Biblio

While much of the discussion around supply chain security has focused on the parts, components and gear that make up an organization's physical IT assets, a growing number of experts are making the case that vulnerabilities in the software supply chain may represent the larger cybersecurity threat over the long haul.

Single sign-on (SSO) is becoming more and more popular in the Internet. An SSO ticket issued by the identity provider (IdP) allows an entity to sign onto a relying party (RP) on behalf of the account enclosed in the ticket. To ensure its authenticity, an SSO ticket is digitally signed by the IdP and verified by the RP. However, recent security incidents indicate that a signing system (e.g., certification authority) might be compromised to sign fraudulent messages, even when it is well protected in accredited commercial systems. Compared with certification authorities, the online signing components of IdPs are even more exposed to adversaries and thus more vulnerable to such threats in practice. This paper proposes ticket transparency to provide accountable SSO services with privacy-preserving public logs against potentially fraudulent tickets issued by a compromised IdP. With this scheme, an IdP-signed ticket is accepted by the RP only if it is recorded in the public logs. It enables a user to check all his tickets in the public logs and detect any fraudulent ticket issued without his participation or authorization. We integrate blind signatures, identity-based encryption and Bloom filters in the design, to balance transparency, privacy and efficiency in these security-enhanced SSO services. To the best of our knowledge, this is the first attempt to solve the security problems caused by potentially intruded or compromised IdPs in the SSO services.

Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.

Safety is one of basic human needs so we need a security system that able to prevent crime happens. Commonly, we use surveillance video to watch environment and human behaviour in a location. However, the surveillance video can only used to record images or videos with no additional information. Therefore we need more advanced camera to get another additional information such as human position and movement. This research were able to extract those information from surveillance video footage by using human detection and tracking algorithm. The human detection framework is based on Deep Learning Convolutional Neural Networks which is a very popular branch of artificial intelligence. For tracking algorithms, channel and spatial correlation filter is used to track detected human. This system will generate and export tracked movement on footage as an additional information. This tracked movement can be analysed furthermore for another research on surveillance video problems.

Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.

Smart Grids require extensive communication to enable safe and stable energy supply in the age of decentralized and dynamic energy production and consumption. To protect the communication in this critical infrastructure, public authorities mandate smart meter gateways (SMGWs) to be in control of the communication security. To this end, the SMGW intercepts all inbound and outbound communication of its premise, e.g., a factory or smart home, and forwards it on secure channels that the SMGW established itself. However, using the SMGW as proxy, local devices can neither review the security of these remote connections established by the SMGW nor enforce higher security guarantees than established by the all in one configuration of the SMGW which does not allow for use case-specific security settings. We present mechanisms that enable local devices to regain this insight and control over the full connection, i.e., up to the final receiver, while retaining the SMGW's ability to ensure a suitable security level. Our evaluation shows modest computation and transmission overheads for this increased security in the critical smart grid infrastructure.

We present Diana, an embodied agent who is aware of her own virtual space and the physical space around her. Using video and depth sensors, Diana attends to the user's gestures, body language, gaze and (soon) facial expressions as well as their words. Diana also gestures and emotes in addition to speaking, and exists in a 3D virtual world that the user can see. This produces symmetric and shared perception, in the sense that Diana can see the user, the user can see Diana, and both can see the virtual world. The result is an embodied agent that begins to develop the conceit that the user is interacting with a peer rather than a program.

Style transfer is an emerging trend in the fields of deep learning's applications, especially in images and audio data this is proven very useful and sometimes the results are astonishing. Gradually styles of textual data are also being changed in many novel works. This paper focuses on the transfer of the sentimental vibe of a sentence. Given a positive clause, the negative version of that clause or sentence is generated keeping the context same. The opposite is also done with negative sentences. Previously this was a very tough job because the go-to techniques for such tasks such as Recurrent Neural Networks (RNNs) [1] and Long Short-Term Memories(LSTMs) [2] can't perform well with it. But since newer technologies like Generative Adversarial Network(GAN) and Variational AutoEncoder(VAE) are emerging, this work seem to become more and more possible and effective. In this paper, Multi-Genarative Variational Auto-Encoder is employed to transfer sentiment values. Inspite of working with a small dataset, this model proves to be promising.

