Biblio

Unlike traditional processors, embedded Internet of Things (IoT) devices lack resources to incorporate protection against modern sophisticated attacks resulting in critical consequences. Remote attestation (RA) is a security service to establish trust in the integrity of a remote device. While conventional RA is static and limited to detecting malicious modification to software binaries at load-time, recent research has made progress towards runtime attestation, such as attesting the control flow of an executing program. However, existing control-flow attestation schemes are inefficient and vulnerable to sophisticated data-oriented programming (DOP) attacks subvert these schemes and keep the control flow of the code intact. In this paper, we present LiteHAX, an efficient hardware-assisted remote attestation scheme for RISC-based embedded devices that enables detecting both control-flow attacks as well as DOP attacks. LiteHAX continuously tracks both the control-flow and data-flow events of a program executing on a remote device and reports them to a trusted verifying party. We implemented and evaluated LiteHAX on a RISC-V System-on-Chip (SoC) and show that it has minimal performance and area overhead.

In blockchain-based systems, malicious behaviour can be detected using auditable information in transactions managed by distributed ledgers. Besides cryptocurrency, blockchain technology has recently been used for other applications, such as file storage. However, most of existing blockchain- based file storage systems can not revoke a user efficiently when multiple users have access to the same file that is encrypted. Actually, they need to update file encryption keys and distribute new keys to remaining users, which significantly increases computation and bandwidth overheads. In this work, we propose a blockchain and proxy re-encryption based design for encrypted file sharing that brings a distributed access control and data management. By combining blockchain with proxy re-encryption, our approach not only ensures confidentiality and integrity of files, but also provides a scalable key management mechanism for file sharing among multiple users. Moreover, by storing encrypted files and related keys in a distributed way, our method can resist collusion attacks between revoked users and distributed proxies.

The need to process the verity, volume and velocity of data generated by today's Internet of Things (IoT) devices has pushed both academia and the industry to investigate new architectural alternatives to support the new challenges. As a result, Edge Computing (EC) has emerged to address these issues, by placing part of the cloud resources (e.g., computation, storage, logic) closer to the edge of the network, which allows faster and context dependent data analysis and storage. However, as EC infrastructures grow, different providers who do not necessarily trust each other need to collaborate in order serve different IoT devices. In this context, EC infrastructures, IoT devices and the data transiting the network all need to be subject to identity and provenance checks, in order to increase trust and accountability. Each device/data in the network needs to be identified and the provenance of its actions needs to be tracked. In this paper, we propose a blockchain container based architecture that implements the W3C-PROV Data Model, to track identities and provenance of all orchestration decisions of a business network. This architecture provides new forms of interaction between the different stakeholders, which supports trustworthy transactions and leads to a new decentralized interaction model for IoT based applications.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is untrusted and unreliable network and thus IoT network is vulnerable to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So, it is necessary to design intrusion detection system for such network. In our paper, we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our network from these attacks. We designed detection and prevention algorithm, i.e., KMA (Key Match Algorithm) and CBA (Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50% to 80% with KMA and 76% to 96% with CBA algorithm.

Application whitelisting software allows only examined and trusted applications to run on user's machine. Since many malicious files don't require administrative privileges in order for them to be executed, whitelisting can be the only way to block the execution of unauthorized applications in enterprise environment and thus prevent infection or data breach. In order to assess the current state of such solutions, the access to three whitelisting solution licenses was obtained with the purpose to test their effectiveness against different modern types of ransomware found in the wild. To conduct this study a virtual environment was used with Windows Server and Enterprise editions installed. The objective of this paper is not to evaluate each vendor or make recommendations of purchasing specific software but rather to assess the ability of application control solutions to block execution of ransomware files, as well as assess the potential for future research. The results of the research show the promise and effectiveness of whitelisting solutions.

Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.

Nowadays, the Internet is developed, so that the requirements for on- and offline data storage have increased. Large storage IT projects, are related to large costs and high level of business risk. A storage service provider (SSP) provides computer storage space and management. In addition to that, it offers also back-up and archiving. Despite this, many companies fears security, privacy and integrity of outsourced data. As a solution, File Assured Deletion (FADE) is a system built upon standard cryptographic issues. It aims to guarantee their privacy and integrity, and most importantly, assuredly deleted files to make them unrecoverable to anybody (including those who manage the cloud storage) upon revocations of file access policies, by encrypting outsourced data files. Unfortunately, This system remains weak, in case the key manager's security is compromised. Our work provides a new scheme that aims to improve the security of FADE by using the TPM (Trusted Platform Module) that stores safely keys, passwords and digital certificates.

