Biblio

In this paper, we introduce the concept of transforming attribute-value assignments from one set to another set. We specify two types of transformations–-attribute reduction and attribute expansion. We distinguish policy attributes from non-policy attributes in that policy attributes are used in authorization policies whereas the latter are not. Attribute reduction is a process of contracting a large set of assignments of non-policy attributes into a possibly smaller set of policy attribute-value assignments. This process is useful for abstracting attributes that are too specific for particular types of objects or users, designing modular authorization policies, and modeling hierarchical policies. On the other hand, attribute expansion is a process of performing a large set of attribute-value assignments to users or objects from a possibly smaller set of assignments. We define a language for specifying mapping for the transformation process. We also identify and discuss various issues that stem from the transformation process.

Wireless sensor network operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Node misbehavior due to selfish or malicious reasons or faulty nodes can significantly degrade the performance of mobile ad-hoc networks. To cope with misbehavior in such self-organized networks, nodes need to be able to automatically adapt their strategy to changing levels of cooperation. The problem of identifying and isolating misbehaving nodes that refuses to forward packets in multi-hop ad hoc networks. a comprehensive system called Audit-based Misbehavior Detection (AMD) that effectively and efficiently isolates both continuous and selective packet droppers. The AMD system integrates reputation management, trustworthy route discovery, and identification of misbehaving nodes based on behavioral audits. AMD evaluates node behavior on a per-packet basis, without employing energy-expensive overhearing techniques or intensive acknowledgment schemes. AMD can detect selective dropping attacks even if end-to-end traffic is encrypted and can be applied to multi-channel networks.

Maintaining the security and integrity of our food supply chain has emerged as a critical need. In this paper, we describe a novel authentication approach that can significantly improve the security of the food supply chain. It relies on applying nuclear quadrupole resonance (NQR) spectroscopy to authenticate the contents of packaged food products. NQR is a non-invasive, non-destructive, and quantitative radio frequency (RF) spectroscopic technique. It is sensitive to subtle features of the solid-state chemical environment such that signal properties are influenced by the manufacturing process, thus generating a manufacturer-specific watermark or intrinsic tag for the product. Such tags enable us to uniquely characterize and authenticate products of identical composition but from different manufacturers based on their NQR signal parameters. These intrinsic tags can be used to verify the integrity of a product and trace it through the supply chain. We apply a support vector machine (SVM)-based classification approach that trains the SVM with measured NQR parameters and then authenticates food products by checking their test responses. Measurement on an example substance using semi-custom hardware shows promising results (95% classification accuracy) which can be further improved with improved instrumentation.

Constraints such as separation-of-duty are widely used to specify requirements that supplement basic authorization policies. However, the existence of constraints (and authorization policies) may mean that a user is unable to fulfill her/his organizational duties because access to resources is denied. In short, there is a tension between the need to protect resources (using policies and constraints) and the availability of resources. Recent work on workflow satisfiability and resiliency in access control asks whether this tension compromises the ability of an organization to achieve its objectives. In this paper, we develop a new method of specifying constraints which subsumes much related work and allows a wider range of constraints to be specified. The use of such constraints leads naturally to a range of questions related to "policy existence", where a positive answer means that an organization's objectives can be realized. We provide an overview of our results establishing that some policy existence questions, notably for those instances that are restricted to user-independent constraints, are fixed-parameter tractable.

Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan [1]. Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation. We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm's feature extraction and provide analysis of the method's robustness to variations of input data—changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) antivirus (AV) products fail.

Many popular web applications incorporate end-toend secure messaging protocols, which seek to ensure that messages sent between users are kept confidential and authenticated, even if the web application's servers are broken into or otherwise compelled into releasing all their data. Protocols that promise such strong security guarantees should be held up to rigorous analysis, since protocol flaws and implementations bugs can easily lead to real-world attacks. We propose a novel methodology that allows protocol designers, implementers, and security analysts to collaboratively verify a protocol using automated tools. The protocol is implemented in ProScript, a new domain-specific language that is designed for writing cryptographic protocol code that can both be executed within JavaScript programs and automatically translated to a readable model in the applied pi calculus. This model can then be analyzed symbolically using ProVerif to find attacks in a variety of threat models. The model can also be used as the basis of a computational proof using CryptoVerif, which reduces the security of the protocol to standard cryptographic assumptions. If ProVerif finds an attack, or if the CryptoVerif proof reveals a weakness, the protocol designer modifies the ProScript protocol code and regenerates the model to enable a new analysis. We demonstrate our methodology by implementing and analyzing a variant of the popular Signal Protocol with only minor differences. We use ProVerif and CryptoVerif to find new and previously-known weaknesses in the protocol and suggest practical countermeasures. Our ProScript protocol code is incorporated within the current release of Cryptocat, a desktop secure messenger application written in JavaScript. Our results indicate that, with disciplined programming and some verification expertise, the systematic analysis of complex cryptographic web applications is now becoming practical.

