Biblio

The vision of smart environments, systems, and services is driven by the development of the Internet of Things (IoT). IoT devices produce large amounts of data and this data is used to make critical decisions in many systems. The data produced by these devices has to satisfy various security related requirements in order to be useful in practical scenarios. One of these requirements is data provenance which allows a user to trust the data regarding its origin and location. The low cost of many IoT devices and the fact that they may be deployed in unprotected spaces requires security protocols to be efficient and secure against physical attacks. This paper proposes a light-weight protocol for data provenance in the IoT. The proposed protocol uses physical unclonable functions (PUFs) to provide physical security and uniquely identify an IoT device. Moreover, wireless channel characteristics are used to uniquely identify a wireless link between an IoT device and a server/user. A brief security and performance analysis are presented to give a preliminary validation of the protocol.

A majority of today's mobile apps integrate web content of various kinds. Unfortunately, the interactions between app code and web content expose new attack vectors: a malicious app can subvert its embedded web content to steal user secrets; on the other hand, malicious web content can use the privileges of its embedding app to exfiltrate sensitive information such as the user's location and contacts. In this paper, we discuss security weaknesses of the interface between app code and web content through attacks, then introduce defenses that can be deployed without modifying the OS. Our defenses feature WIREframe, a service that securely embeds and renders external web content in Android apps, and in turn, prevents attacks between em- bedded web and host apps. WIREframe fully mediates the interface between app code and embedded web content. Un- like the existing web-embedding mechanisms, WIREframe allows both apps and embedded web content to define simple access policies to protect their own resources. These policies recognize fine-grained security principals, such as origins, and control all interactions between apps and the web. We also introduce WIRE (Web Isolation Rewriting Engine), an offline app rewriting tool that allows app users to inject WIREframe protections into existing apps. Our evaluation, based on 7166 popular apps and 20 specially selected apps, shows these techniques work on complex apps and incur acceptable end-to-end performance overhead.

With smart phones being indispensable in people's everyday life, Android malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving Android malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic Android malware detection. In these systems, based on different feature representations, various kinds of classifiers are constructed to detect Android malware. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the security of machine learning in Android malware detection on the basis of a learning-based classifier with the input of a set of features extracted from the Android applications (apps). We consider different importances of the features associated with their contributions to the classification problem as well as their manipulation costs, and present a novel feature selection method (named SecCLS) to make the classifier harder to be evaded. To improve the system security while not compromising the detection accuracy, we further propose an ensemble learning approach (named SecENS) by aggregating the individual classifiers that are constructed using our proposed feature selection method SecCLS. Accordingly, we develop a system called SecureDroid which integrates our proposed methods (i.e., SecCLS and SecENS) to enhance security of machine learning-based Android malware detection. Comprehensive experiments on the real sample collections from Comodo Cloud Security Center are conducted to validate the effectiveness of SecureDroid against adversarial Android malware attacks by comparisons with other alternative defense methods. Our proposed secure-learning paradigm can also be readily applied to other malware detection tasks.

This paper studies the stability of event-triggered control systems subject to Denial-of-Service attacks. An improved method is provided to increase frequency and duration of the DoS attacks where closed-loop stability is not destroyed. A two-mode switching control method is adopted to maintain stability of event-triggered control systems in the presence of attacks. Moreover, this paper reveals the relationship between robustness of systems against DoS attacks and lower bound of the inter-event times, namely, enlarging the inter-execution time contributes to enhancing the robustness of the systems against DoS attacks. Finally, some simulations are presented to illustrate the efficiency and feasibility of the obtained results.

Cloud computing offers many advantages as flexibility or resource efficiency and can significantly reduce costs. However, when sensitive data is outsourced to a cloud provider, classified records can leak. To protect data owners and application providers from a privacy breach data must be encrypted before it is uploaded. In this work, we present a distributed key management scheme that handles user-specific keys in a single-tenant scenario. The underlying database is encrypted and the secret key is split into parts and only reconstructed temporarily in memory. Our scheme distributes shares of the key to the different entities. We address bootstrapping, key recovery, the adversary model and the resulting security guarantees.

Distributed storage systems and caching systems are becoming widespread, and this motivates the increasing interest on assessing their achievable performance in terms of reliability for legitimate users and security against malicious users. While the assessment of reliability takes benefit of the availability of well established metrics and tools, assessing security is more challenging. The classical cryptographic approach aims at estimating the computational effort for an attacker to break the system, and ensuring that it is far above any feasible amount. This has the limitation of depending on attack algorithms and advances in computing power. The information-theoretic approach instead exploits capacity measures to achieve unconditional security against attackers, but often does not provide practical recipes to reach such a condition. We propose a mixed cryptographic/information-theoretic approach with a twofold goal: estimating the levels of information-theoretic security and defining a practical scheme able to achieve them. In order to find optimal choices of the parameters of the proposed scheme, we exploit an effective probabilistic model checker, which allows us to overcome several limitations of more conventional methods.

