Biblio

Delay-Tolerant Networks exhibit highly asynchronous connections often routed over many mobile hops before reaching its intended destination. The Bundle Security Protocol has been standardized providing properties such as authenticity, integrity, and confidentiality of bundles using traditional Public-Key Cryptography. Other protocols based on Identity-Based Cryptography have been proposed to reduce the key distribution overhead. However, in both schemes, secret keys are usually valid for several months. Thus, a secret key extracted from a compromised node allows for decryption of past communications since its creation. We solve this problem and propose the first forward secure protocol for Delay-Tolerant Networking. For this, we apply the Puncturable Encryption construction designed by Green and Miers, integrate it into the Bundle Security Protocol and adapt its parameters for different highly asynchronous scenarios. Finally, we provide performance measurements and discuss their impact.

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.

Research on advertisement has mainly focused on how to accurately predict the click-through rate (CTR). Much less is known about fraud detection and malicious behavior defense. Previous studies usually use statistics, design threshold and manually make strategies, which cannot find potential fraud behavior effectively and suffer from new attacks. In this paper, we make the first step to understand the type of malicious activities on large-scale online advertising platforms. By analyzing each feature comprehensively, we propose a novel coding approach to transform nominal attributes into numeric while maintaining the most effective information of the original data for fraud detection. Next, we code important features such as IP and cookie in our dataset and train machine learning methods to detect fraud traffic automatically. Experimental results on real datasets demonstrate that the proposed fraud detection method performs well considering both the accuracy and efficiency. Finally, we conclude how to design a defense system by considering which methods could be used for the anti-spam gaming in the future.

Advances in virtual reality have generated substantial interest in accurately reproducing and storing spatial audio in the higher order ambisonics (HOA) representation, given its rendering flexibility. Recent standardization for HOA compression adopted a framework wherein HOA data are decomposed into principal components that are then encoded by standard audio coding, i.e., frequency domain quantization and entropy coding to exploit psychoacoustic redundancy. A noted shortcoming of this approach is the occasional mismatch in principal components across blocks, and the resulting suboptimal transitions in the data fed to the audio coder. Instead, we propose a framework where singular value decomposition (SVD) is performed after transformation to the frequency domain via the modified discrete cosine transform (MDCT). This framework not only ensures smooth transition across blocks, but also enables frequency dependent SVD for better energy compaction. Moreover, we introduce a novel noise substitution technique to compensate for suppressed ambient energy in discarded higher order ambisonics channels, which significantly enhances the perceptual quality of the reconstructed HOA signal. Objective and subjective evaluation results provide evidence for the effectiveness of the proposed framework in terms of both higher compression gains and better perceptual quality, compared to existing methods.

Covert operations involving clandestine dealings and communication through cryptic and hidden messages have existed since time immemorial. While these do have a negative connotation, they have had their fair share of use in situations and applications beneficial to society in general. A "Dead Drop" is one such method of espionage trade craft used to physically exchange items or information between two individuals using a secret rendezvous point. With a "Dead Drop", to maintain operational security, the exchange itself is asynchronous. Information hiding in the slack space is one modern technique that has been used extensively. Slack space is the unused space within the last block allocated to a stored file. However, hiding in slack space operates under significant constraints with little resilience and fault tolerance. In this paper, we propose FROST – a novel asynchronous "Digital Dead Drop" robust to detection and data loss with tunable fault tolerance. Fault tolerance is a critical attribute of a secure and robust system design. Through extensive validation of FROST prototype implementation on Ubuntu Linux, we confirm the performance and robustness of the proposed digital dead drop to detection and data loss. We verify the recoverability of the secret message under various operating conditions ranging from block corruption and drive de-fragmentation to growing existing files on the target drive.