Software-defined networks provide new facilities for deploying security mechanisms dynamically. In particular, it is possible to build and adjust security chains to protect the infrastructures, by combining different security functions, such as firewalls, intrusion detection systems and services for preventing data leakage. It is important to ensure that these security chains, in view of their complexity and dynamics, are consistent and do not include security violations. We propose in this paper an automated strategy for supporting the verification of security chains in software-defined networks. It relies on an architecture integrating formal verification methods for checking both the control and data planes of these chains, before their deployment. We describe algorithms for translating specifications of security chains into formal models that can then be verified by SMT1 solving or model checking. Our solution is prototyped as a package, named Synaptic, built as an extension of the Frenetic family of SDN programming languages. The performances of our approach are evaluated through extensive experimentations based on the CVC4, veriT, and nuXmv checkers.

Empirical research in the Internet is fraught with challenges. Among these is the possibility that local environmental conditions (e.g., CPU load or network load) introduce unexpected bias or artifacts in measurements that lead to erroneous conclusions. In this paper, we describe a framework for local environment monitoring that is designed to be used during Internet measurement experiments. The goals of our work are to provide a critical, expanded perspective on measurement results and to improve the opportunity for reproducibility of results. We instantiate our framework in a tool we call SoMeta, which monitors the local environment during active probe-based measurement experiments. We evaluate the runtime costs of SoMeta and conduct a series of experiments in which we intentionally perturb different aspects of the local environment during active probe-based measurements. Our experiments show how simple local monitoring can readily expose conditions that bias active probe-based measurement results. We conclude with a discussion of how our framework can be expanded to provide metadata for a broad range of Internet measurement experiments.

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).

We present AVAMAT: AntiVirus and Malware Analysis Tool - a tool for analysing the malware detection capabilities of AntiVirus (AV) products running on different operating system (OS) platforms. Even though similar tools are available, such as VirusTotal and MetaDefender, they have several limitations, which motivated the creation of our own tool. With AVAMAT we are able to analyse not only whether an AV detects a malware, but also at what stage of inspection does it detect it and on what OS. AVAMAT enables experimental campaigns to answer various research questions, ranging from the detection capabilities of AVs on OSs, to optimal ways in which AVs could be combined to improve malware detection capabilities.

In the recent years, we have observed the development of several connected and mobile devices intended for daily use. This development has come with many risks that might not be perceived by the users. These threats are compromising when an unauthorized entity has access to private big data generated through the user objects in the Internet of Things. In the literature, many solutions have been proposed in order to protect the big data, but the security remains a challenging issue. This work is carried out with the aim to provide a solution to the access control to the big data and securing the localization of their generator objects. The proposed models are based on Attribute Based Encryption, CHORD protocol and $μ$TESLA. Through simulations, we compare our solutions to concurrent protocols and we show its efficiency in terms of relevant criteria.

Trusted Execution Environment (TEE) is designed to deliver a safe execution environment for software systems. Intel Software Guard Extensions (SGX) provides isolated memory regions (i.e., SGX enclaves) to protect code and data from adversaries in the untrusted world. While existing research has proposed techniques to execute entire executable files inside enclave instances by providing rich sets of OS facilities, one notable limitation of these techniques is the unavoidably large size of Trusted Computing Base (TCB), which can potentially break the principle of least privilege. In this work, we describe techniques that provide practical and efficient protection of security sensitive code components in legacy binary code. Our technique dissects input binaries into multiple components which are further built into SGX enclave instances. We also leverage deliberately-designed binary editing techniques to retrofit the input binary code and preserve the original program semantics. Our tentative evaluations on hardening AES encryption and decryption procedures demonstrate the practicability and efficiency of the proposed technique.

