Biblio

User authentication depends largely on the concept of passwords. However, users find it difficult to remember alphanumerical passwords over time. When user is required to choose a secure password, they tend to choose an easy, short and insecure password. Graphical password method is proposed as an alternative solution to text-based alphanumerical passwords. The reason of such proposal is that human brain is better in recognizing and memorizing pictures compared to traditional alphanumerical string. Therefore, in this paper, we propose a conceptual framework to better understand the user performance for new high-end graphical password method. Our proposed framework is based on hybrid approach combining different features into one. The user performance experimental analysis pointed out the effectiveness of the proposed framework.
 

This paper proposes an enhanced method for personal authentication based on finger Knuckle Print using Kekre's wavelet transform (KWT). Finger-knuckle-print (FKP) is the inherent skin patterns of the outer surface around the phalangeal joint of one's finger. It is highly discriminable and unique which makes it an emerging promising biometric identifier. Kekre's wavelet transform is constructed from Kekre's transform. The proposed system is evaluated on prepared FKP database that involves all categories of FKP. The total database of 500 samples of FKP. This paper focuses the different image enhancement techniques for the pre-processing of the captured images. The proposed algorithm is examined on 350 training and 150 testing samples of database and shows that the quality of database and pre-processing techniques plays important role to recognize the individual. The experimental result calculate the performance parameters like false acceptance rate (FAR), false rejection rate (FRR), True Acceptance rate (TAR), True rejection rate (TRR). The tested result demonstrated the improvement in EER (Error Equal Rate) which is very much important for authentication. The experimental result using Kekre's algorithm along with image enhancement shows that the finger knuckle recognition rate is better than the conventional method.
 

Cloud computing is an application and set of services given through the internet. However it is an emerging technology for shared infrastructure but it lacks with an access rights and security mechanism. As it lacks security issues for the cloud users our system focuses only on the security provided through the token management system. It is based on the internet where computing is done through the virtual shared servers for providing infrastructure, software, platform and security as a services. In which security plays an important role in the cloud service. Hence, this security has been given with three types of services such as mutual authentication, directory services, token granting for the resources. Since, existing token issuing mechanism does not provide scalability to large data sets and also increases memory overhead between the client and the server. Hence, our proposed work focuses on providing tokens to the users, which addresses the problem of scalability and memory overhead. The proposed framework of token management system monitors the entire operations of the cloud and there by managing the entire cloud infrastructure. Our model comes under the new category of cloud model known as "Security as a Service". This paper provides the security framework as an architectural model to verify user authorization and data correctness of the resource stored thereby provides guarantee to the data owner for their resource stored into the cloud This framework also describes about the storage of token in a secured manner and it also facilitates search and usage of tokens for auditing purpose and supervision of the users.
 

Efficient authentication, authorization, and accounting (AAA) management mechanisms will be key for the widespread adoption of SDN experimentation facilities beyond the confines of academic labs. In particular, we are interested in a robust AAA infrastructure to identify experimenters, police their actions based on the associated roles, facilitate secure resource sharing, and provide for detailed accountability. Currently, however, said facilities are forced to employ a patchy AAA infrastructure which lacks several of the aforementioned features. This paper proposes a certificate-based AAA architecture for SDN experimental facilities, which is by design both secure and flexible. As this work is implementation-driven and aims for a short deployment cycle in current facilities, we also outline a credible migration path which we are currently pursuing actively.
 

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

An aspect of database forensics that has not received much attention in the academic research community yet is the presence of database triggers. Database triggers and their implementations have not yet been thoroughly analysed to establish what possible impact they could have on digital forensic analysis methods and processes. Conventional database triggers are defined to perform automatic actions based on changes in the database. These changes can be on the data level or the data definition level. Digital forensic investigators might thus feel that database triggers do not have an impact on their work. They are simply interrogating the data and metadata without making any changes. This paper attempts to establish if the presence of triggers in a database could potentially disrupt, manipulate or even thwart forensic investigations. The database triggers as defined in the SQL standard were studied together with a number of database trigger implementations. This was done in order to establish what aspects might have an impact on digital forensic analysis. It is demonstrated in this paper that some of the current database forensic analysis methods are impacted by the possible presence of certain types of triggers in a database. Furthermore, it finds that the forensic interpretation and attribution processes should be extended to include the handling and analysis of database triggers if they are present in a database.
 

Mobile users access location services from a location based server. While doing so, the user's privacy is at risk. The server has access to all details about the user. Example the recently visited places, the type of information he accesses. We have presented synergetic technique to safeguard location privacy of users accessing location-based services via mobile devices. Mobile devices have a capability to form ad-hoc networks to hide a user's identity and position. The user who requires the service is the query originator and who requests the service on behalf of query originator is the query sender. The query originator selects the query sender with equal probability which leads to anonymity in the network. The location revealed to the location service provider is a rectangle instead of exact co-ordinate. In this paper we have simulated the mobile network and shown the results for cloaking area sizes and performance against the variation in the density of users.

WiFi fingerprint-based localization is regarded as one of the most promising techniques for indoor localization. The location of a to-be-localized client is estimated by mapping the measured fingerprint (WiFi signal strengths) against a database owned by the localization service provider. A common concern of this approach that has never been addressed in literature is that it may leak the client's location information or disclose the service provider's data privacy. In this paper, we first analyze the privacy issues of WiFi fingerprint-based localization and then propose a Privacy-Preserving WiFi Fingerprint Localization scheme (PriWFL) that can protect both the client's location privacy and the service provider's data privacy. To reduce the computational overhead at the client side, we also present a performance enhancement algorithm by exploiting the indoor mobility prediction. Theoretical performance analysis and experimental study are carried out to validate the effectiveness of PriWFL. Our implementation of PriWFL in a typical Android smartphone and experimental results demonstrate the practicality and efficiency of PriWFL in real-world environments.

