Biblio

While the rapid progress in smart city technologies are changing cities and the lifestyle of the people, there are increasingly enormous challenges in terms of the safety and security of smart cities. The potential vulnerabilities of e-government products and imminent attacks on smart city infrastructure and services will have catastrophic consequences on the governments and can cause substantial economic and noneconomic losses, even chaos, to the cities and their residents. This paper aims to explore alternative economic solutions ranging from incentive mechanisms to market-based solutions to motivate smart city product vendors, governments, and vulnerability researchers and finders to improve the cybersecurity of smart cities.

The notion of edge computing introduces new computing functions away from centralized locations and closer to the network edge and thus facilitating new applications and services. This enhanced computing paradigm is provides new opportunities to applications developers, not available otherwise. In this talk, I will discuss why placing computation functions at the extreme edge of our network infrastructure, i.e., in wireless Access Points and home set-top boxes, is particularly beneficial for a large class of emerging applications. I will discuss a specific approach, called ParaDrop, to implement such edge computing functionalities, and use examples from different domains – smarter homes, sustainability, and intelligent transportation – to illustrate the new opportunities around this concept.

Wireless sensor networks (WSNs) are playing a vital role in collecting data about a natural or built environment. WSNs have attractive advantages such as low-cost, low maintains and flexible arrangements for applications. Wireless sensor network has been used for many different applications such as military implementations in a battlefield, an environmental monitoring, and multifunction in health sector. In order to ensure its functionality, especially in malicious environments, security mechanisms become essential. Especially internal attacks have gained prominence and pose most challenging threats to all WSNs. Although, a number of works have been done to discuss a WSN under the internal attacks it has gained little attention. For example, the conventional cryptographic technique does not give the appropriated security to save the network from internal attack that causes by abnormally behaviour at the legitimate nodes in a network. In this paper, we propose an effective algorithm to make an evaluation for detecting internal attack by multi-criteria in real time. This protecting is based on the combination of the multiple pieces of evidences collected from the nodes under an internal attacker in a network. A theory of the decision is carefully discussed based on the Dempster-Shafer Theory (DST). If you really wanted to make sure the designed network works exactly works as you expected, you will be benefited from this algorithm. The advantage of this proposed method is not just its performance in real-time but also it is effective as it does not need the knowledge about the normal or malicious node in advance with very high average accuracy that is close to 100%. It also can be used as one of maintaining tools for the regulations of the deployed WSNs.

Accounts on web services are always exposed to the menace of attacks. Especially, a large number of accounts can be used for unfair uses such as stealth marketing or SPAM attacks. Needless to say, acquisition of those accounts and attacks are automatically done by software programs called bots. Therefore, a technology called CAPTCHA is usually used in the acquisition of accounts for web services in order to distinguish human beings from bots. The most popular kind of CAPTCHA methods is text-based CAPTCHA in which distorted alphabets and numbers appear with obstacles or noise. However, it is known that all of text-based CAPTCHA algorithms can be analyzed by computers. In addition, too much distortion or noise prevents human beings from alphabets or numbers. There are other kinds of CAPTCHA methods such as image CAPTCHA and audio CAPTCHA. However, they also have problems in use. As a related work, an effective text-based CAPTCHA algorithm was proposed to which amodal completion is applied. The CAPTCHA provides computers a large amount of calculation cost while amodal completion helps human beings to recognize characters momentarily. On the other hand, momentary recognition is uncomfortable for human beings since extreme concentration is required within ten seconds. Therefore, in this paper, we propose an improved algorithm to which amodal completion and aftereffects are applied. The aftereffects extend time for recognition of characters from a moment to several seconds.

The Elliptic Curve Cryptosystems(ECC) are proved to be the cryptosystem of future generation because of its smaller key size and uncompromised security. It is well suited for applications running in resource-restricted devices such as smart cards. At present, there is no efficient algorithm or known sub-exponential algorithm to break ECC theoretically. However, a hardware implementation of ECC leaks secret key information due to power analysis attacks particularly differential power analysis attack(DPA). These attacks break the system with far less effort when compared to all other attacks based on algebraic weaknesses of the algorithms. There are many solutions to overcome the power analysis attack, but all the available solutions have their own advantages and disadvantages by compromising either its security or performance. In this paper, we present a secure and efficient algorithm to solve the elliptic curve scalar multiplication(ECSM) using initial points randomization and by delaying the point addition operation. The implementation results and performance analysis shows that the proposed algorithm is efficient and secure against power analysis attacks.

