Biblio

Named Data Networking (NDN) is a content-oriented future Internet architecture, which well suits the increasingly mobile and information-intensive applications that dominate today's Internet. NDN relies on in-network caching to facilitate content delivery. This makes it challenging to enforce access control since the content has been cached in the routers and the content producer has lost the control over it. Due to its salient advantages in content delivery, network coding has been introduced into NDN to improve content delivery effectiveness. In this paper, we design ACNC, the first Access Control solution specifically for Network Coding-based NDN. By combining a novel linear AONT (All Or Nothing Transform) and encryption, we can ensure that only the legitimate user who possesses the authorization key can successfully recover the encoding matrix for network coding, and hence can recover the content being transmitted. In addition, our design has two salient merits: 1) the linear AONT well suits the linear nature of network coding; 2) only one vector of the encoding matrix needs to be encrypted/decrypted, which only incurs small computational overhead. Security analysis and experimental evaluation in ndnSIM show that our design can successfully enforce access control on network coding-based NDN with an acceptable overhead.

Today's major concern is not only maximizing the information rate through linear network coding scheme which is intelligent combination of information symbols at sending nodes but also secured transmission of information. Though cryptographic measure of security (computational security) gives secure transmission of information, it results system complexity and consequent reduction in efficiency of the communication system. This problem leads to alternative way of optimally secure and maximized information transmission. The alternative solution is secure network coding which is information theoretic approach. Depending up on applications, different security measures are needed during the transmission of information over wiretapped network with potential attack by the adversaries. In this research work, mathematical model for different security constraints with upper and lower boundaries were studied depending up on the randomness added to the source message and hence the security constraints on linear network code for randomized source messages depends both on randomness added and number of random source symbols. If the source generates large number random symbols, lesser number of random keys can give higher security to the information but information theoretic security bounds remain same. Hence maximizing randomness to the source is equivalent to adding security level.

Network coding is a potential method that numerous investigators have move forwarded due to its significant advantages to enhance the proficiency of data communication. In this work, utilize simulations to assess the execution of various network topologies employing network coding. By contrasting the results of network and without network coding, it insists that network coding can improve the throughput, end-to-end delays, Packet Delivery Rate (PDR) and consistency. This paper presents the comparative performance analysis of network coding such as, XOR, LNC, and RLNC. The results demonstrates the XOR technique has attractive outcomes and can improve the real time performance metrics i.e.; throughput, end-to-end delay and PDR by substantial limitations. The analysis has been carried out based on packet size and also number of packets to be transmitted. Results illustrates that the network coding facilitate in dependence between networks.

With the steady increase of offered cloud storage services, they became a popular alternative to local storage systems. Beside several benefits, the usage of cloud storage services can offer, they have also some downsides like potential vendor lock-in or unavailability. Different pricing models, storage technologies and changing storage requirements are further complicating the selection of the best fitting storage solution. In this work, we present a heuristic optimization approach that optimizes the placement of data on cloud-based storage services in a redundant, cost- and latency-efficient way while considering user-defined Quality of Service requirements. The presented approach uses monitored data access patterns to find the best fitting storage solution. Through extensive evaluations, we show that our approach saves up to 30% of the storage cost and reduces the upload and download times by up to 48% and 69% in comparison to a baseline that follows a state-of-the-art approach.

This paper proposes a new DNA cryptographic technique based on dynamic DNA encoding and asymmetric cryptosystem to increase the level of secrecy of data. The key idea is: to split the plaintext into fixed sized chunks, to encrypt each chunk using asymmetric cryptosystem and finally to merge the ciphertext of each chunk using dynamic DNA encoding. To generate chunks, characters of the plaintext are transformed into their equivalent ASCII values and split it into finite values. Now to encrypt each chunk, asymmetric cryptosystem is applied and the ciphertext is transformed into its equivalent binary value. Then this binary value is converted into DNA bases. Finally to merge each chunk, sufficient random strings are generated. Here to settle the required number of random strings, dynamic DNA encoding is exploited which is generated using Fibonacci series. Thus the use of finite chunks, asymmetric cryptosystem, random strings and dynamic DNA encoding increases the level of security of data. To evaluate the encryption-decryption time requirement, an empirical analysis is performed employing RSA, ElGamal and Paillier cryptosystems. The proposed technique is suitable for any use of cryptography.

This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena, and extracts a code vector that captures the semantic similarity between the feature vectors. This similarity is embedded in an abstract latent representation. Because the AE is trained in an unsupervised fashion, the main part of this success comes from appropriate original feature set that is used in this paper. It can also provide more discriminative features in contrast to other feature engineering approaches. Furthermore, the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements. We selected two different cyber security tasks: networkbased anomaly intrusion detection and Malware classication. We have analysed the proposed scheme with various classifiers using publicly available datasets for network anomaly intrusion detection and malware classifications. Several appropriate evaluation metrics show improvement compared to prior results.

