Biblio

The key challenge to a datacenter network is its scalability to handle many customers and their applications. In a datacenter network, packet classification plays an important role in supporting various network services. Previous algorithms store classification rules with the same length combinations in a hash table to simplify the search procedure. The search performance of hash-based algorithms is tied to the number of hash tables. To achieve fast and scalable packet classification, we propose an algorithm, encoded rule expansion, to transform rules into an equivalent set of rules with fewer distinct length combinations, without affecting the classification results. The new algorithm can minimize the storage penalty of transformation and achieve a short search time. In addition, the scheme supports fast incremental updates. Our simulation results show that more than 90% hash tables can be eliminated. The reduction of length combinations leads to an improvement on speed performance of packet classification by an order of magnitude. The results also show that the software implementation of our scheme without using any hardware parallelism can support up to one thousand customer VLANs and one million rules, where each rule consumes less than 60 bytes and each packet classification can be accomplished under 50 memory accesses.
 

The emergence of new network applications, such as the network intrusion detection system and packet-level accounting, requires packet classification to report all matched rules instead of only the best matched rule. Although several schemes have been proposed recently to address the multimatch packet classification problem, most of them require either huge memory or expensive ternary content addressable memory (TCAM) to store the intermediate data structure, or they suffer from steep performance degradation under certain types of classifiers. In this paper, we decompose the operation of multimatch packet classification from the complicated multidimensional search to several single-dimensional searches, and present an asynchronous pipeline architecture based on a signature tree structure to combine the intermediate results returned from single-dimensional searches. By spreading edges of the signature tree across multiple hash tables at different stages, the pipeline can achieve a high throughput via the interstage parallel access to hash tables. To exploit further intrastage parallelism, two edge-grouping algorithms are designed to evenly divide the edges associated with each stage into multiple work-conserving hash tables. To avoid collisions involved in hash table lookup, a hybrid perfect hash table construction scheme is proposed. Extensive simulation using realistic classifiers and traffic traces shows that the proposed pipeline architecture outperforms HyperCuts and B2PC schemes in classification speed by at least one order of magnitude, while having a similar storage requirement. Particularly, with different types of classifiers of 4K rules, the proposed pipeline architecture is able to achieve a throughput between 26.8 and 93.1 Gb/s using perfect hash tables.

Blind Source Separation (BSS) deals with the recovery of source signals from a set of observed mixtures, when little or no knowledge of the mixing process is available. BSS can find an application in the context of network coding, where relaying linear combinations of packets maximizes the throughput and increases the loss immunity. By relieving the nodes from the need to send the combination coefficients, the overhead cost is largely reduced. However, the scaling ambiguity of the technique and the quasi-uniformity of compressed media sources makes it unfit, at its present state, for multimedia transmission. In order to open new practical applications for BSS in the context of multimedia transmission, we have recently proposed to use a non-linear encoding to increase the discriminating power of the classical entropy-based separation methods. Here, we propose to append to each source a non-linear message digest, which offers an overhead smaller than a per-symbol encoding and that can be more easily tuned. Our results prove that our algorithm is able to provide high decoding rates for different media types such as image, audio, and video, when the transmitted messages are less than 1.5 kilobytes, which is typically the case in a realistic transmission scenario.

As most of the modern encryption algorithms are broken fully/partially, the world of information security looks in new directions to protect the data it transmits. The concept of using DNA computing in the fields of cryptography has been identified as a possible technology that may bring forward a new hope for hybrid and unbreakable algorithms. Currently, several DNA computing algorithms are proposed for cryptography, cryptanalysis and steganography problems, and they are proven to be very powerful in these areas. This paper gives an architectural framework for encryption & Generation of digital signature using DNA Cryptography. To analyze the performance; the original plaintext size and the key size; together with the encryption and decryption time are examined also the experiments on plaintext with different contents are performed to test the robustness of the program.