The advances in natural language processing and the wide use of social networks have boosted the proliferation of chatbots. These are software services typically embedded within a social network, and which can be addressed using conversation through natural language. Many chatbots exist with different purposes, e.g., to book all kind of services, to automate software engineering tasks, or for customer support. In previous work, we proposed the use of chatbots for domain-specific modelling within social networks. In this short paper, we report on the needs for flexible modelling required by modelling using conversation. In particular, we propose a process of meta-model relaxation to make modelling more flexible, followed by correction steps to make the model conforming to its meta-model. The paper shows how this process is integrated within our conversational modelling framework, and illustrates the approach with an example.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

Cloud storage solutions have gained momentum in recent years. However, cloud servers can not be fully trusted. Data access control have becomes one of the main impediments for further adoption. One appealing approach is to incorporate the access control into encrypted data, thus removing the need to trust the cloud servers. Among existing cryptographic solutions, Ciphertext Policy Attribute-Based Encryption (CP-ABE) is well suited for fine-grained data access control in cloud storage. As promising as it is, user revocation is a cumbersome problem that impedes its wide application. To address this issue, we design an access control system called DUR-CP-ABE, which implements identity-based User Revocation in a data owner Discretionary way. In short, the proposed solution provides the following salient features. First, user revocation enforcement is based on the discretion of the data owner, thus providing more flexibility. Second, no private key updates are needed when user revocation occurs. Third, the proposed scheme allows for group revocation of affiliated users in a batch operation. To the best of our knowledge, DUR-CP-ABE is the first CP-ABE solution to provide affiliation- based batch revocation functionality, which fits naturally into organizations' Identity and Access Management (IAM) structure. The analysis shows that the proposed access control system is provably secure and efficient in terms of computation, communi- cation and storage.

The rise of connectivity, digitalization, robotics, and artificial intelligence (AI) is rapidly changing our society and shaping its future development. During this technological and societal revolution, security has been persistently neglected, yet a hacked robot can act as an insider threat in organizations, industries, public spaces, and private homes. In this paper, we perform a structured security assessment of Pepper, a commercial humanoid robot. Our analysis, composed by an automated and a manual part, points out a relevant number of security flaws that can be used to take over and command the robot. Furthermore, we suggest how these issues could be fixed, thus, avoided in the future. The very final aim of this work is to push the rise of the security level of IoT products before they are sold on the public market.

Hardware security architectures and primitives are becoming increasingly important in practice providing trust anchors and trusted execution environment to protect modern software systems. Over the past two decades we have witnessed various hardware security solutions and trends from Trusted Platform Modules (TPM), performance counters for security, ARM's TrustZone, and Physically Unclonable Functions (PUFs), to very recent advances such as Intel's Software Guard Extension (SGX). Unfortunately, these solutions are rarely used by third party developers, make strong trust assumptions (including in manufacturers), are too expensive for small constrained devices, do not easily scale, or suffer from information leakage. Academic research has proposed a variety of solutions, in hardware security architectures, these advancements are rarely deployed in practice.

With the advancement in technology, inclusion of Information and Communication Technology (ICT) in the conventional Electrical Power Grid has become evident. The combination of communication system with physical system makes it cyber-physical system (CPS). Though the advantages of this improvement in technology are numerous, there exist certain issues with the system. Security and privacy concerns of a CPS are a major field and research and the insight of which is content of this paper.

With the development of the grid and more and more attention attached to the privacy security, there is an urgent need of a secure anonymous authentication mechanism. In order to meet this requirement, we proposed an anonymous authentication mechanism based on Kerberos and HIBC, which is called KHIBC. It can meet the demand of authentication of Grid. At the same time, it can also protect the users' identity through anonymous method. Through analysis, KHIBC can meet the requirement of anonymity, mutual authentication, traceability and so on.

Verification of security policies represents one of the most critical, complex, and expensive steps of modern SoC design validation. SoC security policies are typically implemented as part of functional design flow, with a diverse set of protection mechanisms sprinkled across various IP blocks. An obvious upshot is that their verification requires comprehension and analysis of the entire system, representing a scalability bottleneck for verification tools. The scale and complexity of industrial SoC is far beyond the analysis capacity of state-of-the-art formal tools; even simulation-based security verification is severely limited in effectiveness because of the need to exercise subtle corner-cases across the entire system. We address this challenge by developing a novel security architecture that accounts for verification needs from the ground up. Our framework, ArtiFact, provides an alternative architecture for security policy implementation that exploits a flexible, centralized, infrastructure IP and enables scalable, streamlined verification of these policies. With our architecture, verification of system-level security policies reduces to analysis of this single IP and its interfaces, enabling off-the-shelf formal tools to successfully verify these policies. We introduce a CAD flow that supports both formal and dynamic (simulation-based) verification, and is built on top of such off-the-shelf tools. Our approach reduces verification time by over 62X and bug detection time by 34X for illustrative policies.