This paper introduces a design and implementation of a security scheme for the Internet of Things (IoT) based on ECQV Implicit Certificates and Datagram Transport Layer Security (DTLS) protocol. In this proposed security scheme, Elliptic curve cryptography based ECQV implicit certificate plays a key role allowing mutual authentication and key establishment between two resource-constrained IoT devices. We present how IoT devices get ECQV implicit certificates and use them for authenticated key exchange in DTLS. An evaluation of execution time of the implementation is also conducted to assess the efficiency of the solution.

The malicious JavaScript is a common springboard for attackers to launch several types of network attacks, such as Drive-by-Download and malicious PDF delivery attack. In order to elude detection of signature matching, malicious JavaScript is often packed (so-called "obfuscation") with diversified algorithms therefore the occurrence of obfuscation is always a good pointer for potential maliciousness. In this investigation, we propose a light weight approach for quickly filtering obfuscated JavaScript by a novel method of tokenizing JavaScript text at letter level and information-theoretic measures, based on the previous work in the domain of detecting obfuscated malicious code as well as the pattern analysis of natural languages. The new approach is apparently time efficient compared to existing systems since it processes much less objects while it is also proved to be able to reach the acceptable detection accuracies.

Resource discovery in unstructured peer-to-peer networks causes a search query to be flooded throughout the network via random nodes, leading to security and privacy issues. The owner of the search query does not have control over the transmission of its query through the network. Although algorithms have been proposed for policy-compliant query or data routing in a network, these algorithms mainly deal with authentic route computation and do not provide mechanisms to actually verify the network paths taken by the query. In this work, we propose an approach to deal with the problem of verifying network paths taken by a search query during resource discovery, and detection of malicious forwarding of search query. Our approach aims at being secure and yet very scalable, even in the presence of huge number of nodes in the network.

The massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.

The microelectronics industry seeks screening tools that can be used to verify the origin of and track integrated circuits (ICs) throughout their lifecycle. Embedded circuits that measure process variation of an IC are well known. This paper adds to previous work using these circuits for studying manufacturer characteristics on final product ICs, particularly for the purpose of developing and verifying a signature for a microelectronics manufacturing facility (fab). We present the design, measurements and analysis of 159 silicon ICs which were built as a proof of concept for this purpose. 80 copies of our proof of concept IC were built at one fab, and 80 more copies were built across two lots at a second fab. Using these ICs, our prototype circuits allowed us to distinguish these two fabs with up to 98.7% accuracy and also distinguish the two lots from the second fab with up to 98.8% accuracy.
 

The microelectronics industry seeks screening tools that can be used to verify the origin of and track integrated circuits (ICs) throughout their lifecycle. Embedded circuits that measure process variation of an IC are well known. This paper adds to previous work using these circuits for studying manufacturer characteristics on final product ICs, particularly for the purpose of developing and verifying a signature for a microelectronics manufacturing facility (fab). We present the design, measurements and analysis of 159 silicon ICs which were built as a proof of concept for this purpose. 80 copies of our proof of concept IC were built at one fab, and 80 more copies were built across two lots at a second fab. Using these ICs, our prototype circuits allowed us to distinguish these two fabs with up to 98.7% accuracy and also distinguish the two lots from the second fab with up to 98.8% accuracy.

One of the most challenging issues facing academic conferences and educational institutions today is plagiarism detection. Typically, these entities wish to ensure that the work products submitted to them have not been plagiarized from another source (e.g., authors submitting identical papers to multiple journals). Assembling large centralized databases of documents dramatically improves the effectiveness of plagiarism detection techniques, but introduces a number of privacy and legal issues: all document contents must be completely revealed to the database operator, making it an attractive target for abuse or attack. Moreover, this content aggregation involves the disclosure of potentially sensitive private content, and in some cases this disclosure may be prohibited by law. In this work, we introduce Elxa, the first scalable centralized plagiarism detection system that protects the privacy of the submissions. Elxa incorporates techniques from the current state of the art in plagiarism detection, as evaluated by the information retrieval community. Our system is designed to be operated on existing cloud computing infrastructure, and to provide incentives for the untrusted database operator to maintain the availability of the network. Elxa can be used to detect plagiarism in student work, duplicate paper submissions (and their associated peer reviews), similarities between confidential reports (e.g., malware summaries), or any approximate text reuse within a network of private documents. We implement a prototype using the Hadoop MapReduce framework, and demonstrate that it is feasible to achieve competitive detection effectiveness in the private setting.