This paper proposes a method to detect two primary means of using the Domain Name System (DNS) for malicious purposes. We develop machine learning models to detect information exfiltration from compromised machines and the establishment of command & control (C&C) servers via tunneling. We validate our approach by experiments where we successfully detect a malware used in several recent Advanced Persistent Threat (APT) attacks [1]. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

Data Deduplication provides lots of benefits to security and privacy issues which can arise as user's sensitive data at risk of within and out of doors attacks. Traditional secret writing that provides knowledge confidentiality is incompatible with knowledge deduplication. Ancient secret writing wants completely different users to encode their knowledge with their own keys. Thus, identical knowledge copies of completely different various users can result in different ciphertexts that makes Deduplication not possible. Convergent secret writing has been planned to enforce knowledge confidentiality whereas creating Deduplication possible. It encrypts/decrypts a knowledge copy with a confluent key, that is obtained by computing the cryptographical hash price of the content of the information copy. Once generation of key and encryption, the user can retain the keys and send ciphertext to cloud.

The principal mission of Multi-Source Multicast (MSM) is to disseminate all messages from all sources in a network to all destinations. MSM is utilized in numerous applications. In many of them, securing the messages disseminated is critical. A common secure model is to consider a network where there is an eavesdropper which is able to observe a subset of the network links, and seek a code which keeps the eavesdropper ignorant regarding all the messages. While this is solved when all messages are located at a single source, Secure MSM (SMSM) is an open problem, and the rates required are hard to characterize in general. In this paper, we consider Individual Security, which promises that the eavesdropper has zero mutual information with each message individually. We completely characterize the rate region for SMSM under individual security, and show that such a security level is achievable at the full capacity of the network, that is, the cut-set bound is the matching converse, similar to non-secure MSM. Moreover, we show that the field size is similar to non-secure MSM and does not have to be larger due to the security constraint.

In this paper, we initiate the study of garbled protocols - a generalization of Yao's garbled circuits construction to distributed protocols. More specifically, in a garbled protocol construction, each party can independently generate a garbled protocol component along with pairs of input labels. Additionally, it generates an encoding of its input. The evaluation procedure takes as input the set of all garbled protocol components and the labels corresponding to the input encodings of all parties and outputs the entire transcript of the distributed protocol. We provide constructions for garbling arbitrary protocols based on standard computational assumptions on bilinear maps (in the common random string model). Next, using garbled protocols we obtain a general compiler that compresses any arbitrary round multiparty secure computation protocol into a two-round UC secure protocol. Previously, two-round multiparty secure computation protocols were only known assuming witness encryption or learning-with errors. Benefiting from our generic approach we also obtain protocols (i) for the setting of random access machines (RAM programs) while keeping communication and computational costs proportional to running times, while (ii) making only a black-box use of the underlying group, eliminating the need for any expensive non-black-box group operations. Our results are obtained by a simple but powerful extension of the non-interactive zero-knowledge proof system of Groth, Ostrovsky and Sahai [Journal of ACM, 2012].

Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important for an efficient representation of compositionality in object-based representations. With the perspective of feature learning as a data compression operation, we propose a new greedy inference algorithm for hierarchical sparse coding. Convolutional matching pursuit with a L0-norm constraint was used to encode the input signal into compact and non-redundant codes distributed across levels of the hierarchy. Simple and complex synthetic datasets of temporal signals were created to evaluate the encoding efficiency and compare with the theoretical lower bounds on the information rate for those signals. Empirical evidence have shown that the algorithm is able to infer near-optimal codes for simple signals. However, it failed for complex signals with strong overlapping between objects. We explain the inefficiency of convolutional matching pursuit that occurred in such case. This brings new insights about the NP-hard optimization problem related to using L0-norm constraint in inferring optimally compact and distributed object-based representations.

In the paper, we demonstrate novel approach for network Intrusion Detection System (IDS) for cyber security using unsupervised Deep Learning (DL) techniques. Very often, the supervised learning and rules based approach like SNORT fetch problem to identify new type of attacks. In this implementation, the input samples are numerical encoded and applied un-supervised deep learning techniques called Auto Encoder (AE) and Restricted Boltzmann Machine (RBM) for feature extraction and dimensionality reduction. Then iterative k-means clustering is applied for clustering on lower dimension space with only 3 features. In addition, Unsupervised Extreme Learning Machine (UELM) is used for network intrusion detection in this implementation. We have experimented on KDD-99 dataset, the experimental results show around 91.86% and 92.12% detection accuracy using unsupervised deep learning technique AE and RBM with K-means respectively. The experimental results also demonstrate, the proposed approach shows around 4.4% and 2.95% improvement of detection accuracy using RBM with K-means against only K-mean clustering and Unsupervised Extreme Learning Machine (USELM) respectively.