Law enforcement employs an investigative approach based on marked money bills to track illegal drug dealers. In this paper we discuss research that aims at providing law enforcement with the cyber counterpart of that approach in order to track perpetrators that operate botnets. We have devised a novel steganographic approach that generates a watermark hidden within a honey token, i.e. A decoy Word document. The covert bits that comprise the watermark are carried via secret interpretation of object properties in the honey token. The encoding and decoding of object properties into covert bits follow a scheme based on bijective functions generated via a chaotic logistic map. The watermark is retrievable via a secret cryptographic key, which is generated and held by law enforcement. The honey token is leaked to a botmaster via a honey net. In the paper, we elaborate on possible means by which law enforcement can track the leaked honey token to the IP address of a botmaster's machine.

This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6-44.5 and percentage root mean square difference (PRD) of 0.8-2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications.
 

Encryption and decryption of data in an efficient manner is one of the challenging aspects of modern computer science. This paper introduces a new algorithm for Cryptography to achieve a higher level of security. In this algorithm it becomes possible to hide the meaning of a message in unprintable characters. The main issue of this paper is to make the encrypted message undoubtedly unprintable using several times of ASCII conversions and a cyclic mathematical function. Dividing the original message into packets binary matrices are formed for each packet to produce the unprintable encrypted message through making the ASCII value for each character below 32. Similarly, several ASCII conversions and the inverse cyclic mathematical function are used to decrypt the unprintable encrypted message. The final encrypted message received from three times of encryption becomes an unprintable text through which the algorithm possesses higher level of security without increasing the size of data or loosing of any data.
 

The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.

We investigate large wireless networks subject to security constraints. In contrast to point-to-point, interference-limited communications considered in prior works, we propose active cooperative relaying based schemes. We consider a network with nl legitimate nodes and ne eavesdroppers, and path loss exponent α ≥ 2. As long as ne2(log(ne))γ = o(nl) holds for some positive γ, we show one can obtain unbounded secure aggregate rate. This means zero-cost secure communication, given a fixed total power constraint for the entire network. We achieve this result with (i) the source using Wyner randomized encoder and a serial (multi-stage) block Markov scheme, to cooperate with the relays, and (ii) the relays acting as a virtual multi-antenna to apply beamforming against the eavesdroppers. Our simpler parallel (two-stage) relaying scheme can achieve the same unbounded secure aggregate rate when neα/2 + 1 (log(ne))γ+δ(α/2+1) = o(nl) holds, for some positive γ, δ.

In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of ℓ1 nodes as well as all the repair traffic entering a second disjoint set of ℓ2 nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever ℓ2 ≤ d - k + 1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.

Information is increasing quickly, database owners have tendency to outsource their data to an external service provider called Cloud Computing. Using Cloud, clients can remotely store their data without burden of local data storage and maintenance. However, such service provider is untrusted, therefore there are some challenges in data security: integrity, availability and confidentiality. Since integrity and availability are prerequisite conditions of the existence of a system, we mainly focus on them rather than confidentiality. To ensure integrity and availability, researchers have proposed network coding-based POR (Proof of Retrievability) schemes that enable the servers to demonstrate whether the data is retrievable or not. However, most of network coding-based POR schemes are inefficient in data checking and also cannot prevent a common attack in POR: small corruption attack. In this paper, we propose a new network coding-based POR scheme using dispersal code in order to reduce cost in checking phase and also to prevent small corruption attack.

Social networking sites (SNSs), with their large number of users and large information base, seem to be the perfect breeding ground for exploiting the vulnerabilities of people, who are considered the weakest link in security. Deceiving, persuading, or influencing people to provide information or to perform an action that will benefit the attacker is known as "social engineering." Fraudulent and deceptive people use social engineering traps and tactics through SNSs to trick users into obeying them, accepting threats, and falling victim to various crimes such as phishing, sexual abuse, financial abuse, identity theft, and physical crime. Although organizations, researchers, and practitioners recognize the serious risks of social engineering, there is a severe lack of understanding and control of such threats. This may be partly due to the complexity of human behaviors in approaching, accepting, and failing to recognize social engineering tricks. This research aims to investigate the impact of source characteristics on users' susceptibility to social engineering victimization in SNSs, particularly Facebook. Using grounded theory method, we develop a model that explains what and how source characteristics influence Facebook users to judge the attacker as credible.

In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of ℓ1 nodes as well as all the repair traffic entering a second disjoint set of ℓ2 nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever ℓ2 ≤ d - k + 1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.