In an Internet of Things (IOT) network, each node (device) provides and requires services and with the growth in IOT, the number of nodes providing the same service have also increased, thus creating a problem of selecting one reliable service from among many providers. In this paper, we propose a scalable graph-based collaborative filtering recommendation algorithm, improved using trust to solve service selection problem, which can scale to match the growth in IOT unlike a central recommender which fails. Using this recommender, a node can predict its ratings for the nodes that are providing the required service and then select the best rated service provider.

In the open network environment, the strange entities can establish the mutual trust through Automated Trust Negotiation (ATN) that is based on exchanging digital credentials. In traditional ATN, the attribute certificate required to either satisfied or not, and in the strategy, the importance of the certificate is same, it may cause some unnecessary negotiation failure. And in the actual situation, the properties is not just 0 or 1, it is likely to between 0 and 1, so the satisfaction degree is different, and the negotiation strategy need to be quantified. This paper analyzes the fuzzy negotiation process, in order to improve the trust establishment in high efficiency and accuracy further.

Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.

Current technologies to include cloud computing, social networking, mobile applications and crowd and synthetic intelligence, coupled with the explosion in storage and processing power, are evolving massive-scale marketplaces for a wide variety of resources and services. They are also enabling unprecedented forms and levels of collaborations among human and machine entities. In this new era, trust remains the keystone of success in any relationship between two or more parties. A primary challenge is to establish and manage trust in environments where massive numbers of consumers, providers and brokers are largely autonomous with vastly diverse requirements, capabilities, and trust profiles. Most contemporary trust management solutions are oblivious to diversities in trustors' requirements and contexts, utilize direct or indirect experiences as the only form of trust computations, employ hardcoded trust computations and marginally consider collaboration in trust management. We surmise the need for reference architecture for trust management to guide the development of a wide spectrum of trust management systems. In our previous work, we presented a preliminary reference architecture for trust management which provides customizable and reconfigurable trust management operations to accommodate varying levels of diversity and trust personalization. In this paper, we present a comprehensive taxonomy for trust management and extend our reference architecture to feature collaboration as a first-class object. Our goal is to promote the development of new collaborative trust management systems, where various trust management operations would involve collaborating entities. Using the proposed architecture, we implemented a collaborative personalized trust management system. Simulation results demonstrate the effectiveness and efficiency of our system.

We propose an approach to enforce security in disruption- and delay-tolerant networks (DTNs) where long delays, high packet drop rates, unavailability of central trusted entity etc. make traditional approaches unfeasible. We use trust model based on subjective logic to continuously evaluate trustworthiness of security credentials issued in distributed manner by network participants to deal with absence of centralised trusted authorities.

Untrusted third-party vendors and manufacturers have raised security concerns in hardware supply chain. Among all existing solutions, formal verification methods provide powerful solutions in detection malicious behaviors at the pre-silicon stage. However, little work have been done towards built-in hardware runtime verification at the post-silicon stage. In this paper, a runtime formal verification framework is proposed to evaluate the trust of hardware during its execution. This framework combines the symbolic execution and SAT solving methods to validate the user defined properties. The proposed framework has been demonstrated on an FPGA platform using an SoC design with untrusted IPs. The experimentation results show that the proposed approach can provide high-level security assurance for hardware at runtime.

Software attacks are commonly performed against embedded systems in order to access private data or to run restricted services. In this work, we demonstrate some vulnerabilities of commonly use processor which can be leveraged by hackers to attack a system. The targeted devices are based on open processor architectures OpenRISC and RISC-V. Several software exploits are discussed and demonstrated while a hardware countermeasure is proposed and validated on OpenRISC against Return Oriented Programming attack.

Code signing which at present is the only methodology of trusting a code that is distributed to others. It heavily relies on the security of the software providers private key. Attackers employ targeted attacks on the code signing infrastructure for stealing the signing keys which are used later for distributing malware in disguise of genuine software. Differentiating a malware from a benign software becomes extremely difficult once it gets signed by a trusted software providers private key as the operating systems implicitly trusts this signed code. In this paper, we analyze the growing menace of signed malware by examining several real world incidents and present a threat model for the current code signing infrastructure. We also propose a novel solution that prevents this issue of malicious code signing by requiring additional verification of the executable. We also present the serious threat it poses and it consequences. To our knowledge this is the first time this specific issue of Malicious code signing has been thoroughly studied and an implementable solution is proposed.

Recently perceived vulnerabilities in public key infrastructures (PKI) demand that a semantic or cognitive definition of trust is essential for augmenting the security through trust formulations. In this paper, we examine the meaning of trust in PKIs. Properly categorized trust can help in developing intelligent algorithms that can adapt to the security and privacy requirements of the clients. We delineate the different types of trust in a generic PKI model.

