Biblio

The fast growth of encrypted traffic puts forward burning requirements on the efficiency of traffic classification. Although deep learning models perform well in the classification, they sacrifice the efficiency to obtain high-precision results. To reduce the resource and time consumption, a novel and lightweight model is proposed in this paper. Our design principle is to “maximize the reuse of thin modules”. A thin module adopts the multi-head attention and the 1D convolutional network. Attributed to the one-step interaction of all packets and the parallelized computation of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of 1D convolutional networks are proved in traffic classification. Besides, the proposed model can work well in a real time manner, since only three consecutive packets of a flow are needed. To improve the stability of the model, the designed network is trained with the aid of ResNet, layer normalization and learning rate warmup. The proposed model outperforms the state-of-the-art works based on deep learning on two public datasets. The results show that our model has higher accuracy and running efficiency, while the number of parameters used is 1.8% of the 1D convolutional network and the training time halves.

This paper explains a novel caching data protection scheme that uses lightweight blockchain and hashchain for information-centric wireless sensor networks. The proposed verification procedure in a Blockchain is conducted based on the proof-of-consensus validation mechanism without using exhaustive mining computations; therefore, our scheme is suitable for resource-shortage wireless and mobile devices. Hashchains are utilized for traceability and signatures that ensure a block's validity. We make a primitive evaluation of the scheme using computer simulations in familiar low-power wide-area wireless environments.

Information-centric networking (ICN) has played an increasingly important role in the next generation network design. However, to make better use of request-response communication mode in the ICN network, revoke user privileges more efficiently and protect user privacy more safely, an effective access control mechanism is needed. In this paper, we propose IBSS (identity-based secret sharing), which achieves efficient content distribution by using improved Shamir's secret sharing method. At the same time, collusion attacks are avoided by associating polynomials' degree with the number of users. When authenticating user identity and transmitting content, IBE and IBS are introduced to achieve more efficient and secure identity encryption. From the experimental results, the scheme only introduces an acceptable delay in file retrieval, and it can request follow-up content very efficiently.

Despite the latest initiatives and research efforts to increase user privacy in digital scenarios, identity-related cybercrimes such as identity theft, wrong identity or user transactions surveillance are growing. In particular, blanket surveillance that might be potentially accomplished by Identity Providers (IdPs) contradicts the data minimization principle laid out in GDPR. Hence, user movements across Service Providers (SPs) might be tracked by malicious IdPs that become a central dominant entity, as well as a single point of failure in terms of privacy and security, putting users at risk when compromised. To cope with this issue, the OLYMPUS H2020 EU project is devising a truly privacy-preserving, yet user-friendly, and distributed identity management system that addresses the data minimization challenge in both online and offline scenarios. Thus, OLYMPUS divides the role of the IdP among various authorities by relying on threshold cryptography, thereby preventing user impersonation and surveillance from malicious or nosy IdPs. This paper overviews the OLYMPUS framework, including requirements considered, the proposed architecture, a series of use cases as well as the privacy analysis from the legal point of view.

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.

Digital identity is the key element of digital transformation in representing any real-world entity in the digital form. To ensure a successful digital future the requirement for an effective digital identity is paramount, especially as demand increases for digital services. Several Identity Management (IDM) systems are developed to cope with identity effectively, nonetheless, existing IDM systems have some limitations corresponding to identity and its management such as sovereignty, storage and access control, security, privacy and safeguarding, all of which require further improvement. Self-Sovereign Identity (SSI) is an emerging IDM system which incorporates several required features to ensure that identity is sovereign, secure, reliable and generic. It is an evolving IDM system, thus it is essential to analyse its various features to determine its effectiveness in coping with the dynamic requirements of identity and its current challenges. This paper proposes numerous governing principles of SSI to analyse any SSI ecosystem and its effectiveness. Later, based on the proposed governing principles of SSI, it performs a comparative analysis of the two most popular SSI ecosystems uPort and Sovrin to present their effectiveness and limitations.

Managing identity across an ever-growing digital services landscape has become one of the most challenging tasks for security experts. Over the years, several Identity Management (IDM) systems were introduced and adopted to tackle with the growing demand of an identity. In this series, a recently emerging IDM system is Self-Sovereign Identity (SSI) which offers greater control and access to users regarding their identity. This distinctive feature of the SSI IDM system represents a major development towards the availability of sovereign identity to users. uPort is an emerging open-source identity management system providing sovereign identity to users, organisations, and other entities. As an emerging identity management system, it requires meticulous analysis of its architecture, working, operational services, efficiency, advantages and limitations. Therefore, this paper contributes towards achieving all of these objectives. Firstly, it presents the architecture and working of the uPort identity management system. Secondly, it develops a Decentralized Application (DApp) to demonstrate and evaluate its operational services and efficiency. Finally, based on the developed DApp and experimental analysis, it presents the advantages and limitations of the uPort identity management system.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

When communication about security to end users is ineffective, people frequently misinterpret the protection offered by a system. The discrepancy between the security users perceive a system to have and the actual system state can lead to potentially risky behaviors. It is thus crucial to understand how security perceptions are shaped by interface elements such as text-based descriptions of encryption. This article addresses the question of how encryption should be described to non-experts in a way that enhances perceived security. We tested the following within-subject variables in an online experiment (N=309): a) how to best word encryption, b) whether encryption should be described with a focus on the process or outcome, or both c) whether the objective of encryption should be mentioned d) when mentioning the objective of encryption, how to best describe it e) whether a hash should be displayed to the user. We also investigated the role of context (between subjects). The verbs "encrypt" and "secure" performed comparatively well at enhancing perceived security. Overall, participants stated that they felt more secure not knowing about the objective of encryption. When it is necessary to state the objective, positive wording of the objective of encryption worked best. We discuss implications and why using these results to design for perceived lack of security might be of interest as well. This leads us to discuss ethical concerns, and we give guidelines for the design of user interfaces where encryption should be communicated to end users.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods.

