Biblio

In the paradigm of network coding, information-theoretic security is considered in the presence of wiretappers, who can access one arbitrary edge subset up to a certain size, referred to as the security level. Secure network coding is applied to prevent the leakage of the source information to the wiretappers. In this paper, we consider the problem of secure network coding for flexible pairs of information rate and security level with any fixed dimension (equal to the sum of rate and security level). We present a novel approach for designing a secure linear network code (SLNC) such that the same SLNC can be applied for all the rate and security-level pairs with the fixed dimension. We further develop a polynomial-time algorithm for efficient implementation and prove that there is no penalty on the required field size for the existence of SLNCs in terms of the best known lower bound by Guang and Yeung. Finally, by applying our approach as a crucial building block, we can construct a family of SLNCs that not only can be applied to all possible pairs of rate and security level but also share a common local encoding kernel at each intermediate node in the network.

Secure network coding realizes the secrecy of the message when the message is transmitted via noiseless network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the network has noise, we apply the error correction to noisy channel before applying the secure network coding. In contrast, secure physical layer network coding is a method to securely transmit a message by a combination of coding operation on nodes when the network is given as a set of noisy channels. In this paper, we give several examples of network, in which, secure physical layer network coding realizes a performance that cannot be realized by secure network coding.

It is well known that superposition coding, namely separately encoding the independent sources, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). However, the characterization of the coding rate region therein involves uncountably many linear inequalities and the constant term (i.e., the lower bound) in each inequality is given in terms of the solution of a linear optimization problem. Thus this implicit characterization of the coding rate region does not enable the determination of the achievability of a given rate tuple. In this paper, we first obtain closed-form expressions of these uncountably many inequalities. Then we identify a finite subset of inequalities that is sufficient for characterizing the coding rate region. This gives an explicit characterization of the coding rate region. We further show by the symmetry of the problem that only a much smaller subset of this finite set of inequalities needs to be verified in determining the achievability of a given rate tuple. Yet, the cardinality of this smaller set grows at least exponentially fast with L.

In this paper, we consider the application of Reed-Solomon (RS) channel coding for joint error correction and cooperative network coding on non-binary phase shift keying (PSK) modulated signals. The relay first decodes the RS channel coded messages received each in a time slot from all sources before applying network coding (NC) by the use of bit-level exclusive OR (XOR) operation. The network coded resulting message is then channel encoded before its transmission to the next relay or to the destination according to the network configuration. This scenario shows superior performance in comparison with the case where the relay does not perform channel coding/decoding. For different orders of PSK modulation and different wireless configurations, simulation results demonstrate the improvements resulting from the use of RS channel codes in terms of symbol error rate (SER) versus signal-to-noise ratio (SNR).

In this work, we investigate the MARC (Multiple Access Relay Channel) setup, in which two Markov sources communicate to a single destination, aided by one relay, based on Joint Source Channel Network (JSCN) LDPC codes. In addition, the two source nodes compress the information sequences with an LDPC source code. The compressed symbols are directly transmitted to both a relay and a destination nodes in two transportation phases. Indeed, the relay performs the concatenation of the received compressed sequences to obtain a recovered sequence, which is encoded with an LDPC channel code, before being forwarded to the destination. At the receiver, we propose an iterative joint decoding algorithm that exploits the correlation between the two sources-relay data and takes into account the errors occurring in the sources-relay links to estimate the source data. We show based on simulation results that the JSCN coding and decoding scheme into a MARC setup achieves a good performance with a gain of about 5 dB compared to a conventional LDPC code.

-In wireless networks, Cooperative communication can be used to increase the strength of the communication by means of spatial diversity. Basic idea that exists behind Cooperative communication is, if the transmission from source to destination is not successful, a helping node called relay can be used to send the same information to the destination through independent paths. In order to improve the performance of such communication, channel coding techniques can be used which reduces the Bit Error Rate. Previous works on cooperative communication only concentrated on improving channel capacity through cooperation. Hence this paper presents different Channel coding methods such as Turbo coding, Convolutional coding, and low-density parity-check coding over Rayleigh fading channels in the presence of Additive white Gaussian noise. Performance of these Channel coding techniques are measured in terms of noise power spectral density (NO ) vs. Bit error rate.

Cyber-physical systems connect the physical world and the information world by sensors and actuators. These sensors are usually small embedded systems which have many limitations on wireless communication, computing and storage. This paper proposes a lightweight coding method for secure and reliable transmission over a wireless communication links in cyber-physical systems. The reliability of transmission is provided by forward error correction. And to ensure the confidentiality, we utilize different encryption matrices at each time of coding which are generated by the sequence number of packets. So replay attacks and other cyber threats can be resisted simultaneously. The issues of the prior reliable transmission protocols and secure communication protocols in wireless networks of a cyber-physical system are reduced, such as large protocol overhead, high interaction delay and large computation cost.

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.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

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.