We analyze the security practices of three smart toys that communicate with children through voice commands. We show the general communication architecture, and some general security and privacy practices by each of the devices. Then we focus on the analysis of one particular toy, and show how attackers can decrypt communications to and from a target device, and perhaps more worryingly, the attackers can also inject audio into the toy so the children listens to any arbitrary audio file the attacker sends to the toy. This last attack raises new safety concerns that manufacturers of smart toys should prevent.

We all are very much aware of IoT that is Internet of Things which is emerging technology in today's world. The new and advanced field of technology and inventions make use of IoT for better facility. The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Our project is based on IoT and other supporting techniques which can bring out required output. Security issues are everywhere now-a-days which we are trying to deal with by our project. Our security throwbot (a throwable device) will be tossed into a room after activating it and it will capture 360 degree panaromic video from a single IP camera, by using two end connectivity that is, robot end and another is user end, will bring more features to this project. Shape of the robot will be shperical so that problem of retrieving back can be solved. Easy to use and cheap to buy is one of our goal which will be helpful to police and soldiers who get stuck in situations where they have to question oneself before entering to dangerous condition/room. Our project will help them to handle and verify any area before entering by just throwing this robot and getting the sufficient results.

SoCs implementing security modules should be both testable and secure. Oversights in a design's test structure could expose internal modules creating security vulnerabilities during test. In this paper, for the first time, we propose a novel automated security vulnerability analysis framework to identify violations of confidentiality, integrity, and availability policies caused by test structures and designer oversights during SoC integration. Results demonstrate existing information leakage vulnerabilities in implementations of various encryption algorithms and secure microprocessors. These can be exploited to obtain secret keys, control finite state machines, or gain unauthorized access to memory read/write functions.

This paper introduces the first state-based formalization of isolation guarantees. Our approach is premised on a simple observation: applications view storage systems as black-boxes that transition through a series of states, a subset of which are observed by applications. Defining isolation guarantees in terms of these states frees definitions from implementation-specific assumptions. It makes immediately clear what anomalies, if any, applications can expect to observe, thus bridging the gap that exists today between how isolation guarantees are defined and how they are perceived. The clarity that results from definitions based on client-observable states brings forth several benefits. First, it allows us to easily compare the guarantees of distinct, but semantically close, isolation guarantees. We find that several well-known guarantees, previously thought to be distinct, are in fact equivalent, and that many previously incomparable flavors of snapshot isolation can be organized in a clean hierarchy. Second, freeing definitions from implementation-specific artefacts can suggest more efficient implementations of the same isolation guarantee. We show how a client-centric implementation of parallel snapshot isolation can be more resilient to slowdown cascades, a common phenomenon in large-scale datacenters.

Patches and related information about software vulnerabilities are often made available to the public, aiming to facilitate timely fixes. Unfortunately, the slow paces of system updates (30 days on average) often present to the attackers enough time to recover hidden bugs for attacking the unpatched systems. Making things worse is the potential to automatically generate exploits on input-validation flaws through reverse-engineering patches, even though such vulnerabilities are relatively rare (e.g., 5% among all Linux kernel vulnerabilities in last few years). Less understood, however, are the implications of other bug-related information (e.g., bug descriptions in CVE), particularly whether utilization of such information can facilitate exploit generation, even on other vulnerability types that have never been automatically attacked. In this paper, we seek to use such information to generate proof-of-concept (PoC) exploits for the vulnerability types never automatically attacked. Unlike an input validation flaw that is often patched by adding missing sanitization checks, fixing other vulnerability types is more complicated, usually involving replacement of the whole chunk of code. Without understanding of the code changed, automatic exploit becomes less likely. To address this challenge, we present SemFuzz, a novel technique leveraging vulnerability-related text (e.g., CVE reports and Linux git logs) to guide automatic generation of PoC exploits. Such an end-to-end approach is made possible by natural-language processing (NLP) based information extraction and a semantics-based fuzzing process guided by such information. Running over 112 Linux kernel flaws reported in the past five years, SemFuzz successfully triggered 18 of them, and further discovered one zero-day and one undisclosed vulnerabilities. These flaws include use-after-free, memory corruption, information leak, etc., indicating that more complicated flaws can also be automatically attacked. This finding calls into question the way vulnerability-related information is shared today.

Classifying users according to their behaviors is a complex problem due to the high-volume of data and the unclear association between distinct data points. Although over the past years behavioral researches has mainly focused on Massive Multiplayer Online Role Playing Games (MMORPG), such as World of Warcraft (WoW), which has predefined player classes, there has been little applied to Open World Sandbox Games (OWSG). Some OWSG do not have player classes or structured linear gameplay mechanics, as freedom is given to the player to freely wander and interact with the virtual world. This research focuses on identifying different play styles that exist within the non-structured gameplay sessions of OWSG. This paper uses the OWSG TUG as a case study and over a period of forty-five days, a database stored selected gameplay events happening on the research server. The study applied k-means clustering to this dataset and evaluated the resulting distinct behavioral profiles to classify player sessions on an open world sandbox game.