The challenge of maintaining confidentiality of stored and processed data in a remote database or cloud is quite urgent. Using homomorphic encryption may solve the problem, because it allows to compute some functions over encrypted data without preliminary deciphering of data. Fully homomorphic encryption schemes have a number of limitations such as accumulation of noise and increase of ciphertext extension during performing operations, the range of operations is limited. Nowadays a lot of homomorphic encryption schemes and their modifications have been investigated, so more than 25 reports on homomorphic encryption schemes have already been published on Cryptology ePrint Archive for 2016. We propose an overview of current Fully Homomorphic Encryption Schemes and analyze specific operations for databases which homomorphic cryptosystems allow to perform. We also investigate the possibility of sorting over encrypted data and present our approach to compare data encrypted by Multi-bit FHE scheme.

Security of sensible data for ultraconstrained IoT smart devices is one of the most challenging task in modern design. The needs of CPA-resistant cryptographic devices has to deal with the demanding requirements of small area and small impact on the overall power consumption. In this work, a novel current-mode feedback suppressor as on-chip analog-level CPA countermeasure is proposed. It aims to suppress differences in power consumption due to data-dependency of CMOS cryptographic devices, in order to counteract CPA attacks. The novel countermeasure is able to improve MTD of unprotected CMOS implementation of at least three orders of magnitude, providing a ×1.1 area and ×1.7 power overhead.

To meet the growing railway-transportation demand, a new train control system, communication-based train control (CBTC) system, aims to maximize the ability of train lines by reducing the headway of each train. However, the wireless communications expose the CBTC system to new security threats. Due to the cyber-physical nature of the CBTC system, a jamming attack can damage the physical part of the train system by disrupting the communications. To address this issue, we develop a secure framework to mitigate the impact of the jamming attack based on a security criterion. At the cyber layer, we apply a multi-channel model to enhance the reliability of the communications and develop a zero-sum stochastic game to capture the interactions between the transmitter and jammer. We present analytical results and apply dynamic programming to find the equilibrium of the stochastic game. Finally, the experimental results are provided to evaluate the performance of the proposed secure mechanism.

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].

The deceleration of transistor feature size scaling has motivated growing adoption of specialized accelerators implemented as GPUs, FPGAs, ASICs, and more recently new types of computing such as neuromorphic, bio-inspired, ultra low energy, reversible, stochastic, optical, quantum, combinations, and others unforeseen. There is a tension between specialization and generalization, with the current state trending to master slave models where accelerators (slaves) are instructed by a general purpose system (master) running an Operating System (OS). Traditionally, an OS is a layer between hardware and applications and its primary function is to manage hardware resources and provide a common abstraction to applications. Does this function, however, apply to new types of computing paradigms? This paper revisits OS functionality for memristor-based accelerators. We explore one accelerator implementation, the Dot Product Engine (DPE), for a select pattern of applications in machine learning, imaging, and scientific computing and a small set of use cases. We explore typical OS functionality, such as reconfiguration, partitioning, security, virtualization, and programming. We also explore new types of functionality, such as precision and trustworthiness of reconfiguration. We claim that making an accelerator, such as the DPE, more general will result in broader adoption and better utilization.

Companies want to offer chat bots to their customers and employees which can answer questions, enable self-service, and showcase their products and services. Implementing and maintaining chat bots by hand costs time and money. Companies typically have web APIs for their services, which are often documented with an API specification. This paper presents a compiler that takes a web API specification written in Swagger and automatically generates a chat bot that helps the user make API calls. The generated bot is self-documenting, using descriptions from the API specification to answer help requests. Unfortunately, Swagger specifications are not always good enough to generate high-quality chat bots. This paper addresses this problem via a novel in-dialogue curation approach: the power user can improve the generated chat bot by interacting with it. The result is then saved back as an API specification. This paper reports on the design and implementation of the chat bot compiler, the in-dialogue curation, and working case studies.

