Biblio

Traditional deception-based cyber defenses often undertake reactive strategies that utilize decoy systems or services for attack detection and information gathering. Unfortunately, the effectiveness of these defense mechanisms has been largely constrained by the low decoy fidelity, the poor scalability of decoy platform, and the static decoy configurations, which allow the attackers to identify and bypass the deployed decoys. In this paper, we develop a decoy-enhanced defense framework that can proactively protect critical servers against targeted remote attacks through deception. To achieve both high fidelity and good scalability, our system follows a hybrid architecture that separates lightweight yet versatile front-end proxies from back-end high-fidelity decoy servers. Moreover, our system can further invalidate the attackers' reconnaissance through dynamic proxy address shuffling. To guarantee service availability, we develop a transparent connection translation strategy to maintain existing connections during shuffling. Our evaluation on a prototype implementation demonstrates the effectiveness of our approach in defeating attacker reconnaissance and shows that it only introduces small performance overhead.

Being an era of fast internet-based application environment, large volumes of relational data are being outsourced for business purposes. Therefore, ownership and digital rights protection has become one of the greatest challenges and among the most critical issues. This paper presents a novel fingerprinting technique to protect ownership rights of non-numeric digital data on basis of pattern generation and row association schemes. Firstly, fingerprint sequence is formulated by using secret key and buyer's Unique ID. With the chunks of these sequences and by applying the Fibonacci series, we select some rows. The selected rows are candidates of fingerprinting. The primary key of selected row is protected using RSA encryption; after which a pattern is designed by randomly choosing the values of different attributes of datasets. The encryption of primary key leads to develop an association between original and fake pattern; creating an ease in fingerprint detection. Fingerprint detection algorithm first finds the fake rows and then extracts the fingerprint sequence from the fake attributes, hence identifying the traitor. Some most important features of the proposed approach is to overcome major weaknesses such as error tolerance, integrity and accuracy in previously proposed fingerprinting techniques. The results show that technique is efficient and robust against several malicious attacks.

This paper describes a video fingerprinting system that is highly robust to audio and video transformations. The proposed system adapts a robust audio fingerprint extraction approach to video fingerprinting. The audio fingerprinting system converts the spectrogram into binary images, and then encodes the positions of salient regions selected from each binary image. Visual features are extracted in a similar way from the video images. We propose two visual fingerprint generation methods where fingerprints encode the positions of salient regions of greyscale video images. Salient regions of the first method are selected based on the intensity values of the image, while the second method identifies the regions that represent the highest variations between two successive images. The similarity between two fingerprints is defined as the intersection between their elements. The search algorithm is speeded up by an efficient implementation on a Graphics Processing Unit (GPU). We evaluate the performance of the proposed video system on TRECVID 2009 and 2010 datasets, and we show that this system achieves promising results and outperforms other state-of-the-art video copy detection methods for queries that do not includes geometric transformations. In addition, we show the effectiveness of this system for a challenging audio+video copy detection task.

One of the primary motivations for using ultrasound reflectometry for fingerprint imaging is the promise of increased spoof resistance over conventional optical or capacitive sensing approaches due to the ability for ultrasound to determine the elastic impedance of the imaged material. A fake 3D printed plastic finger can therefore be easily distinguished from a real finger. However, ultrasonic sensors are still vulnerable to materials that are similar in impedance to tissue, such as water or rubber. Previously we demonstrated an ultrasonic fingerprint reader operating with 1.3GHz ultrasound based on pulse echo impedance imaging on the backside silicon interface. In this work, we utilize the large bandwidth of these sensors to differentiate between a finger and materials with similar impedances using the frequency response of elastic impedance obtained by transducer excitation with a wideband RF chirp signal. The reflected signal is a strong function of impedance mismatch and absorption [Hoople 2015].

This paper discusses possible approaches to address the loss of user privacy when browsing the web and being tracked by websites which compute a browser fingerprint identifying the user computer. The key problem is that the current fingerprinting countermeasures are insufficient to prevent fingerprinting tracking and also frequently produce side-effects on the web browser. The advantages and disadvantages of possible countermeasures are discussed in the context of improving resistance against browser fingerprinting. Finally, using a new browser extension is proposed as the best way to inhibit fingerprinting as it could probably inhibit some of the fingerprinting techniques used and also diminish the side-effects on the user browser experience, compared with existing techniques.

In view of the high demand for the security of visiting data in power system, a network data security analysis method based on DPI technology was put forward in this paper, to solve the problem of security gateway judge the legality of the network data. Considering the legitimacy of the data involves data protocol and data contents, this article will filters the data from protocol matching and content detection. Using deep packet inspection (DPI) technology to screen the protocol. Using protocol analysis to detect the contents of data. This paper implements the function that allowing secure data through the gateway and blocking threat data. The example proves that the method is more effective guarantee the safety of visiting data.