This paper describes a unified framework for the simulation and analysis of cyber physical systems (CPSs). The framework relies on the FreeBSD-based IMUNES network simulator. Components of the CPS are modeled as nodes within the IMUNES network simulator; nodes that communicate using real TCP/IP traffic. Furthermore, the simulated system can be exposed to other networks and the Internet to make it look like a real SCADA system. The frame-work has been used to simulate a TRIGA nuclear reactor. This is accomplished by creating nodes within the IMUNES network capable of running system modules simulating different CPS components. Nodes communicate using MODBUS/TCP, a widely used process control protocol. A goal of this work is to eventually integrate the simulator with a honeynet. This allows researchers to not only simulate a digital control system using real TCP/IP traffic to test control strategies and network topologies, but also to explore possible cyber attacks and mitigation strategies.

Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical energy systems with a high(er) penetration of fluctuating renewable generation and controllable loads. As a result of these developments the validation on the system level becomes much more important during the whole engineering and deployment process, today. In earlier development stages and for larger system configurations laboratory-based testing is not always an option. Due to recent developments, simulation-based approaches are now an appropriate tool to support the development, implementation, and roll-out of smart grid solutions. This paper discusses the current state of simulation-based approaches and outlines the necessary future research and development directions in the domain of power and energy systems.

Internet eXchange Points (IXPs) play an ever-growing role in Internet inter-connection. To facilitate the exchange of routes amongst their members, IXPs provide Route Server (RS) services to dispatch the routes according to each member's peering policies. Nowadays, to make use of RSes, these policies must be disclosed to the IXP. This poses fundamental questions regarding the privacy guarantees of route-computation on confidential business information. Indeed, as evidenced by interaction with IXP administrators and a survey of network operators, this state of affairs raises privacy concerns among network administrators and even deters some networks from subscribing to RS services. We design Sixpack1, an RS service that leverages Secure Multi-Party Computation (SMPC) to keep peering policies confidential, while extending, the functionalities of today's RSes. As SMPC is notoriously heavy in terms of communication and computation, our design and implementation of Sixpack aims at moving computation outside of the SMPC without compromising the privacy guarantees. We assess the effectiveness and scalability of our system by evaluating a prototype implementation using traces of data from one of the largest IXPs in the world. Our evaluation results indicate that Sixpack can scale to support privacy-preserving route-computation, even at IXPs with many hundreds of member networks.

Securing cyber-physical systems is hard. They are complex infrastructures comprising multiple technological artefacts, designers, operators and users. Existing research has established the security challenges in such systems as well as the role of usable security to support humans in effective security decisions and actions. In this paper we focus on smart cyber-physical systems, such as those based on the Internet of Things (IoT). Such smart systems aim to intelligently automate a variety of functions, with the goal of hiding that complexity from the user. Furthermore, the interactions of the user with such systems are more often implicit than explicit, for instance, a pedestrian with wearables walking through a smart city environment will most likely interact with the smart environment implicitly through a variety of inferred preferences based on previously provided or automatically collected data. The key question that we explore is that of empowering software engineers to pragmatically take into account how users make informed security choices about their data and information in such a pervasive environment. We discuss a range of existing frameworks considering the impact of automation on user behaviours and argue for the need of a shift–-from usability to security ergonomics as a key requirement when designing and implementing security features in smart cyber-physical environments. Of course, the considerations apply more broadly than security but, in this paper, we focus only on security as a key concern.

Internet of Things (IoT) devices are getting increasingly popular, becoming a core element for the next generations of informational architectures: smart city, smart factory, smart home, smart health-care and many others. IoT systems are mainly comprised of embedded devices with limited computing capabilities while having a cloud component which processes the data and delivers it to the end-users. IoT devices access the user private data, thus requiring robust security solution which must address features like usability and scalability. In this paper we discuss about an IoT authentication service for smart-home devices using a smart-phone as security anchor, QR codes and attribute based cryptography (ABC). Regarding the fact that in an IoT ecosystem some of the IoT devices and the cloud components may be considered untrusted, we propose a privacy preserving attribute based access control protocol to handle the device authentication to the cloud service. For the smart-phone centric authentication to the cloud component, we employ the FIDO UAF protocol and we extend it, by adding an attributed based privacy preserving component.

In a spectrally congested environment or a spectrally contested environment which often occurs in cyber security applications, multiple signals are often mixed together with significant overlap in spectrum. This makes the signal detection and parameter estimation task very challenging. In our previous work, we have demonstrated the feasibility of using a second order spectrum correlation function (SCF) cyclostationary feature to perform mixed signal detection and parameter estimation. In this paper, we present our recent work on software defined radio (SDR) based implementation and demonstration of such mixed signal detection algorithms. Specifically, we have developed a software defined radio based mixed RF signal generator to generate mixed RF signals in real time. A graphical user interface (GUI) has been developed to allow users to conveniently adjust the number of mixed RF signal components, the amplitude, initial time delay, initial phase offset, carrier frequency, symbol rate, modulation type, and pulse shaping filter of each RF signal component. This SDR based mixed RF signal generator is used to transmit desirable mixed RF signals to test the effectiveness of our developed algorithms. Next, we have developed a software defined radio based mixed RF signal detector to perform the mixed RF signal detection. Similarly, a GUI has been developed to allow users to easily adjust the center frequency and bandwidth of band of interest, perform time domain analysis, frequency domain analysis, and cyclostationary domain analysis.