In this work, we perform security analysis of using an e-mail as a self-service password reset point, and exploit some of the vulnerabilities of e-mail servers' forgotten password reset paths. We perform and illustrate three different attacks on a personal Email account, using a variety of tools such as: public knowledge attainable through social media or public records to answer security questions and execute a social engineering attack, hardware available to the public to perform a man in the middle attack, and free software to perform a brute-force attack on the login of the email account. Our results expose some of the inherent vulnerabilities in using emails as password reset points. The findings are extremely relevant to the security of mobile devices since users' trend has leaned towards usage of mobile devices over desktops for Internet access.

Producing an editable model texture could be a challenging problem if the model is scanned from real world or generated by multi-view reconstruction algorithm. To solve this problem, we present a novel re-topology and UV remapping method based on neural network, which transforms arbitrary models with textured coordinates to a semi-regular meshes, and keeps models texture and removes the influence of lighting information. The main innovation of this paper is to use a neural network to find the appropriate location of the starting and ending points for models in the UV maps. Then each fragmented mesh is projected to the 2D planar domain. After calculating and optimizing the orientation field, a semi-regular mesh for each patch is then generated. Those patches can be projected back to three-dimension space and be spliced to a complete mesh. Experiments show that our method can achieve satisfactory performance.

Network measurements are essential for troubleshooting and active management of networks. Protocol analysis of captured network packet traffic is an important passive network measurement technique used by researchers and network operations engineers. In this work, we present a measurement workbench tool named BITS Darshini (Darshini in short) to enable scientific network measurements. We have created Darshini as a modular, concurrent web application that stores experimental meta-data and allows users to specify protocol parse graphs. Darshini performs protocol analysis on a concurrent pipeline architecture, persists the analysis to a database and provides the analysis results via a REST API service. We formulate the problem of mapping protocol parse graph to a concurrent pipeline as a graph embedding problem. Our tool, Darshini, performs protocol analysis up to transport layer and is suitable for the study of small and medium-sized networks. Darshini enables secure collaboration and consultations with experts.

Detecting violence in videos through automatic means is significant for law enforcement and analysis of surveillance cameras with the intent of maintaining public safety. Moreover, it may be a great tool for protecting children from accessing inappropriate content and help parents make a better informed decision about what their kids should watch. However, this is a challenging problem since the very definition of violence is broad and highly subjective. Hence, detecting such nuances from videos with no human supervision is not only technical, but also a conceptual problem. With this in mind, we explore how to better describe the idea of violence for a convolutional neural network by breaking it into more objective and concrete parts. Initially, our method uses independent networks to learn features for more specific concepts related to violence, such as fights, explosions, blood, etc. Then we use these features to classify each concept and later fuse them in a meta-classification to describe violence. We also explore how to represent time-based events in still-images as network inputs; since many violent acts are described in terms of movement. We show that using more specific concepts is an intuitive and effective solution, besides being complementary to form a more robust definition of violence. When compared to other methods for violence detection, this approach holds better classification quality while using only automatic features.

In 2009, Craig Gentry introduced the first "fully" homomorphic encryption scheme allowing arbitrary circuits to be evaluated on encrypted data. Homomorphic encryption is a very powerful cryptographic primitive, though it has often been viewed by practitioners as too inefficient for practical applications. However, the performance of these encryption schemes has come a long way from that of Gentry's original work: there are now several well-maintained libraries implementing homomorphic encryption schemes and protocols demonstrating impressive performance results, alongside an ongoing standardization effort by the community. In this tutorial we survey the existing homomorphic encryption landscape, providing both a general overview of the state of the art, as well as a deeper dive into several of the existing libraries. We aim to provide a thorough introduction to homomorphic encryption accessible by the broader computer security community. Several of the presenters are core developers of well-known publicly available homomorphic encryption libraries, and organizers of the homomorphic encryption standardization effort \textbackslashtextbackslashhrefhttp://homomorphicencryption.org/. This tutorial is targeted at application developers, security researchers, privacy engineers, graduate students, and anyone else interested in learning the basics of modern homomorphic encryption.The tutorial is divided into two parts: Part I is accessible by everyone comfortable with basic college-level math; Part II will cover more advanced topics, including descriptions of some of the different homomorphic encryption schemes and libraries, concrete example applications and code samples, and a deeper discussion on implementation challenges. Part II requires the audience to be familiar with modern C++.