Different organizations or countries maybe adopt different PKI trust model in real applications. On a large scale, all certification authorities (CA) and end entities construct a huge mesh network. PKI trust model exhibits unstructured mesh network as a whole. However, mesh trust model worsens computational complexity in certification path processing when the number of PKI domains increases. This paper proposes an enhanced mesh trust model for PKI. Keys generation and signature are fulfilled in Trusted Platform Module (TPM) for higher security level. An algorithm is suggested to improve the performance of certification path processing in this model. This trust model is less complex but more efficient and robust than the existing PKI trust models.

New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model.

Software Defined Networking (SDN) is an emerging paradigm that changes the way networks are managed by separating the control plane from data plane and making networks programmable. The separation brings about flexibility, automation, orchestration and offers savings in both capital and operational expenditure. Despite all the advantages offered by SDN it introduces new threats that did not exist before or were harder to exploit in traditional networks, making network penetration potentially easier. One of the key threat to SDN is the authentication and authorisation of network applications that control network behaviour (unlike the traditional network where network devices like routers and switches are autonomous and run proprietary software and protocols to control the network). This paper proposes a mechanism that helps the control layer authenticate network applications and set authorisation permissions that constrict manipulation of network resources.

Wireless sensor network (WSN) considered as one of the important technology in our days. Low-cost, low-power and multifunction based on these characteristics WSN become more and more apply in many areas. However, one of the major challenges in WSN is the security. Indeed, the usual method of security cannot be applied in WSN because the technological limit of the different components. In this context, we propose a new method to establish a secure route between the source node and the Sink node. Particularly, our method based on routing trust history table (RTH) and trust path routing algorithm (TPR). Therefore, our method offers a high level of security for the routing path with efficiency and stability in the network.

Semiconductor design houses are increasingly becoming dependent on third party vendors to procure intellectual property (IP) and meet time-to-market constraints. However, these third party IPs cannot be trusted as hardware Trojans can be maliciously inserted into them by untrusted vendors. While different approaches have been proposed to detect Trojans in third party IPs, their limitations have not been extensively studied. In this paper, we analyze the limitations of the state-of-the-art Trojan detection techniques and demonstrate with experimental results how to defeat these detection mechanisms. We then propose a Trojan detection framework based on information flow security (IFS) verification. Our framework detects violation of IFS policies caused by Trojans without the need of white-box knowledge of the IP. We experimentally validate the efficacy of our proposed technique by accurately identifying Trojans in the trust-hub benchmarks. We also demonstrate that our technique does not share the limitations of the previously proposed Trojan detection techniques.

Client-side JavaScript has become ubiquitous in web applications to improve user experience and reduce server load. However, since clients are untrusted, servers cannot rely on the confidentiality or integrity of client-side JavaScript code and the data that it operates on. For example, client-side input validation must be repeated at server side, and confidential business logic cannot be offloaded. In this paper, we present TrustJS, a framework that enables trustworthy execution of security-sensitive JavaScript inside commodity browsers. TrustJS leverages trusted hardware support provided by Intel SGX to protect the client-side execution of JavaScript, enabling a flexible partitioning of web application code. We present the design of TrustJS and provide initial evaluation results, showing that trustworthy JavaScript offloading can further improve user experience and conserve more server resources.

The Semantic Web today is a web that allows for intelligent knowledge retrieval by means of semantically annotated tags. This web also known as Intelligent web aims to provide meaningful information to man and machines equally. However, the information thus provided lacks the component of trust. Therefore we propose a method to embed trust in semantic web documents by the concept of provenance which provides answers to who, when, where and by whom the documents were created or modified. This paper demonstrates the same using the Manchester approach of provenance implemented in a University Ontology.

Trust Management (TM) systems for authentication are vital to the security of online interactions, which are ubiquitous in our everyday lives. Various systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage trust in this setting. In recent years, blockchain technology has been introduced as a panacea to our security problems, including that of authentication, without sufficient reasoning, as to its merits.In this work, we investigate the merits of using open distributed ledgers (ODLs), such as the one implemented by blockchain technology, for securing TM systems for authentication. We formally model such systems, and explore how blockchain can help mitigate attacks against them. After formal argumentation, we conclude that in the context of Trust Management for authentication, blockchain technology, and ODLs in general, can offer considerable advantages compared to previous approaches. Our analysis is, to the best of our knowledge, the first to formally model and argue about the security of TM systems for authentication, based on blockchain technology. To achieve this result, we first provide an abstract model for TM systems for authentication. Then, we show how this model can be conceptually encoded in a blockchain, by expressing it as a series of state transitions. As a next step, we examine five prevalent attacks on TM systems, and provide evidence that blockchain-based solutions can be beneficial to the security of such systems, by mitigating, or completely negating such attacks.