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Integrity of image data plays an important role in data communication. Image data contain confidential information so it is very important to protect data from intruder. When data is transmitted through the network, there may be possibility that data may be get lost or damaged. Existing system does not provide all functionality for securing image during transmission. i.e image compression, encryption and user authentication. In this paper hybrid cryptosystem is proposed in which biometric fingerprint is used for key generation which is further useful for encryption purpose. Secret fragment visible mosaic image method is used for secure transmission of image. For reducing the size of image lossless compression technique is used which leads to the fast transmission of image data through transmission channel. The biometric fingerprint is useful for authentication purpose. Biometric method is more secure method of authentication because it requires physical presence of human being and it is untraceable.

Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control. Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc. Still, there are novel identification technologies based on the individual's biometric features under development [1-4].

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
 

In this paper, we propose a remote password authentication scheme based on 3-D geometry with biometric value of a user. It is simple and practically useful and also a legal user can freely choose and change his password using smart card that contains some information. The security of the system depends on the points on the diagonal of a cuboid in 3D environment. Using biometric value makes the points more secure because the characteristics of the body parts cannot be copied or stolen.
 

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.

Acoustic microscopy is characterized by relatively long scanning time, which is required for the motion of the transducer over the entire scanning area. This time may be reduced by using a multi-channel acoustical system which has several identical transducers arranged as an array and is mounted on a mechanical scanner so that each transducer scans only a fraction of the total area. The resulting image is formed as a combination of all acquired partial data sets. The mechanical instability of the scanner, as well as the difference in parameters of the individual transducers causes a misalignment of the image fractures. This distortion may be partially compensated for by the introduction of constant or dynamical signal leveling and data shift procedures. However, a reduction of the random instability component requires more advanced algorithms, including auto-adjustment of processing parameters. The described procedure was implemented into the prototype of an ultrasonic fingerprint reading system. The specialized cylindrical scanner provides a helical spiral lens trajectory which eliminates repeatable acceleration, reduces vibration and allows constant data flow on maximal rate. It is equipped with an array of four spherically focused 50 MHz acoustic lenses operating in pulse-echo mode. Each transducer is connected to a separate channel including pulser, receiver and digitizer. The output 3D data volume contains interlaced B-scans coming from each channel. Afterward, data processing includes pre-determined procedures of constant layer shift in order to compensate for the transducer displacement, phase shift and amplitude leveling for compensation of variation in transducer characteristics. Analysis of statistical parameters of individual scans allows adaptive eliminating of the axial misalignment and mechanical vibrations. Further 2D correlation of overlapping partial C-scans will realize an interpolative adjustment which essentially improves the output image. Implementation of this adaptive algorithm into a data processing sequence allows us to significantly reduce misreading due to hardware noise and finger motion during scanning. The system provides a high quality acoustic image of the fingerprint including different levels of information: fingerprint pattern, sweat porous locations, internal dermis structures. These additional features can effectively facilitate fingerprint based identification. The developed principles and algorithm implementations allow improved quality, stability and reliability of acoustical data obtained with the mechanical scanner, accommodating several transducers. General principles developed during this work can be applied to other configurations of advanced ultrasonic systems designed for various biomedical and NDE applications. The data processing algorithm, developed for a specific biometric task, can be adapted for the compensation of mechanical imperfections of the other devices.

Over the past decade, we have witnessed a huge upsurge in social networking which continues to touch and transform our lives till present day. Social networks help us to communicate amongst our acquaintances and friends with whom we share similar interests on a common platform. Globally, there are more than 200 million visually impaired people. Visual impairment has many issues associated with it, but the one that stands out is the lack of accessibility to content for entertainment and socializing safely. This paper deals with the development of a keyboard less social networking website for visually impaired. The term keyboard less signifies minimum use of keyboard and allows the user to explore the contents of the website using assistive technologies like screen readers and speech to text (STT) conversion technologies which in turn provides a user friendly experience for the target audience. As soon as the user with minimal computer proficiency opens this website, with the help of screen reader, he/she identifies the username and password fields. The user speaks out his username and with the help of STT conversion (using Web Speech API), the username is entered. Then the control moves over to the password field and similarly, the password of the user is obtained and matched with the one saved in the website database. The concept of acoustic fingerprinting has been implemented for successfully validating the passwords of registered users and foiling intentions of malicious attackers. On successful match of the passwords, the user is able to enjoy the services of the website without any further hassle. Once the access obstacles associated to deal with social networking sites are successfully resolved and proper technologies are put to place, social networking sites can be a rewarding, fulfilling, and enjoyable experience for the visually impaired people.

Fingerprint-based Audio recognition system must address concurrent objectives. Indeed, fingerprints must be both robust to distortions and discriminative while their dimension must remain to allow fast comparison. This paper proposes to restate these objectives as a penalized sparse representation problem. On top of this dictionary-based approach, we propose a structured sparsity model in the form of a probabilistic distribution for the sparse support. A practical suboptimal greedy algorithm is then presented and evaluated on robustness and recognition tasks. We show that some existing methods can be seen as particular cases of this algorithm and that the general framework allows to reach other points of a Pareto-like continuum.

This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.

Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-SOMMELIER+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-SOMMELIER+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-SOMMELIER+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels.