Biblio

Implementing security by design in practice often involves the application of threat modeling to elicit security threats and to aid designers in focusing efforts on the most stringent problems first. Existing threat modeling methodologies are capable of generating lots of threats, yet they lack even basic support to triage these threats, except for relying on the expertise and manual assessment by the threat modeler. Since the essence of creating a secure design is to minimize associated risk (and countermeasure costs), risk analysis approaches offer a very compelling solution to this problem. By combining risk analysis and threat modeling, elicited threats in a design can be enriched with risk analysis information in order to provide support in triaging and prioritizing threats and focusing security efforts on the high-risk threats. It requires the following inputs: the asset values, the strengths of countermeasures, and an attacker model. In his paper, we provide an integrated threat elicitation and risk analysis approach, implemented in a threat modeling tool prototype, and evaluate it using a real-world application, namely the SecureDrop whistleblower submission system. We show that the security measures implemented in SecureDrop indeed correspond to the high-risk threats identified by our approach. Therefore, the risk-based security analysis provides useful guidance on focusing security efforts on the most important problems first.

Traditional risk management produces a rather static listing of weaknesses, probabilities and mitigations. Large share of cyber security risks realize through computer networks. These attacks or attack attempts produce events that are detected by various monitoring techniques such as Intrusion Detection Systems (IDS). Often the link between detecting these potentially dangerous real-time events and risk management process is lacking, or completely missing. This paper presents means for transferring and visualizing the network events in the risk management instantly with a tool called Metrics Visualization System (MVS). The tool is used to dynamically visualize network security events of a Terrestrial Trunked Radio (TETRA) network running in Software Defined Networking (SDN) context as a case study. Visualizations are presented with a treelike graph, that gives a quick easily understandable overview of the cyber security situation. This paper also discusses what network security events are monitored and how they affect the more general risk levels. The major benefit of this approach is that the risk analyst is able to map the designed risk tree/security metrics into actual real-time events and view the system's security posture with the help of a runtime visualization view.

By allowing a large number of users to behave as readers or writers, Multi-User Searchable Encryption (MUSE) raises new security and performance challenges beyond the typical requirements of Symmetric Searchable Encryption (SSE). In this paper we identify two core mandatory requirements of MUSE protocols being privacy in face of users colluding with the CSP and low complexity for the users, pointing that no existing MUSE protocol satisfies these two requirements at the same time. We then come up with the first MUSE protocol that satisfies both of them. The design of the protocol also includes new constructions for a secure variant of Bloom Filters (BFs) and multi-query Oblivious Transfer (OT).

Third-party storage services pose the risk of integrity and confidentiality violations as the current storage policy enforcement mechanisms are spread across many layers in the system stack. To mitigate these security vulnerabilities, we present the design and implementation of Pesos, a Policy Enhanced Secure Object Store (Pesos) for untrusted third-party storage providers. Pesos allows clients to specify per-object security policies, concisely and separately from the storage stack, and enforces these policies by securely mediating the I/O in the persistence layer through a single unified enforcement layer. More broadly, Pesos exposes a rich set of storage policies ensuring the integrity, confidentiality, and access accounting for data storage through a declarative policy language. Pesos enforces these policies on untrusted commodity platforms by leveraging a combination of two trusted computing technologies: Intel SGX for trusted execution environment (TEE) and Kinetic Open Storage for trusted storage. We have implemented Pesos as a fully-functional storage system supporting many useful end-to-end storage features, and a range of effective performance optimizations. We evaluated Pesos using a range of micro-benchmarks, and real-world use cases. Our evaluation shows that Pesos incurs reasonable performance overheads for the enforcement of policies while keeping the trusted computing base (TCB) small.

Many real-world IoT solutions have to be implemented in a federated environment, which are environments where many different administrative organizations are involved in different parts of the solution. Smarter Cities, Federated Governance, International Trade and Military Coalition Operations are examples of federated environments. As end devices become more capable and intelligent, learning from their environment, and adapting on their own, they expose new types of security vulnerabilities and present an increased attack surface. A distributed AI approach can help mitigate many of the security problems that one may encounter in such federated environments. In this paper, we outline some of the scenarios in which we need to rethink security issues as devices become more intelligent, and discuss how distributed AI techniques can be used to reduce the security exposures in such environments.

Mobile ad hoc networks (MANETs) are self-configuring, dynamic networks in which nodes are free to move. These nodes are susceptible to various malicious attacks. In this paper, we propose a distributed trust-based security scheme to prevent multiple attacks such as Probe, Denial-of-Service (DoS), Vampire, User-to-Root (U2R) occurring simultaneously. We report above 95% accuracy in data transmission and reception by applying the proposed scheme. The simulation has been carried out using network simulator ns-2 in a AODV routing protocol environment. To the best of the authors' knowledge, this is the first work reporting a distributed trust-based prevention scheme for preventing multiple attacks. We also check the scalability of the technique using variable node densities in the network.

Vehicular ad-hoc networks (VANETs) are designed to play a key role in the development of future transportation systems. Although cooperative awareness messages provide the required situational awareness for new safety and efficiency applications, they also introduce a new attack vector to compromise privacy. The use of ephemeral credentials called pseudonyms for privacy protection was proposed while ensuring the required security properties. In order to prevent an attacker from linking old to new pseudonyms, mix-zones provide a region in which vehicles can covertly change their signing material. In this poster, we extend the idea of mix-zones to mitigate pseudonym linking attacks with a mechanism inspired by chaff-based privacy defense techniques for mix-networks. By providing chaff trajectories, our system restores the efficacy of mix-zones to compensate for a lack of vehicles available to participate in the mixing procedure. Our simulation results of a realistic traffic scenario show that a significant improvement is possible.

We present the IT solution for remote modeling of cryptographic protocols and other cryptographic primitives and a number of education-oriented capabilities based on them. These capabilities are provided at the Department of Mathematical Modeling using the MPEI algebraic processor, and allow remote participants to create automata models of cryptographic protocols, use and manage them in the modeling process. Particular attention is paid to the IT solution for modeling of the private communication and key distribution using the processor combined with the Kerberos protocol. This allows simulation and studying of key distribution protocols functionality on remote computers via the Internet. The importance of studying cryptographic primitives for future IT specialists is emphasized.

Companies analyse large amounts of data on clusters of machines, using big data analytic tools such as Apache Spark and Apache Flink to analyse the data. Big data analytic tools are mainly tested regarding speed and reliability. Efforts about Security and thus authentication are spent only at second glance. In such big data analytic tools, authentication is achieved with the help of the Kerberos protocol that is basically built as authentication on top of big data analytic tools. However, Kerberos is vulnerable to attacks, and it lacks providing high availability when users are all over the world. To improve the authentication, this work presents first an analysis of the authentication in Hadoop and the data analytic tools. Second, we propose a concept to deploy Transport Layer Security (TLS) not only for the security of data transportation but as well for authentication within the big data tools. This is done by establishing the connections using certificates with a short lifetime. The proof of concept is realized in Apache Spark, where Kerberos is replaced by the method proposed. We deploy new short living certificates for authentication that are less vulnerable to abuse. With our approach the requirements of the industry regarding multi-factor authentication and scalability are met.

Voice interfaces are increasingly becoming integrated into a variety of Internet of Things (IoT) devices. Such systems can dramatically simplify interactions between users and devices with limited displays. Unfortunately voice interfaces also create new opportunities for exploitation. Specifically any sound-emitting device within range of the system implementing the voice interface (e.g., a smart television, an Internet-connected appliance, etc) can potentially cause these systems to perform operations against the desires of their owners (e.g., unlock doors, make unauthorized purchases, etc). We address this problem by developing a technique to recognize fundamental differences in audio created by humans and electronic speakers. We identify sub-bass over-excitation, or the presence of significant low frequency signals that are outside of the range of human voices but inherent to the design of modern speakers, as a strong differentiator between these two sources. After identifying this phenomenon, we demonstrate its use in preventing adversarial requests, replayed audio, and hidden commands with a 100%/1.72% TPR/FPR in quiet environments. In so doing, we demonstrate that commands injected via nearby audio devices can be effectively removed by voice interfaces.

The generative and transformative nature of the Internet which has become a synonym for the infrastructure of the contemporary digital society, is also a place where there are unsavoury and illegal activities such as fraud, human trafficking, exchange of control substances, arms smuggling, extremism, and terrorism. The legitimate concerns such as anonymity and privacy are used for proliferation of nefarious deeds in parts of the Internet termed as a deep web and a dark web. The cryptographic and anonymity mechanisms employed by the dark web miscreants create serious problems for the law enforcement agencies and other legal institutions to monitor, control, investigate, prosecute, and prevent the range of criminal events which should not be part of the Internet, and the human society in general. The paper describes the research on developing a framework for identifying, collecting, analysing, and reporting information from the dark web in a forensically sound manner. The framework should provide the fundamentals for creating a real-life system that could be used as a tool by law enforcement institutions, digital forensics researchers and practitioners to explore and study illicit actions and their consequences on the dark web. The design science paradigms is used to develop the framework, while international security and forensic experts are behind the ex-ante evaluation of the basic components and their functionality, the architecture, and the organization of the system. Finally, we discuss the future work concerning the implementation of the framework along with the inducement of some intelligent modules that should empower the tool with adaptability, effectiveness, and efficiency.