Lin et al.'s scheme is a hash function Message Authentication Codes (MAC) block cipher based scheme that's composed of the compression function. Fixed point messages have been found on SMALLPRESENT-[s] algorithm. The vulnerability of block cipher algorithm against fixed point attacks can affect the vulnerability of block cipher based hash function schemes. This paper applies fixed point attack against Lin et al.'s modified scheme based on SMALLPRESENT-[8] algorithm. Fixed point attack was done using fixed point message from SMALLPRESENT-[8] algorithm which used as Initial Value (IV) on the scheme branch. The attack result shows that eight fixed point messages are successfully discovered on the B1 branch. The fixed point messages discovery on B1 and B2 branches form 18 fixed point messages on Lin et al.'s modified scheme with different IVs and keys. The discovery of fixed point messages shows that Lin et al.'s modified scheme is vulnerable to fixed point attack.

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..

This article presents a hardware implementation review of a popular family of hash algorithms: Secure Hash Algorithm 2 (SHA2). It presents various schematic solutions and their assessments for 28 nm CMOS technology. Using this paper we can estimate the expected performance of the hardware hash accelerator based on the IC.

With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.

Software Defined Networking (SDN) focus on the isolation of control plane and data plane, greatly enhancing the network's support for heterogeneity and flexibility. However, although the programmable network greatly improves the performance of all aspects of the network, flexible load balancing across controllers still challenges the current SDN architecture. Complex application scenarios lead to flexible and changeable communication requirements, making it difficult to guarantee the Quality of Service (QoS) for SDN users. To address this issue, this paper proposes a paradigm that uses blockchain to incentive safe load balancing for multiple controllers. We proposed a controller consortium blockchain for secure and efficient load balancing of multi-controllers, which includes a new cryptographic currency balance coin and a novel consensus mechanism Proof-of-Balance (PoB). In addition, we have designed a novel game theory-based incentive mechanism to incentive controllers with tight communication resources to offload tasks to idle controllers. The security analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.

In this study, our aim is to extend the scope for public key cryptography. We offered a new efficient public key encryption scheme using partial discrete logarithm problem (PDLP). It is known as the Quadratic Exponentiation Randomized Public Key Cryptosystem (QERPKC). Security of the presented scheme is based on the hardness of PDLP. We reflect the safety in contrast to trick of certain elements in the offered structure and demonstrated the prospect of creating an extra safety structure. The presented new efficient QERPKC structure is appropriate for low-bandwidth communication, low-storage and low-computation environments.

The modular exponentiation is crucial to the RSA cryptographic protocol, and variants inspired by the Montgomery ladder have been studied to provide more secure algorithms. In this paper, we abstract away the iterative conditional branching used in the Montgomery ladder, and formalize systems of equations necessary to obtain what we call the semi-interleaved and fully-interleaved ladder properties. In particular, we design fault-injection attacks able to obtain bits of the secret against semi-interleaved ladders, including the Montgomery ladder, but not against fully-interleaved ladders that are more secure. We also apply these equations to extend the Montgomery ladder for both the semi- and fully-interleaved cases, thus proposing novel and more secure algorithms to compute the modular exponentiation.

This research aims to implement different modular exponentiation algorithms and evaluate the average complexity and compare it to the theoretical value. We use the library GMP to implement seven modular exponentiation algorithms. They are Left-to-right Square and Multiply, Right-to-left Square and Multiply, Left-to-right Signed Digit Square, and Multiply Left-to-right Square and Multiply Always Right-to-left Square and Multiply Always, Montgomery Ladder and Joye Ladder. For some exponent bit length, we choose 1024 bits and execute each algorithm on many exponent values and count the average numbers of squares and the average number of multiplications. Whenever relevant, our programs will check the consistency relations between the registers at the end of the exponentiation.

As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.

Cloud computing is a vast area within which large amounts of data are exchanged through cloud services and has fully grown with its on-demand technology. Due to these versatile cloud services, sensitive data will be stored on cloud storage servers and it is also used to dynamically control a number of problems: security, privacy, data privacy, data sharing, and integrity across cloud servers. Moreover, the legitimacy and control of data access should be maintained in this extended environment. So, one of the most important concepts of cryptographic techniques in cloud computing environment is Attribute Based Encryption (ABE). In this research work, data auditing or integrity checking is considered as an area of concern for securing th cloud storage. In data auditing approach, an auditor inspects and verifies the data file integrity without having any knowledge about the content of file and sends the verification report to the data owner. In this research, Elliptical Curve Modified RSA (ECMRSA) is proposed along with Modified MD5 algorithm which is used for attribute-based cloud data integrity verification, in which data user or owner uploads their encrypted data files at cloud data server and send the auditing request to the Third-Party Auditor (TPA) for verification of their data files. The Third-Party Auditor (TPA) challenges the data server for ensuring the integrity of data files on behalf of the data owners. After verification of integrity of data file auditor sends the audit report to the owner. The proposed algorithm integrates the auditing scheme with public key encryption with homomorphic algorithm which generates digital signature or hash values of data files on encrypted files. The result analysis is performed on time complexity by evaluating encryption time, GenProof time and VerifyProof Time and achieved improvement in resolving time complexity as compared to existing techiques.