution for the legal distribution ofmultimedia contents with copyright protection whilst preserving the privacy of buyers, whose identities are only revealed in case of illegal re-distribution. However, most of the existing anonymousfingerprinting protocols are impractical for two main reasons: 1) the use of complex time-consuming protocols and/or homomorphic encryption of the content, and 2) a unicast approach for distribution that does not scale for a large number of buyers. This paper stems from a previous proposal of recombinedfingerprints which overcomes some of these drawbacks. However, the recombined fingerprintapproach requires a complex graph search for traitor tracing, which needs the participation of other buyers, and honest proxies in its P2P distribution scenario. This paper focuses on removing these disadvantages resulting in an efficient, scalable, privacy–preserving and P2P–based fingerprintingsystem.