Over the past decade, the reliance on Unmanned Aerial Systems (UAS) to carry out critical missions has grown drastically. With an increased reliance on UAS as mission assets and the dependency of UAS on cyber resources, cyber security of UAS must be improved by adopting sound security principles and relevant technologies from the computing community. On the other hand, the traditional avionics community, being aware of the importance of cyber security, is looking at new architecture and designs that can accommodate both the traditional safety oriented principles as well as the cyber security principles and techniques. It is with the effective and timely convergence of these domains that a holistic approach and co-design can meet the unique requirements of modern systems and operations. In this paper, authors from both the cyber security and avionics domains describe our joint effort and insights obtained during the course of designing secure and resilient embedded avionics systems.

The paper deals with the implementation aspects of the bilinear pairing operation over an elliptic curve on constrained devices, such as smart cards, embedded devices, smart meters and similar devices. Although cryptographic constructions, such as group signatures, anonymous credentials or identity-based encryption schemes, often rely on the pairing operation, the implementation of such schemes into practical applications is not straightforward, in fact, it may become very difficult. In this paper, we show that the implementation is difficult not only due to the high computational complexity, but also due to the lack of cryptographic libraries and programming interfaces. In particular, we show how difficult it is to implement pairing-based schemes on constrained devices and show the performance of various libraries on different platforms. Furthermore, we show the performance estimates of fundamental cryptographic constructions, the group signatures. The purpose of this paper is to reduce the gap between the cryptographic designers and developers and give performance results that can be used for the estimation of the implementability and performance of novel, upcoming schemes.

In this paper, we consider ways of organizing group authentication, as well as the features of constructing the isogeny of elliptic curves. The work includes the study of isogeny graphs and their application in postquantum systems. A hierarchical group authentication scheme has been developed using transformations based on the search for isogeny of elliptic curves.

We survey elliptic curve implementations from several vantage points. We perform internet-wide scans for TLS on a large number of ports, as well as SSH and IPsec to measure elliptic curve support and implementation behaviors, and collect passive measurements of client curve support for TLS. We also perform active measurements to estimate server vulnerability to known attacks against elliptic curve implementations, including support for weak curves, invalid curve attacks, and curve twist attacks. We estimate that 1.53% of HTTPS hosts, 0.04% of SSH hosts, and 4.04% of IKEv2 hosts that support elliptic curves do not perform curve validity checks as specified in elliptic curve standards. We describe how such vulnerabilities could be used to construct an elliptic curve parameter downgrade attack called CurveSwap for TLS, and observe that there do not appear to be combinations of weak behaviors we examined enabling a feasible CurveSwap attack in the wild. We also analyze source code for elliptic curve implementations, and find that a number of libraries fail to perform point validation for JSON Web Encryption, and find a flaw in the Java and NSS multiplication algorithms.

Despite recent regulations and growing user awareness, undesired browser tracking is increasing. In addition to cookies, browser fingerprinting is a stateless technique that exploits a device's configuration for tracking purposes. In particular, browser fingerprinting builds on attributes made available from Javascript and HTTP headers to create a unique and stable fingerprint. For example, browser plugins have been heavily exploited by state-of-the-art browser fingerprinters as a rich source of entropy. However, as browser vendors abandon plugins in favor of extensions, fingerprinters will adapt. We present FP-TESTER, an approach to automatically test the effectiveness of browser fingerprinting countermeasure extensions. We implement a testing toolkit to be used by developers to reduce browser fingerprintability. While countermeasures aim to hinder tracking by changing or blocking attributes, they may easily introduce subtle side-effects that make browsers more identifiable, rendering the extensions counterproductive. FP-TESTER reports on the side-effects introduced by the countermeasure, as well as how they impact tracking duration from a fingerprinter's point-of-view. To the best of our knowledge, FP-TESTER is the first tool to assist developers in fighting browser fingerprinting and reducing the exposure of end-users to such privacy leaks.

Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed. In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking. FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98,598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.

Botnet on a mobile platform is one of the severe problems for the Internet security. It causes damages to both individual users and the economic system. Botnet detection is required to stop these damages. However, botmasters keep developing their botnets. Peer-to-peer (P2P) connection and encryption are used in the botnet communication to avoid the exposure and takedown. To tackle this problem, we propose the P2P mobile botnet detection by using communication patterns. A graph representation called "graphlet" is used to capture the natural communication patterns of a P2P mobile botnet. The graphlet-based detection does not violate the user privacy, and also effective with encrypted traffic. Furthermore, a machine learning technique with graphlet-based features can detect the P2P mobile botnet even it runs simultaneously with other applications such as Facebook, Line, Skype, YouTube, and Web. Moreover, we employ the Principal Components Analysis (PCA) to analyze graphlet's features to leverage the detection performance when the botnet coexists with dense traffic such as Web traffic. Our work focuses on the real traffic of an advanced P2P mobile botnet named "NotCompatible.C". The detection performance shows high F-measure scores of 0.93, even when sampling only 10% of traffic in a 3-minute duration.