In this paper, we quantify the effectiveness of third-party tracker blockers on a large scale. First, we analyze the architecture of various state-of-the-art blocking solutions and discuss the advantages and disadvantages of each method. Second, we perform a two-part measurement study on the effectiveness of popular tracker-blocking tools. Our analysis quantifies the protection offered against trackers present on more than 100,000 popular websites and 10,000 popular Android applications. We provide novel insights into the ongoing arms race between trackers and developers of blocking tools as well as which tools achieve the best results under what circumstances. Among others, we discover that rule-based browser extensions outperform learning-based ones, trackers with smaller footprints are more successful at avoiding being blocked, and CDNs pose a major threat towards the future of tracker-blocking tools. Overall, the contributions of this paper advance the field of web privacy by providing not only the largest study to date on the effectiveness of tracker-blocking tools, but also by highlighting the most pressing challenges and privacy issues of third-party tracking.
 

HB+ is a lightweight authentication scheme, which is secure against passive attacks if the Learning Parity with Noise Problem (LPN) is hard. However, HB+ is vulnerable to a key-recovery, man-in-the-middle (MiM) attack dubbed GRS. The HB+DB protocol added a distance-bounding dimension to HB+, and was experimentally proven to resist the GRS attack. We exhibit several security flaws in HB+DB. First, we refine the GRS strategy to induce a different key-recovery MiM attack, not deterred by HB+DB's distancebounding. Second, we prove HB+DB impractical as a secure distance-bounding (DB) protocol, as its DB security-levels scale poorly compared to other DB protocols. Third, we refute that HB+DB's security against passive attackers relies on the hardness of LPN; moreover, (erroneously) requiring such hardness lowers HB+DB's efficiency and security. We also propose anew distance-bounding protocol called BLOG. It retains parts of HB+DB, yet BLOG is provably secure and enjoys better (asymptotical) security.

Cyber security management of systems in the cyberspace has been a challenging problem for both practitioners and the research community. Their proprietary nature along with the complexity renders traditional approaches rather insufficient and creating the need for the adoption of a holistic point of view. This paper draws upon the principles theory game in order to present a novel systemic approach towards cyber security management, taking into account the complex inter-dependencies and providing cost-efficient defense solutions.

Cryptography and encryption is a topic that is blurred by its complexity making it difficult for the majority of the public to easily grasp. The focus of our research is based on SSL technology involving CAs, a centralized system that manages and issues certificates to web servers and computers for validation of identity. We first explain how the certificate provides a secure connection creating a trust between two parties looking to communicate with one another over the internet. Then the paper goes into what happens when trust is compromised and how information that is being transmitted could possibly go into the hands of the wrong person. We are proposing a browser plugin, Certificate Authority Rescue Engine (CAre), to serve as an added source of security with simplicity and visibility. In order to see why CAre will be an added benefit to average and technical users of the internet, one must understand what website security entails. Therefore, this paper will dive deep into website security through the use of public key infrastructure and its core components; certificates, certificate authorities, and their relationship with web browsers.

The modern world is becoming increasingly dependent on computing and communication technology to function, but unfortunately its application and impact on areas such as critical infrastructure and industrial control system (ICS) networks remains to be thoroughly studied. Significant research has been conducted to address the myriad security concerns in these areas, but they are virtually all based on artificial testbeds or simulations designed on assumptions about their behavior either from knowledge of traditional IT networking or from basic principles of ICS operation. In this work, we provide the most detailed characterization of an example ICS to date in order to determine if these common assumptions hold true. A live power distribution substation is observed over the course of two and a half years to measure its behavior and evolution over time. Then, a horizontal study is conducted that compared this behavior with three other substations from the same company. Although most predictions were found to be correct, some unexpected behavior was observed that highlights the fundamental differences between ICS and IT networks including round trip times dominated by processing speed as opposed to network delay, several well known TCP features being largely irrelevant, and surprisingly large jitter from devices running real-time operating systems. The impact of these observations is discussed in terms of generality to other embedded networks, network security applications, and the suitability of the TCP protocol for this environment.

Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.