In this paper, a framework for Moving Target Defense is introduced. This framework bases on three pillars: network address mutation, communication stack randomization and the dynamic deployment of decoys. The network address mutation is based on the concept of domain generation algorithms, where different features are included to fulfill the system requirements. Those requirements are time dependency, unpredictability and determinism. Communication stack randomization is applied additionally to increase the complexity of reconnaissance activity. By employing communication stack randomization, previously fingerprinted systems do not only differ in the network address but also in their communication pattern behavior. And finally, decoys are integrated into the proposed framework to detect attackers that have breached the perimeter. Furthermore, attacker's resources can be bound by interacting with the decoy systems. Additionally, the framework can be extended with more advanced Moving Target Defense methods such as obscuring port numbers of services.

Real-Time Ethernet has become the major communication technology for modern automation and industrial control systems. On the one hand, this trend increases the need for an automation-friendly security solution, as such networks can no longer be considered sufficiently isolated. On the other hand, it shows that, despite diverging requirements, the domain of Operational Technology (OT) can derive advantage from high-volume technology of the Information Technology (IT) domain. Based on these two sides of the same coin, we study the challenges and prospects of approaches to communication security in real-time Ethernet automation systems. In order to capitalize the expertise aggregated in decades of research and development, we put a special focus on the reuse of well-established security technology from the IT domain. We argue that enhancing such technology to become automation-friendly is likely to result in more robust and secure designs than greenfield designs. Because of its widespread deployment and the (to this date) nonexistence of a consistent security architecture, we use PROFINET as a showcase of our considerations. Security requirements for this technology are defined and different well-known solutions are examined according their suitability for PROFINET. Based on these findings, we elaborate the necessary adaptions for the deployment on PROFINET.

In order to cater the growing spectrum demands of large scale future 5G Internet of Things (IoT) applications, Dynamic Spectrum Access (DSA) based networks are being proposed as a high-throughput and cost-effective solution. However the lack of understanding of DSA paradigm's inherent security vulnerabilities on IoT networks might become a roadblock towards realizing such spectrum aware 5G vision. In this paper, we make an attempt to understand how such inherent DSA vulnerabilities in particular Spectrum Sensing Data Falsification (SSDF) attacks can be exploited by collaborative group of selfish adversaries and how that can impact the performance of spectrum aware IoT applications. We design a utility based selfish adversarial model mimicking collaborative SSDF attack in a cooperative spectrum sensing scenario where IoT networks use dedicated environmental sensing capability (ESC) for spectrum availability estimation. We model the interactions between the IoT system and collaborative selfish adversaries using a leader-follower game and investigate the existence of equilibrium. Using simulation results, we show the nature of adversarial and system utility components against system variables. We also explore Pareto-optimal adversarial strategy design that maximizes the attacker utility for varied system strategy spaces.

Secure naming systems, or more narrowly public key infrastructures (PKIs), form the basis of secure communications over insecure networks. All security guarantees against active attackers come from a trustworthy binding between user-facing names, such as domain names, to cryptographic identities, such as public keys. By offering a secure, distributed ledger with highly decentralized trust, blockchains such as Bitcoin show promise as the root of trust for naming systems with no central trusted parties. PKIs based upon blockchains, such as Namecoin and Blockstack, have greatly improved security and resilience compared to traditional centralized PKIs. Yet blockchain PKIs tend to significantly sacrifice scalability and flexibility in pursuit of decentralization, hindering large-scale deployability on the Internet. We propose Conifer, a novel PKI with an architecture based upon CONIKS, a centralized transparency-based PKI, and Catena, a blockchain-agnostic way of embedding a permissioned log, but with a different lookup strategy. In doing so, Conifer achieves decentralized trust with security at least as strong as existing blockchain-based naming systems, yet without sacrificing the flexibility and performance typically found in centralized PKIs. We also present our reference implementation of Conifer, demonstrating how it can easily be integrated into applications. Finally, we use experiments to evaluate the performance of Conifer compared with other naming systems, both centralized and blockchain-based, demonstrating that it incurs only a modest overhead compared to traditional centralized-trust systems while being far more scalable and performant than purely blockchain-based solutions.