Embedded virtualization has emerged as a valuable way to reduce costs, improve software quality, and decrease design time. Additionally, virtualization can enforce the overall system's security from several perspectives. One is security due to separation, where the hypervisor ensures that one domain does not compromise the execution of other domains. At the same time, the advances in the development of IoT applications opened discussions about the security flaws that were introduced by IoT devices. In a few years, billions of these devices will be connected to the cloud exchanging information. This is an opportunity for hackers to exploit their vulnerabilities, endangering applications connected to such devices. At this point, it is inevitable to consider virtualization as a possible approach for IoT security. In this paper we discuss how embedded virtualization could take place on IoT devices as a sound solution for security.

The rapid progress of multi-/many-core architectures has caused data-intensive parallel applications not yet be fully suited for getting the maximum performance. The advent of parallel programming frameworks offering structured patterns has alleviated developers' burden adapting such applications to parallel platforms. For example, the use of synchronization mechanisms in multithreaded applications is essential on shared-cache multi-core architectures. However, ensuring an appropriate use of their interfaces can be challenging, since different memory models plus instruction reordering at compiler/processor levels may influence the occurrence of data races. The benefits of race detectors are formidable in this sense, nevertheless if lock-free data structures with no high-level atomics are used, they may emit false positives. In this paper, we extend the ThreadSanitizer race detection tool in order to support semantics of the general Single-Producer/Single-Consumer (SPSC) lock-free parallel queue and to detect benign data races where it was correctly used. To perform our analysis, we leverage the FastFlow SPSC bounded lock-free queue implementation to test our extensions over a set of μ-benchmarks and real applications on a dual-socket Intel Xeon CPU E5-2695 platform. We demonstrate that this approach can reduce, on average, 30% the number of data race warning messages.

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

The Wikidata platform is a crowdsourced, structured knowledgebase aiming to provide integrated, free and language-agnostic facts which are–-amongst others–-used by Wikipedias. Users who actively enter, review and revise data on Wikidata are assisted by a property suggesting system which provides users with properties that might also be applicable to a given item. We argue that evaluating and subsequently improving this recommendation mechanism and hence, assisting users, can directly contribute to an even more integrated, consistent and extensive knowledge base serving a huge variety of applications. However, the quality and usefulness of such recommendations has not been evaluated yet. In this work, we provide the first evaluation of different approaches aiming to provide users with property recommendations in the process of curating information on Wikidata. We compare the approach currently facilitated on Wikidata with two state-of-the-art recommendation approaches stemming from the field of RDF recommender systems and collaborative information systems. Further, we also evaluate hybrid recommender systems combining these approaches. Our evaluations show that the current recommendation algorithm works well in regards to recall and precision, reaching a recall@7 of 79.71% and a precision@7 of 27.97%. We also find that generally, incorporating contextual as well as classifying information into the computation of property recommendations can further improve its performance significantly.

In the cloud storage, users lose direct control over their data. How to surely delete data in the cloud becomes a crucial problem for a secure cloud storage system. The existing way to this problem is to encrypt the data before outsourcing and destroy the encryption key when deleting. However, this solution may cause heavy computation overhead for the user-side and the encrypted data remains intact in the cloud after the deletion operation. To solve this challenge problem, we propose a novel method to surely delete data in the cloud storage by overwriting. Different from existing works, our scheme is efficient in the user-side and is able to wipe out the deleted data from the drives of the cloud servers.

We show for the first time that commodity devices can be used to generate wireless data transmissions that are confined to the human body. Specifically, we show that commodity input devices such as fingerprint sensors and touchpads can be used to transmit information to only wireless receivers that are in contact with the body. We characterize the propagation of the resulting transmissions across the whole body and run experiments with ten subjects to demonstrate that our approach generalizes across different body types and postures. We also evaluate our communication system in the presence of interference from other wearable devices such as smartwatches and nearby metallic surfaces. Finally, by modulating the operations of these input devices, we demonstrate bit rates of up to 50 bits per second over the human body.