Information-Centric Network (ICN) is one of the most promising network architecture to handle the problem of rapid increase of data traffic because it allows in-network cache. ICNs with Linear Network Coding (LNC) can greatly improve the performance of content caching and delivery. In this paper, we propose a Secure Content Caching and Routing (SCCR) framework based on Software Defined Network (SDN) to find the optimal cache management and routing for secure content delivery, which aims to firstly minimize the total cost of cache and bandwidth consumption and then minimize the usage of random chunks to guarantee information theoretical security (ITS). Specifically, we firstly propose the SCCR problem and then introduce the main ideas of the SCCR framework. Next, we formulate the SCCR problem to two Linear Programming (LP) formulations and design the SCCR algorithm based on them to optimally solve the SCCR problem. Finally, extensive simulations are conducted to evaluate the proposed SCCR framework and algorithms.

We consider Delay Tolerant Mobile Social Networks (DTMSNs), made of wireless nodes with intermittent connections and clustered into social communities. The lack of infrastructure and its reliance on nodes' mobility make routing a challenge. Network Coding (NC) is a generalization of routing and has been shown to bring a number of advantages over routing. We consider the problem of pollution attacks in these networks, that are a very important issue both for NC and for DTMSNs. Our first contribution is to propose a protocol which allows controlling adversary's capacity by combining cryptographic hash dissemination and error-correction to ensure message recovery at the receiver. Our second contribution is the modeling of the performance of such a protection scheme. To do so, we adapt an inter-session NC model based on a fluid approximation of the dissemination process. We provide a numerical validation of the model. We are eventually able to provide a workflow to set the correct parameters and counteract the attacks. We conclude by highlighting how these contributions can help secure a real-world DTMSN application (e.g., a smart-phone app.).

Thanks to the occurrence of the Internet of Things (IoT), the devices are able to collect and transmit data via the Internet and contributing to our big data world. It will permit devices to exchange monitoring data content in real time. Real-time communication (RTC) with these devices was analyzed in respect to the Network delay. Network coding (NC) combines data packets and the output packet which is a mixture of the input packets. This technique can provide many potential gains to the network, including reducing Round-Trip Time (RTT), decreasing latency and improving Network delay (ND). In the present paper, the authors improve network delay metrics in the context of the remote management of renewable energy using a random NC with an efficient strategy technique.

Neural networks (NNs) are currently a very popular topic in machine learning for both research and practice. GPUs are the dominant computing platform for research efforts and are also gaining popularity as a deployment platform for applications such as autonomous vehicles. As a result, GPU vendors such as NVIDIA have spent enormous effort to write special-purpose NN libraries. On other hardware targets, especially mobile GPUs, such vendor libraries are not generally available. Thus, the development of portable, open, high-performance, energy-efficient GPU code for NN operations would enable broader deployment of NN-based algorithms. A root problem is that high efficiency GPU programming suffers from high complexity, low productivity, and low portability. To address this, this work presents a framework to enable productive, high-efficiency GPU programming for NN computations across hardware platforms and programming models. In particular, the framework provides specific support for metaprogramming and autotuning of operations over ND-Arrays. To show the correctness and value of our framework and approach, we implement a selection of NN operations, covering the core operations needed for deploying three common image-processing neural networks. We target three different hardware platforms: NVIDIA, AMD, and Qualcomm GPUs. On NVIDIA GPUs, we show both portability between OpenCL and CUDA as well competitive performance compared to the vendor library. On Qualcomm GPUs, we show that our framework enables productive development of target-specific optimizations, and achieves reasonable absolute performance. Finally, On AMD GPUs, we show initial results that indicate our framework can yield reasonable performance on a new platform with minimal effort.

Unlike traditional routing where packets are only stored and forward, network coding allows packets to mix together. New packets can be formed by combining other packets. This technique can provide benefits to the network. Network coding has been shown to improve network throughput, reduce energy consumption, improve network robustness and achieve the network capacity. 5G Network is foreseen as a novel network paradigm enabling massive device connectivity and enabling device-to-device communication (D2D). It has many potential applications ranging from ultra high definition video to virtual reality applications. In this paper, we present the advantages, benefits, scenarios, and applications of Network coding for 5G Network and device-to-device communication. We present the state-of-art research, the theoretical benefits, and detail how network coding can improve 5G Networks and D2D communication. Our results show that network coding can almost double the network throughput while increasing network robustness and decreasing the overall time to disseminate messages.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

The application of mobile Wireless Sensor Networks (WSNs) with a big amount of participants poses many challenges. For instance, high transmission loss rates which are caused i.a. by collisions might occur. Additionally, WSNs frequently operate under harsh conditions, where a high probability of link or node failures is inherently given. This leads to reliable data maintenance being a key issue. Existing approaches which were developed to keep data dependably in WSNs often either perform well in highly dynamic or in completely static scenarios, or require complex calculations. Herein, we present the Network Coding based Multicast Growth Codes (MCGC), which represent a solution for reliable data maintenance in large-scale WSNs. MCGC are able to tolerate high fault rates and reconstruct more originally collected data in a shorter period of time than compared existing approaches. Simulation results show performance improvements of up to 75% in comparison to Growth Codes (GC). These results are achieved independently of the systems' dynamics and despite of high fault probabilities.