Botnets represent a widely deployed framework for remotely infecting and controlling hundreds of networked computing devices for malicious ends. Traditionally detection of Botnets from network data using machine learning approaches is framed as an offline, supervised learning activity. However, in practice both normal behaviours and Botnet behaviours represent non-stationary processes in which there are continuous developments to both as new services/applications and malicious behaviours appear. This work formulates the task of Botnet detection as a streaming data task in which finite label budgets, class imbalance and incremental/online learning predominate. We demonstrate that effective Botnet detection is possible for label budgets as low as 0.5% when an active learning approach is adopted for genetic programming (GP) streaming data analysis. The full article appears as S. Khanchi et al., (2018) "On Botnet Detection with Genetic Programming under Streaming Data, Label Budgets and Class Imbalance" in Swarm and Evolutionary Computation, 39:139--140. https://doi.org/10.1016/j.swevo.2017.09.008

An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks that may cause significant economic and social harm while being hidden to the user and device. Thus, it becomes very difficult to detect such activity. A considerable amount of research has been conducted to detect and prevent botnet infestation. In this paper, we attempt to create a foundation for an anomaly-based intrusion detection system using a statistical learning method to improve network security and reduce human involvement in botnet detection. We focus on identifying the best features to detect botnet activity within network traffic using a lightweight logistic regression model. The network traffic is processed by Bro, a popular network monitoring framework which provides aggregate statistics about the packets exchanged between a source and destination over a certain time interval. These statistics serve as features to a logistic regression model responsible for classifying malicious and benign traffic. Our model is easy to implement and simple to interpret. We characterized and modeled 8 different botnet families separately and as a mixed dataset. Finally, we measured the performance of our model on multiple parameters using F1 score, accuracy and Area Under Curve (AUC).

This paper presents work towards realizing architectural support for Bluetooth Trusted I/O on SGX-enabled platforms, with the goal of providing I/O data protection that does not rely on system software security. Indeed, we are primarily concerned with protecting I/O from all software adversaries, including privileged software. In this paper we describe the challenges in designing and implementing Trusted I/O at the architectural level for Bluetooth. We propose solutions to these challenges. In addition, we describe our proof-of-concept work that extends existing over-the-air Bluetooth security all the way to an SGX enclave by securing user data between the Bluetooth Controller and an SGX enclave.

As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.

Centralized systems in the Internet of Things—be it local middleware or cloud-based services—fail to fundamentally address privacy of the collected data. We propose an architecture featuring secure multiparty computation at its core in order to realize data processing systems which already incorporate support for privacy protection in the architecture.

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis to generate new parity features and rank them based on their highest Fourier coefficients. We demonstrate on real enterprise data sets that the engineered features enhance the performance of a wide range of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall, while sacrificing no more than 0.47% in accuracy. We also observe better isolation of malicious clusters, when performing clustering on engineered features. In general, a small number of engineered features achieve higher performance than raw data features according to our metrics of interest. Our feature engineering method also retains interpretability, an important consideration in cyber security applications.

Implantable medical devices (IMDs) typically rely on proprietary protocols to wirelessly communicate with external device programmers. In this paper, we fully reverse engineer the proprietary protocol between a device programmer and a widely used commercial neurostimulator from one of the leading IMD manufacturers. For the reverse engineering, we follow a black-box approach and use inexpensive hardware equipment. We document the message format and the protocol state-machine, and show that the transmissions sent over the air are neither encrypted nor authenticated. Furthermore, we conduct several software radio-based attacks that could compromise the safety and privacy of patients, and investigate the feasibility of performing these attacks in real scenarios. Motivated by our findings, we propose a security architecture that allows for secure data exchange between the device programmer and the neurostimulator. It relies on using a patient»s physiological signal for generating a symmetric key in the neurostimulator, and transporting this key from the neurostimulator to the device programmer through a secret out-of-band (OOB) channel. Our solution allows the device programmer and the neurostimulator to agree on a symmetric session key without these devices needing to share any prior secrets; offers an effective and practical balance between security and permissive access in emergencies; requires only minor hardware changes in the devices; adds minimal computation and communication overhead; and provides forward and backward security. Finally, we implement a proof-of-concept of our solution.