Protection of information achieves keeping confidentiality, integrity, and availability of the data. These features are essential for the proper operation of modern industrial technologies, like Smart Grid. The complex grid system integrates many electronic devices that provide an efficient way of exploiting the power systems but cause many problems due to their vulnerabilities to attacks. The aim of the work is to propose a solution to the privacy problem in Smart Grid communication network between the customers and Control center. It consists in using the relatively new cryptographic task - quantum key distribution (QKD). The solution is based on choosing an appropriate quantum key distribution method out of all the conventional ones by performing an assessment in terms of several parameters. The parameters are: key rate, operating distances, resources, and trustworthiness of the devices involved. Accordingly, we discuss an answer to the privacy problem of the SG network with regard to both security and resource economy.

While research on Information-Centric Networking (ICN) flourishes, its adoption seems to be an elusive goal. In this paper we propose Edge-ICN: a novel approach for deploying ICN in a single large network, such as the network of an Internet Service Provider. Although Edge-ICN requires nothing beyond an SDN-based network supporting the OpenFlow protocol, with ICN-aware nodes only at the edges of the network, it still offers the same benefits as a clean-slate ICN architecture but without the deployment hassles. Moreover, by proxying legacy traffic and transparently forwarding it through the Edge-ICN nodes, all existing applications can operate smoothly, while offering significant advantages to applications such as native support for scalable anycast, multicast, and multi-source forwarding. In this context, we show how the proposed functionality at the edge of the network can specifically benefit CoAP-based IoT applications. Our measurements show that Edge-ICN induces on average the same control plane overhead for name resolution as a centralized approach, while also enabling IoT applications to build on anycast, multicast, and multi-source forwarding primitives.

This paper presents a new fractional-order hidden strange attractor generated by a chaotic system without equilibria. The proposed non-equilibrium fractional-order chaotic system (FOCS) is asymmetric, dissimilar, topologically inequivalent to typical chaotic systems and challenges the conventional notion that the presence of unstable equilibria is mandatory to ensure the existence of chaos. The new fractional-order model displays rich bifurcation undergoing a period doubling route to chaos, where the fractional order α is the bifurcation parameter. Study of the hidden attractor dynamics is carried out with the aid of phase portraits, sensitivity to initial conditions, fractal Lyapunov dimension, maximum Lyapunov exponents spectrum and bifurcation analysis. The minimum commensurate dimension to display chaos is determined. With a view to utilizing it in chaos based cryptology and coding information, a synchronisation control scheme is designed. Finally the theoretical analyses are validated by numerical simulation results which are in good agreement with the former.

Software systems nowadays communicate via a number of complex languages. This is often the cause of security vulnerabilities like arbitrary code execution, or injections. Whereby injections such as cross-site scripting are widely known from textual languages such as HTML and JSON that constantly gain more popularity. These systems use parsers to read input and unparsers write output, where these security vulnerabilities arise. Therefore correct parsing and unparsing of messages is of the utmost importance when developing secure and reliable systems. Part of the challenge developers face is to correctly encode data during unparsing and decode it during parsing. This paper presents McHammerCoder, an (un)parser and encoding generator supporting textual and binary languages. Those (un)parsers automatically apply the generated encoding, that is derived from the language's grammar. Therefore manually defining and applying encoding is not required to effectively prevent injections when using McHammerCoder. By specifying the communication language within a grammar, McHammerCoder provides developers with correct input and output handling code for their custom language.

The urgent task of the organization of confidential calculations in crucial objects of informatization on the basis of domestic TPM technologies (Trusted Platform Module) is considered. The corresponding recommendations and architectural concepts of the special hardware TPM module (Trusted Platform Module) which is built in a computing platform are proposed and realize a so-called ``root of trust''. As a result it gave the organization the confidential calculations on the basis of domestic electronic base.

There is widening chasm between the ease of creating software and difficulty of "building security in". This paper reviews the approach, the findings and recent experiments from a seven-year effort to enable consistency across a large, diverse development organization and software portfolio via policies, guidance, automated tools and services. Experience shows that developing secure software is an elusive goal for most. It requires every team to know and apply a wide range of security knowledge in the context of what software is being built, how the software will be used, and the projected threats in the environment where the software will operate. The drive for better outcomes for secure development and increased developer productivity led to experiments to augment developer knowledge and eventually realize the goal of "building the right security in".