This work describes how automated data generation integrates in a big data pipeline. A lack of veracity in big data can cause models that are inaccurate, or biased by trends in the training data. This can lead to issues as a pipeline matures that are difficult to overcome. This work describes the use of a Generative Adversarial Network to generate sketch data, such as those that might be used in a human verification task. These generated sketches are verified as recognizable using a crowd-sourcing methodology, and finds that the generated sketches were correctly recognized 43.8% of the time, in contrast to human drawn sketches which were 87.7% accurate. This method is scalable and can be used to generate realistic data in many domains and bootstrap a dataset used for training a model prior to deployment.

Mobile Ad Hoc Network (MANET) technology provides intercommunication between different nodes where no infrastructure is available for communication. MANET is attracting many researcher attentions as it is cost effective and easy for implementation. Main challenging aspect in MANET is its vulnerability. In MANET nodes are very much vulnerable to attacks along with its data as well as data flowing through these nodes. One of the main reasons of these vulnerabilities is its communication policy which makes nodes interdependent for interaction and data flow. This mutual trust between nodes is exploited by attackers through injecting malicious node or replicating any legitimate node in MANET. One of these attacks is blackhole attack. In this study, the behavior of blackhole attack is discussed and have proposed a lightweight solution for blackhole attack which uses inbuilt functions.

Notable recent security incidents have generated intense interest in adversaries which attempt to subvert–-perhaps covertly–-crypto$\backslash$-graphic algorithms. In this paper we develop (IND-CPA) Semantically Secure encryption in this challenging setting. This fundamental encryption primitive has been previously studied in the "kleptographic setting," though existing results must relax the model by introducing trusted components or otherwise constraining the subversion power of the adversary: designing a Public Key System that is kletographically semantically secure (with minimal trust) has remained elusive to date. In this work, we finally achieve such systems, even when all relevant cryptographic algorithms are subject to adversarial (kleptographic) subversion. To this end we exploit novel inter-component randomized cryptographic checking techniques (with an offline checking component), combined with common and simple software engineering modular programming techniques (applied to the system's black box specification level). Moreover, our methodology yields a strong generic technique for the preservation of any semantically secure cryptosystem when incorporated into the strong kleptographic adversary setting.

A wireless sensor network (WSN) is composed of sensor nodes and a base station. In WSNs, constructing an efficient key-sharing scheme to ensure a secure communication is important. In this paper, we propose a new key-sharing scheme for groups, which shares a group key in a single broadcast without being dependent on the number of nodes. This scheme is based on geometric characteristics and has information-theoretic security in the analysis of transmitted data. We compared our scheme with conventional schemes in terms of communication traffic, computational complexity, flexibility, and security, and the results showed that our scheme is suitable for an Internet-of-Things (IoT) network.

Users today enjoy access to a wealth of services that rely on user-contributed data, such as recommendation services, prediction services, and services that help classify and interpret data. The quality of such services inescapably relies on trustworthy contributions from users. However, validating the trustworthiness of contributions may rely on privacy-sensitive contextual data about the user, such as a user's location or usage habits, creating a conflict between privacy and trust: users benefit from a higher-quality service that identifies and removes illegitimate user contributions, but, at the same time, they may be reluctant to let the service access their private information to achieve this high quality. We argue that this conflict can be resolved with a pragmatic Glimmer of Trust, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used on the client-side–-in contrast to much recent work exploring SGX in cloud services–-to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases.

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The prop- sed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow.