While in business and private settings the disruptive impact of advanced information communication technology (ICT) have already been felt, the legal sector is now starting to face great disruptions due to such ICTs. Bits and pieces of innovations in the legal sector have been emerging for some time, affecting the performance of core functions and the legitimacy of public institutions. In this paper, we present our framework for enabling the smart government vision, particularly for the case of criminal justice systems, by unifying different isolated ICT-based solutions. Our framework, coined as Legal Logistics, supports the well-functioning of a legal system in order to streamline the innovations in these legal systems. The framework targets the exploitation of all relevant data generated by the ICT-based solutions. As will be illustrated for the Dutch criminal justice system, the framework may be used to integrate different ICT-based innovations and to gain insights about the well-functioning of the system. Furthermore, Legal Logistics can be regarded as a roadmap towards a smart and open justice.

The state of network security today is quite abysmal. Security breaches and downtime of critical infrastructures continue to be the norm rather than the exception, despite the dramatic rise in spending on network security. Attackers today can easily leverage a distributed and programmable infrastructure of compromised machines (or botnets) to launch large-scale and sophisticated attack campaigns. In contrast, the defenders of our critical infrastructures are fundamentally crippled as they rely on fixed capacity, inflexible, and expensive hardware appliances deployed at designated "chokepoints". These primitive defense capabilities force defenders into adopting weak and static security postures configured for simple and known attacks, or otherwise risk user revolt, as they face unpleasant tradeoffs between false positives and false negatives. Unfortunately, attacks can easily evade these defenses; e.g., piggybacking on popular services (e.g., drive-by-downloads) and by overloading the appliances. Continuing along this trajectory means that attackers will always hold the upper hand as defenders are stifled by the inflexible and impotent tools in their arsenal. An overarching goal of my work is to change the dynamics of this attack-defense equation. Instead of taking a conventional approach of developing attack-specific defenses, I argue that we can leverage recent trends in software-defined networking and network functions virtualization to better empower defenders with the right tools and abstractions to tackle the constantly evolving attack landscape. To this end, I envision a new software-defined approach to network security, where we can rapidly develop and deploy novel in-depth defenses and dynamically customize the network's security posture to the current operating context. In this talk, I will give an overview of our recent work on the basic building blocks to enable this vision as well as some early security capabilities we have developed. Using anecdotes from this specific exercise, I will also try to highlight lessons and experiences in the overall research process (e.g., how to pick and formulate problems, the role of serendipity, and the benefits of finding ``bridges'' to other subdomains).

The web world is been flooded with multi-media sources such as images, videos, animations and audios, which has in turn made the computer vision researchers to focus over extracting the content from the sources. Scene text recognition basically involves two major steps namely Text Localization and Text Recognition. This paper provides end-to-end text recognition approach to extract the characters alone from the complex natural scene. Using Maximal Stable Extremal Region (MSER) the various objects are localized, using Canny Edge detection method edges are identified, further binary classification is done using Connected-Component method which segregates the text and nontext objects and finally the stroke analysis method is applied to analyse the style of the character, leading to the character recognization. The Experimental results were obtained by testing the approach over ICDAR2015 dataset, wherein text was able to be recognized from most of the scene images with good precision value.

A two-factor authenticated key-agreement scheme for session initiation protocol emerged as a best remedy to overcome the ascribed limitations of the password-based authentication scheme. Recently, Lu et al. proposed an anonymous two-factor authenticated key-agreement scheme for SIP using elliptic curve cryptography. They claimed that their scheme is secure against attacks and achieves user anonymity. Conversely, this paper's keen analysis points out several severe security weaknesses of the Lu et al.'s scheme. In addition, this paper puts forward an enhanced anonymous two-factor mutual authenticated key-agreement scheme for session initiation protocol using elliptic curve cryptography. The security analysis and performance analysis sections demonstrates that the proposed scheme is more robust and efficient than Lu et al.'s scheme.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.

Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.

WebRTC is one of the latest additions to the ever growing repository of Web browser technologies, which push the envelope of native Web application capabilities. WebRTC allows real-time peer-to-peer audio and video chat, that runs purely in the browser. Unlike existing video chat solutions, such as Skype, that operate in a closed identity ecosystem, WebRTC was designed to be highly flexible, especially in the domains of signaling and identity federation. This flexibility, however, opens avenues for identity fraud. In this paper, we explore the technical underpinnings of WebRTC's identity management architecture. Based on this analysis, we identify three novel attacks against endpoint authenticity. To answer the identified threats, we propose and discuss defensive strategies, including security improvements for the WebRTC specifications and mitigation techniques for the identity and service providers.