We propose secure RAID, i.e., low-complexity schemes to store information in a distributed manner that is resilient to node failures and resistant to node eavesdropping. We generalize the concept of systematic encoding to secure RAID and show that systematic schemes have significant advantages in the efficiencies of encoding, decoding and random access. For the practical high rate regime, we construct three XOR-based systematic secure RAID schemes with optimal encoding and decoding complexities, from the EVENODD codes and B codes, which are array codes widely used in the RAID architecture. These schemes optimally tolerate two node failures and two eavesdropping nodes. For more general parameters, we construct efficient systematic secure RAID schemes from Reed-Solomon codes. Our results suggest that building “keyless”, information-theoretic security into the RAID architecture is practical.

We present a novel multimodal fusion model for affective content analysis, combining visual, audio and deep visual-sentiment descriptors from the media content with automated facial action measurements from naturalistic responses to the media. We collected a dataset of 48,867 facial responses to 384 media clips and extracted a rich feature set from the facial responses and media content. The stimulus videos were validated to be informative, inspiring, persuasive, sentimental or amusing. By combining the features, we were able to obtain a classification accuracy of 63% (weighted F1-score: 0.62) for a five-class task. This was a significant improvement over using the media content features alone. By analyzing the feature sets independently, we found that states of informed and persuaded were difficult to differentiate from facial responses alone due to the presence of similar sets of action units in each state (AU 2 occurring frequently in both cases). Facial actions were beneficial in differentiating between amused and informed states whereas media content features alone performed less well due to similarities in the visual and audio make up of the content. We highlight examples of content and reactions from each class. This is the first affective content analysis based on reactions of 10,000s of people.

With the advent of QR readers and mobile phones the use of graphical codes like QR codes and data matrix code has become very popular. Despite the noise like appearance, it has the advantage of high data capacity, damage resistance and fast decoding robustness. The proposed system embeds the image chosen by the user to develop visually appealing QR codes with improved decoding robustness using BCH algorithm. The QR information bits are encoded into luminance value of the input image. The developed Picode can inspire perceptivity in multimedia applications and can ensure data security for instances like online payments. The system is implemented on Matlab and ARM cortex A8.

Cellular Automata based computing paradigm is an efficient platform for modeling complicated computational problems. This can be used for various applications in the field of Cryptography. In this paper, it is used for generating a DNA cryptography based encryption algorithm. The encoded message in binary format is encrypted to cipher colors with the help of a simple algorithm based on the principles of DNA cryptography and cellular automata. The message will be in compressed form using XOR operator. Since cellular automata and DNA cryptographic principles are exploited, high level of parallelism, reversibility, uniformity etc. can be achieved.

The proposed frame describes two objectives one is to issue certificates through online and second is provide three level security through DNA cryptography. DNA Cryptography means converting the data to the DNA sequence. DNA is a succession comprising of four letters in order; A, C, G and T. every letter set is identified with a nucleotide. DNA can be used for store data, transmit the data and also used for computation of the data. This paper implemented 3 levels of cryptography. The receiver will apply the decryption for extracting the readable from the unreadable format. This DNA cryptography provide the security more than the other cryptography, but it takes more time complexity for generating the encoding and decoding and it has the chances to hacking the data by the hacker. So in this paper we implement the fast three level DNA Cryptography for me seva services.

The data security is a challenging issue nowadays with the increase of information capacity and its transmission rate. The most common and widely used techniques in the data security fields are cryptography and steganography. The combination of cryptography and steganography methods provides more security to the data. Now, DNA (Deoxyribonucleic Acid) is explored as a new carrier for data security since it achieves maximum protection and powerful security with high capacity and low modification rate. A new data security method can be developed by taking the advantages of DNA based AES (Advanced Encryption Standard) cryptography and DNA steganography. This new technique will provide multilayer security to the secret message. Here the secret message is first encoded to DNA bases then DNA based AES algorithm is applied to it. Finally the encrypted DNA will be concealed in another DNA sequence. This hybrid technique provides triple layer security to the secret message.

We prove polarization theorems for arbitrary classical-quantum (cq) channels. The input alphabet is endowed with an arbitrary Abelian group operation and an Arikan-style transformation is applied using this operation. It is shown that as the number of polarization steps becomes large, the synthetic cq-channels polarize to deterministic homomorphism channels that project their input to a quotient group of the input alphabet. This result is used to construct polar codes for arbitrary cq-channels and arbitrary classical-quantum multiple access channels (cq-MAC). The encoder can be implemented in O(N log N) operations, where N is the blocklength of the code. A quantum successive cancellation decoder for the constructed codes is proposed. It is shown that the probability of error of this decoder decays faster than 2-Nβ for any β textless; ½.