Many systems rely on passwords for authentication. Due to numerous accounts for different services, users have to choose and remember a significant number of passwords. Password-Manager applications address this issue by storing the user's passwords. They are especially useful on mobile devices, because of the ubiquitous access to the account passwords. Password-Managers often use key derivation functions to convert a master password into a cryptographic key suitable for encrypting the list of passwords, thus protecting the passwords against unauthorized, off-line access. Therefore, design and implementation flaws in the key derivation function impact password security significantly. Design and implementation problems in the key derivation function can render the encryption on the password list useless, by for example allowing efficient bruteforce attacks, or - even worse - direct decryption of the stored passwords. In this paper, we analyze the key derivation functions of popular Android Password-Managers with often startling results. With this analysis, we want to raise the awareness of developers of security critical apps for security, and provide an overview about the current state of implementation security of security-critical applications.

The Philips audio fingerprint[1] has been used for years, but its robustness against external noise has not been studied accurately. This paper shows the Philips fingerprint is noise resistant, and is capable of recognizing music that is corrupted by noise at a -4 to -7 dB signal to noise ratio. In addition, the drawbacks of the Philips fingerprint are addressed by utilizing a “Power Mask” in conjunction with the Philips fingerprint during the matching process. This Power Mask is a weight matrix given to the fingerprint bits, which allows mismatched bits to be penalized according to their relevance in the fingerprint. The effectiveness of the proposed fingerprint was evaluated by experiments using a database of 1030 songs and 1184 query files that were heavily corrupted by two types of noise at varying levels. Our experiments show the proposed method has significantly improved the noise resistance of the standard Philips fingerprint.

This paper proposes a high-performance audio fingerprint extraction method for identifying TV commercial advertisement. In the proposed method, a salient audio peak pair fingerprints based on constant Q transform (CQT) are hashed and stored, to be efficiently compared to one another. Experimental results confirm that the proposed method is quite robust in different noise conditions and improves the accuracy of the audio fingerprinting system in real noisy environments.

This paper presents a novel design of content fingerprints based on maximization of the mutual information across the distortion channel. We use the information bottleneck method to optimize the filters and quantizers that generate these fingerprints. A greedy optimization scheme is used to select filters from a dictionary and allocate fingerprint bits. We test the performance of this method for audio fingerprinting and show substantial improvements over existing learning based fingerprints.

This paper presents a human model-based feature extraction method for a video surveillance retrieval system. The proposed method extracts, from a normalized scene, object features such as height, speed, and representative color using a simple human model based on multiple-ellipse. Experimental results show that the proposed system can effectively track moving routes of people such as a missing child, an absconder, and a suspect after events.

In this paper we propose a methodology and a prototype tool to evaluate web application security mechanisms. The methodology is based on the idea that injecting realistic vulnerabilities in a web application and attacking them automatically can be used to support the assessment of existing security mechanisms and tools in custom setup scenarios. To provide true to life results, the proposed vulnerability and attack injection methodology relies on the study of a large number of vulnerabilities in real web applications. In addition to the generic methodology, the paper describes the implementation of the Vulnerability & Attack Injector Tool (VAIT) that allows the automation of the entire process. We used this tool to run a set of experiments that demonstrate the feasibility and the effectiveness of the proposed methodology. The experiments include the evaluation of coverage and false positives of an intrusion detection system for SQL Injection attacks and the assessment of the effectiveness of two top commercial web application vulnerability scanners. Results show that the injection of vulnerabilities and attacks is indeed an effective way to evaluate security mechanisms and to point out not only their weaknesses but also ways for their improvement.

Threats to modern ICT systems are rapidly changing these days. Organizations are not mainly concerned about virus infestation, but increasingly need to deal with targeted attacks. This kind of attacks are specifically designed to stay below the radar of standard ICT security systems. As a consequence, vendors have begun to ship self-learning intrusion detection systems with sophisticated heuristic detection engines. While these approaches are promising to relax the serious security situation, one of the main challenges is the proper evaluation of such systems under realistic conditions during development and before roll-out. Especially the wide variety of configuration settings makes it hard to find the optimal setup for a specific infrastructure. However, extensive testing in a live environment is not only cumbersome but usually also impacts daily business. In this paper, we therefore introduce an approach of an evaluation setup that consists of virtual components, which imitate real systems and human user interactions as close as possible to produce system events, network flows and logging data of complex ICT service environments. This data is a key prerequisite for the evaluation of modern intrusion detection and prevention systems. With these generated data sets, a system's detection performance can be accurately rated and tuned for very specific settings.

The popularity of mobile devices and the enormous number of third party mobile applications in the market have naturally lead to several vulnerabilities being identified and abused. This is coupled with the immaturity of intrusion detection system (IDS) technology targeting mobile devices. In this paper we propose a modular host-based IDS framework for mobile devices that uses behavior analysis to profile applications on the Android platform. Anomaly detection can then be used to categorize malicious behavior and alert users. The proposed system accommodates different detection algorithms, and is being tested at a major telecom operator in North America. This paper highlights the architecture, findings, and lessons learned.