Object-oriented languages like Java and C\# allow the null value for all references. This supports many flexible patterns, but has led to many errors, security vulnerabilities, and system crashes. % Static type systems can prevent null-pointer exceptions at compile time, but require annotations, in particular for used libraries. Conservative defaults choose the most restrictive typing, preventing many errors, but requiring a large annotation effort. Liberal defaults choose the most flexible typing, requiring less annotations, but giving weaker guarantees. Trusted annotations can be provided, but are not checked and require a large manual effort. None of these approaches provide a strong guarantee that the checked part of the program is isolated from the unchecked part: even with conservative defaults, null-pointer exceptions can occur in the checked part. This paper presents Granullar, a gradual type system for null-safety. Developers start out verifying null-safety for the most important components of their applications. At the boundary to unchecked components, runtime checks are inserted by Granullar to guard the verified system from being polluted by unexpected null values. This ensures that null-pointer exceptions can only occur within the unchecked code or at the boundary to checked code; the checked code is free of null-pointer exceptions. We present Granullar for Java, define the checked-unchecked boundary, and how runtime checks are generated. We evaluate our approach on real world software annotated for null-safety. We demonstrate the runtime checks, and acceptable compile-time and run-time performance impacts. Granullar enables combining a checked core with untrusted libraries in a safe manner, improving on the practicality of such a system.

As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.

In this paper, we present a framework for graph-based representation of relation between sensors and alert types in a security alert sharing platform. Nodes in a graph represent either sensors or alert types, while edges represent various relations between them, such as common type of reported alerts or duplicated alerts. The graph is automatically updated, stored in a graph database, and visualized. The resulting graph will be used by network administrators and security analysts as a visual guide and situational awareness tool in a complex environment of security alert sharing.

Networking system does not liable on static infrastructure that interconnects various nodes in identical broadcast range dynamically called as Mobile Ad-hoc Network. A Network requires adaptive connectivity due to this data transmission rate increased. In this paper, we designed developed a dynamic cluster head selection to detect gray hole attack in MANETs on the origin of battery power. MANETs has dynamic nodes so we delivered novel way to choose cluster head by self-stabilizing election algorithm followed by MD5 algorithm for security purposes. The Dynamic cluster based intrusion revealing system to detect gray hole attack in MANET. This Architecture enhanced performance in terms of Packet delivery ratio and throughput due to dynamic cluster based IDS, associating results of existing system with proposed system, throughput of network increased, end to end delay and routing overhead less compared with existing system due to gray hole nodes in the MANET. The future work can be prolonged by using security algorithm AES and MD6 and also by including additional node to create large network by comparing multiple routing protocol in MANETs.

The rapid development of Internet has resulted in massive information overloading recently. These information is usually represented by high-dimensional feature vectors in many related applications such as recognition, classification and retrieval. These applications usually need efficient indexing and search methods for such large-scale and high-dimensional database, which typically is a challenging task. Some efforts have been made and solved this problem to some extent. However, most of them are implemented in a single machine, which is not suitable to handle large-scale database.In this paper, we present a novel data index structure and nearest neighbor search algorithm implemented on Apache Spark. We impose a grid on the database and index data by non-empty grid cells. This grid-based index structure is simple and easy to be implemented in parallel. Moreover, we propose to build a scalable KNN graph on the grids, which increase the efficiency of this index structure by a low cost in parallel implementation. Finally, experiments are conducted in both public databases and synthetic databases, showing that the proposed methods achieve overall high performance in both efficiency and accuracy.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.

The main goal of introducing an identity-based cryptosystem and certificateless cryptosystem was avoiding certificates' management costs. In turn, the goal of introducing a certificate-based cryptosystem was to solve the certificate revocation problem. In this paper, we propose a new digital Implicit and Explicit Certificates-Based Hess's Signature (IE-CBHS) scheme that combines the features of a standard public key infrastructure (PKI) and certificate-based cryptosystem. Our IE-CBHS scheme is an efficient certificates-based signature. The security analysis proves that the scheme is secure against two game attacks in the random oracle model. The security is closely related to the difficulty of solving the computational Diffie–Hellman and discrete logarithm problems. The IE-CBHS scheme, when compared with other signature schemes, has similar efficiency and is both more flexible and more useful in practice. It is possible to revoke the explicit certificate and use that fact during digital signature verification. Thus, our scheme is useful in applications where typical mechanisms of standard PKI are used. One of many important security features is resistance to denial of signature verification attack. Also, it is impossible for a trusted authority to recreate a partial private key, even with cooperation with the signer.