Biblio

Software permeates every aspect of our world, from our homes to the infrastructure that provides mission-critical services. As the size and complexity of software systems increase, the number and sophistication of software security flaws increase as well. The analysis of these flaws began as a manual approach, but it soon became apparent that a manual approach alone cannot scale, and that tools were necessary to assist human experts in this task, resulting in a number of techniques and approaches that automated certain aspects of the vulnerability analysis process. Recently, DARPA carried out the Cyber Grand Challenge, a competition among autonomous vulnerability analysis systems designed to push the tool-assisted human-centered paradigm into the territory of complete automation, with the hope that, by removing the human factor, the analysis would be able to scale to new heights. However, when the autonomous systems were pitted against human experts it became clear that certain tasks, albeit simple, could not be carried out by an autonomous system, as they require an understanding of the logic of the application under analysis. Based on this observation, we propose a shift in the vulnerability analysis paradigm, from tool-assisted human-centered to human-assisted tool-centered. In this paradigm, the automated system orchestrates the vulnerability analysis process, and leverages humans (with different levels of expertise) to perform well-defined sub-tasks, whose results are integrated in the analysis. As a result, it is possible to scale the analysis to a larger number of programs, and, at the same time, optimize the use of expensive human resources. In this paper, we detail our design for a human-assisted automated vulnerability analysis system, describe its implementation atop an open-sourced autonomous vulnerability analysis system that participated in the Cyber Grand Challenge, and evaluate and discuss the significant improvements that non-expert human assistance can offer to automated analysis approaches.

Information-leakage is one of the most important security issues in the current Internet. In Named-Data Networking (NDN), Interest names introduce novel vulnerabilities that can be exploited. By setting up a malware, Interest names can be used to encode critical information (steganography embedded) and to leak information out of the network by generating anomalous Interest traffic. This security threat based on Interest names does not exist in IP network, and it is essential to solve this issue to secure the NDN architecture. This paper performs risk analysis of information-leakage in NDN. We first describe vulnerabilities with Interest names and, as countermeasures, we propose a name-based filter using search engine information, and another filter using one-class Support Vector Machine (SVM). We collected URLs from the data repository provided by Common Crawl and we evaluate the performances of our per-packet filters. We show that our filters can choke drastically the throughput of information-leakage, which makes it easier to detect anomalous Interest traffic. It is therefore possible to mitigate information-leakage in NDN network and it is a strong incentive for future deployment of this architecture at the Internet scale.

Faced with a turbulent economic, political and social environment, Companies need to build effective risk management systems in their supply chains. Risk management can only be effective when the risks identification and analysis are enough accurate. In this perspective, this paper proposes a risk assessment approach based on the analytic hierarchy process and group decision making. In this study, a new method is introduced that will reduce the impact of incoherent judgments on group decision-making, It is, the “reduced weight function” that decreases the weight associated to a member of the expert panel based on the consistency of its judgments.

Biometric authentication has been extremely popular in large scale industries. The face biometric has been used widely in various applications. Handling large numbers of face images is a challenging task in authentication of biometric system. It requires large amount of secure storage, where the registered user information can be stored. Maintaining centralized data centers to store the information requires high investment and maintenance cost, therefore there is a need for deployment of cloud services. However as there is no guaranty of the security in the cloud, user needs to implement an additional or extra layer of security before storing facial data of all registered users. In this work a unique cloud based biometric authentication system is developed using Microsoft cognitive face API. Because most of the cloud based biometric techniques are scalable it is paramount to implement a security technique which can handle the scalability. Any users can use this system for single enterprise application base over the entire enterprise application. In this work the identification number which is text information associated with each biometric image is protected by AES algorithm. The proposed technique also works under distributed system in order to have wider accessibility. The system is also being extended to validate the registered user with an image of aadhar card. An accuracy of 96% is achieved with 100 registered users face images and aadhar card images. Earlier research carried out for the development of biometric system either suffers from development of distributed system are security aspects to handle multiple biometric information such as facial image and aadhar card image.

In this paper, we present a decentralized nonlinear robust controller to enhance the transient stability margin of synchronous generators. Although, the trend in power system control is shifting towards centralized or distributed controller approaches, the remote data dependency of these schemes fuels cyber-physical security issues. Since the excessive delay or losing remote data affect severely the operation of those controllers, the designed controller emerges as an alternative for stabilization of Smart Grids in case of unavailability of remote data and in the presence of plant parametric uncertainties. The proposed controller actuates distributed storage systems such as flywheels in order to reduce stabilization time and it implements a novel input time delay compensation technique. Lyapunov stability analysis proves that all the tracking error signals are globally uniformly ultimately bounded. Furthermore, the simulation results demonstrate that the proposed controller outperforms traditional local power systems controllers such as Power System Stabilizers.

Recently, matrix factorization has produced state-of-the-art results in recommender systems. However, given the typical sparsity of ratings, the often large problem scale, and the large number of free parameters that are often implied, developing robust and efficient models remains a challenge. Previous works rely on dense and/or sparse factor matrices to estimate unavailable user ratings. In this work we develop a new formulation for recommender systems that is based on projective non-negative matrix factorization, but relaxes the non-negativity constraint. Driven by a simple yet instructive intuition, the proposed formulation delivers promising and stable results that depend on a minimal number of parameters. Experiments that we conducted on two popular recommender system datasets demonstrate the efficiency and promise of our proposed method. We make available our code and datasets at https://github.com/christosbampis/PCMF\_release.