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.

Primary user emulation (PUE) attack is one of the main threats affecting cognitive radio (CR) networks. The PUE can forge the same signal as the real primary user (PU) in order to use the licensed channel and cause deny of service (DoS). Therefore, it is important to locate the position of the PUE in order to stop and avoid any further attack. Several techniques have been proposed for localization, including the received signal strength indication RSSI, Triangulation, and Physical Network Layer Coding. However, the area surrounding the real PU is always affected by uncertainty. This uncertainty can be described as a lost (cost) function and conditional probability to be taken into consideration while proclaiming if a PU/PUE is the real PU or not. In this paper, we proposed a combination of a Bayesian model and trilateration technique. In the first part a trilateration technique is used to have a good approximation of the PUE position making use of the RSSI between the anchor nodes and the PU/PUE. In the second part, a Bayesian decision theory is used to claim the legitimacy of the PU based on the lost function and the conditional probability to help to determine the existence of the PUE attacker in the uncertainty area.

This work presents a systematic analysis of symmetric encryption modes for SSH that are in use on the Internet, providing deployment statistics, new attacks, and security proofs for widely used modes. We report deployment statistics based on two Internet-wide scans of SSH servers conducted in late 2015 and early 2016. Dropbear and OpenSSH implementations dominate in our scans. From our first scan, we found 130,980 OpenSSH servers that are still vulnerable to the CBC-mode-specific attack of Albrecht et al. (IEEE S&P 2009), while we found a further 20,000 OpenSSH servers that are vulnerable to a new attack on CBC-mode that bypasses the counter-measures introduced in OpenSSH 5.2 to defeat the attack of Albrecht et al. At the same time, 886,449 Dropbear servers in our first scan are vulnerable to a variant of the original CBC-mode attack. On the positive side, we provide formal security analyses for other popular SSH encryption modes, namely ChaCha20-Poly1305, generic Encrypt-then-MAC, and AES-GCM. Our proofs hold for detailed pseudo-code descriptions of these algorithms as implemented in OpenSSH. Our proofs use a corrected and extended version of the "fragmented decryption" security model that was specifically developed for the SSH setting by Boldyreva et al. (Eurocrypt 2012). These proofs provide strong confidentiality and integrity guarantees for these alternatives to CBC-mode encryption in SSH. However, we also show that these alternatives do not meet additional, desirable notions of security (boundary-hiding under passive and active attacks, and denial-of-service resistance) that were formalised by Boldyreva et al.

Cloud storage has been gaining in popularity as an on-line service for archiving, backup, and even primary storage of files. However, due to the data outsourcing, cloud storage also introduces new security challenges, which require a data audit and data repair service to ensure data availability and data integrity in the cloud. In this paper, we present the design and implementation of a network-coding-based Proof Of Retrievability scheme called ELAR, which achieves a lightweight data auditing and data repairing. In particular, we support direct repair mechanism in which the client can be free from the data repair process. Simultaneously, we also support the task of allowing a third party auditor (TPA), on behalf of the client, to verify the availability and integrity of the data stored in the cloud servers without the need of an asymmetric-key setting. The client is thus also free from the data audit process. TPA uses spot-checking which is a very efficient probabilistic method for checking a large amount of data. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Cloud service providers offer storage outsourcing facility to their clients. In a secure cloud storage (SCS) protocol, the integrity of the client's data is maintained. In this work, we construct a publicly verifiable secure cloud storage protocol based on a secure network coding (SNC) protocol where the client can update the outsourced data as needed. To the best of our knowledge, our scheme is the first SNC-based SCS protocol for dynamic data that is secure in the standard model and provides privacy-preserving audits in a publicly verifiable setting. Furthermore, we discuss, in details, about the (im)possibility of providing a general construction of an efficient SCS protocol for dynamic data (DSCS protocol) from an arbitrary SNC protocol. In addition, we modify an existing DSCS scheme (DPDP I) in order to support privacy-preserving audits. We also compare our DSCS protocol with other SCS schemes (including the modified DPDP I scheme). Finally, we figure out some limitations of an SCS scheme constructed using an SNC protocol.

In this paper we propose a protocol that allows end-users in a decentralized setup (without requiring any trusted third party) to protect data shipped to remote servers using two factors - knowledge (passwords) and possession (a time based one time password generation for authentication) that is portable. The protocol also supports revocation and recreation of a new possession factor if the older possession factor is compromised, provided the legitimate owner still has a copy of the possession factor. Furthermore, akin to some other recent works, our approach naturally protects the outsourced data from the storage servers themselves, by application of encryption and dispersal of information across multiple servers. We also extend the basic protocol to demonstrate how collaboration can be supported even while the stored content is encrypted, and where each collaborator is still restrained from accessing the data through a multi-factor access mechanism. Such techniques achieving layered security is crucial to (opportunistically) harness storage resources from untrusted entities.