It has recently become apparent that both accidental and maliciously caused randomness failures pose a real and serious threat to the security of cryptographic primitives, and in response, researchers have begone the development of primitives that provide robustness against these. In this paper, however, we focus on standardized, widely available primitives. Specifically, we analyze the RSA-OAEP encryption scheme and RSA-PSS signature schemes, specified in PKCS \#1, using the related randomness security notion introduced by Paterson et al. (PKC 2014) and its extension to signature schemes. We show that, under the RSA and $\Phi$-hiding assumptions, RSA-OAEP encryption is related randomness secure for a large class of related randomness functions in the random oracle model, as long as the recipient is honest, and remains secure even when additionally considering malicious recipients, as long as the related randomness functions does not allow the malicious recipients to efficiently compute the randomness used for the honest recipient. We furthermore show that, under the RSA assumption, the RSA-PSS signature scheme is secure for any class of related randomness functions, although with a non-tight security reduction. However, under additional, albeit somewhat restrictive assumptions on the related randomness functions and the adversary, a tight reduction can be recovered. Our results provides some reassurance regarding the use of RSA-OAEP and RSA-PSS in environments where randomness failures might be a concern. Lastly, we note that, unlike RSA-OAEP and RSA-PSS, several other schemes, including RSA-KEM, part of ISO 18033-2, and DHIES, part of IEEE P1363a, are not secure under simple repeated randomness attacks.

In this paper we present a case study of applying fitness dimensions in API design assessment. We argue that API assessment is company specific and should take into consideration various stakeholders in the API ecosystem. We identified new fitness dimensions and introduced the notion of design considerations for fitness dimensions such as priorities, tradeoffs, and technical versus cognitive classification.

The present study's primary objective is to try to determine whether gender, combined with the educational background of the Internet users, have an effect on the way online privacy is perceived and practiced within the cloud services and specifically in social networking, e-commerce, and online banking. An online questionnaire was distributed through e-mail and the social media (Facebook, LinkedIn, and Google+). Our primary hypothesis is that an interrelationship may exist among a user's gender, educational background, and the way an online user perceives and acts regarding online privacy. An analysis of a representative sample of Greek Internet users revealed that there is an effect by gender on the online users' awareness regarding online privacy, as well as on the way they act upon it. Furthermore, we found that a correlation exists, as well regarding the Educational Background of the users and the issue of online privacy.

Context-dependent applications are relatively complex due to their multiple variations caused by context activation, especially in the presence of unanticipated adaptation. Testing these systems is challenging, as it is hard to reproduce the same execution environments. Therefore, a software failure caused by bugs is no exception. This paper presents a rollback mechanism to recover from software failures as part of a role-based runtime with support for unanticipated adaptation. The mechanism performs checkpoints before each adaptation and employs specialized sensors to detect bugs resulting from recent configuration changes. When the runtime detects a bug, it assumes that the bug belongs to the latest configuration. The runtime rolls back to the recent checkpoint to recover and subsequently notifies the developer to fix the bug and re-applying the adaptation through unanticipated adaptation. We prototype the concept as part of our role-based runtime engine LyRT and demonstrate the applicability of the rollback recovery mechanism for unanticipated adaptation in erroneous situations.

The Domain Name System (DNS) is part of the core of the Internet. Over the past decade, much-needed security features were added to this protocol, with the introduction of the DNS Security Extensions. DNSSEC adds authenticity and integrity to the protocol using digital signatures, and turns the DNS into a public key infrastructure (PKI). At the top of this PKI is a single key, the so-called Key Signing Key (KSK) for the DNS root. The current Root KSK was introduced in 2010, and has not changed since. This year, the Root KSK will be replaced for the first time ever. This event potentially has a major impact on the Internet. Thousands of DNS resolvers worldwide rely on this key to validate DNSSEC signatures, and must start using the new key, either through an automated process, or manual intervention. Failure to pick up the new key will result in resolvers becoming completely unavailable to end users. This work presents the "Root Canary", a system to monitor and measure this event from the perspective of validating DNS resolvers for its entire nine-month duration. The system combines three active measurement platforms to have the broadest possible coverage of validating resolvers. Results will be presented in near real-time, to allow the global DNS community to act if problems arise. Furthermore, after the Root KSK rollover concludes in March 2018, we will use the recorded datasets for an in-depth analysis, from which the Internet community can draw lessons for future key rollovers.

Cryptographic APIs are often vulnerable to attacks that compromise sensitive cryptographic keys. In the literature we find many proposals for preventing or mitigating such attacks but they typically require to modify the API or to configure it in a way that might break existing applications. This makes it hard to adopt such proposals, especially because security APIs are often used in highly sensitive settings, such as financial and critical infrastructures, where systems are rarely modified and legacy applications are very common. In this paper we take a different approach. We propose an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. The method collects logs for various devices and cryptographic services and is able to detect, offline, any leakage of sensitive keys, under the assumption that a key fingerprint is provided for each sensitive key. We define key security formally and we prove that the method is sound, complete and efficient. We also show that without key fingerprinting completeness is lost, i.e., some attacks cannot be detected. We discuss possible practical implementations and we develop a proof-of-concept log analysis tool for PKCS\#11 that is able to detect, on a significant fragment of the API, all key-management attacks from the literature.