Biblio

For many wiretap channel models asymptotically optimal coding schemes are known, but less effort has been put into actual realizations of wiretap codes for practical parameters. Bounds on the mutual information and error probability when using coset coding on a Rayleigh fading channel were recently established by Oggier and Belfiore, and the results in this paper build on their work. However, instead of using their ultimate inverse norm sum approximation, a more precise expression for the eavesdropper's probability of correct decision is used in order to determine a general class of good coset codes. The code constructions are based on well-rounded lattices arising from simple geometric criteria. In addition to new coset codes and simulation results, novel number-theoretic results on well-rounded ideal lattices are presented.
 

For many wiretap channel models asymptotically optimal coding schemes are known, but less effort has been put into actual realizations of wiretap codes for practical parameters. Bounds on the mutual information and error probability when using coset coding on a Rayleigh fading channel were recently established by Oggier and Belfiore, and the results in this paper build on their work. However, instead of using their ultimate inverse norm sum approximation, a more precise expression for the eavesdropper's probability of correct decision is used in order to determine a general class of good coset codes. The code constructions are based on well-rounded lattices arising from simple geometric criteria. In addition to new coset codes and simulation results, novel number-theoretic results on well-rounded ideal lattices are presented.

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

Two mainstream techniques are traditionally used to authorize access to a WiFi network. Small scale networks usually rely on the offline distribution of a WPA/WPA2 static pre-shared secret key (PSK); security hence relies on the fact that this PSK is not leaked by end user, and is not disclosed via dictionary or brute-force attacks. On the other side, Enterprise and large scale networks typically employ online authorization using an 802.1X-based authentication service leveraging a backend online infrastructure (e.g. Radius servers/proxies). In this work, we propose a new mechanism which does not require neither online operation nor backend access control infrastructure, but which does not force us to rely on a static pre-shared secret key. The idea is very simple, yet effective: directly broadcast in the WLAN beacons an encrypted version of the secret key required to access the WLAN network, so that only the users which possess suitable authorization credentials can decrypt and use it. This proposed approach clearly decouples the management of authorization credentials, issued offline to the authorized end users, from the actual secret key used in the WLAN network, which can thus be in principle changed at each new user's access. The solution described in the paper relies on attribute-based encryption, and is designed to be compatible with WPA2 and deployable within standard 802.11 management frames. Since no user identification is required (access control is based on attributes rather than on the user identity), the proposed approach further improves privacy. We demonstrate the feasibility of the proposed solution via a concrete implementation in Linux-based devices and via relevant testing in a real-world experimental setup.

Modern vehicles rely on a variety of electronic systems and components. One of those components is the vehicle key. Today, a key typically implements at least three functions: mechanical locking with a key blade, the electronic immobilizer to autorise the start of the engine, and the remote keyless entry (RKE) system that allows to wirelessly (un)lock the doors and disable the alarm system. These main components of a vehicle key are shown in Figure 1. For the mechanical part of the vehicle key, it is well known that the key blade can be easily copied and that the locking cylinder can be bypassed with other means (using so-called "decoders" or simply a screwdriver). In contrast, immobilizer and RKE rely on wireless protocols to cryptographically authenticate the vehicle key to the car. Immobilizers employ radio frequency identification (RFID) transponders to carry out a challenge-response protocol over a low-range bidirectional link at a frequency of 125 kHz. In the past, researchers have revealed severe aws in the cryptography and protocols used by immobilizers, leading to the break of the major systems Megamos, Hitag2, and DST40 [7, 6, 1]. In contrast to the immobilizer, the RKE part uses unidirectional communication (the vehicle only receives, the key fob only transmits) over a high-range wireless link with operating distances of tens to one hundred meters. These systems are based on rolling codes, which essentially transmit a counter (that is incremented on each button press) in a cryptographically authenticated manner. Until recently, the security of automotive RKE had been scrutinized to a lesser degree than that of immobilizers, even though vulnerabilities in similar systems have been known since 2008 with the attacks on KeeLoq [3]. Other results reported in the literature include an analytical attack on a single, outdated vehicle [2] and the so-called "RollJam" technique [5], which is based on a combination of replay and selective jamming. In 2016, it was shown that severe aws exist in the RKE systems of major automotive manufacturers [4]. On the one hand, the VWgroup (Volkswagen, Seat, Skoda, Audi) based the security of their RKE system on a few global cryptographic keys, potentially affecting hundreds of million vehicles world-wide. By extracting these global keys from the firmware of electronic controls units (ECUs) once, an adversary is able to create a duplicate of the owner's RKE fob by eavesdropping a single rolling code. The second case study in [4] exposes new cryptographic weaknesses in the Hitag2 cipher when used for RKE. Applying a correlation-based attack, an adversary can recover the 48-bit cryptographic key by eavesdropping four to eight rolling codes and performing a one-minute computation on a standard laptop. Again, this attack affects millions of vehicle world-wide. Manufacturers that used Hitag2 in their RKE system include Alfa Romeo, Peugeot, Lancia, Opel, Renault, and Ford among others. In this keynote talk, we will present the results of [4] and put them in into a broader context by revisiting the history of attacks on RKE systems and automotive electronics.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

In this demonstration, we showcase the XD middleware, a framework for expressive multiplexing of application communication streams onto underlying device-to-device communication links. XD allows applications to remain agnostic about which low-level networking stack is actually delivering messages and instead focus on the application-level content and delivery parameters. The IoT space has been flooded with new communication technologies (e.g., BLE, ZigBee, 6LoWPAN) to add to those already available on modern mobile devices (e.g., BLE, WiFi-Direct), substantially increasing the barrier to entry for developing innovative IoT applications. XD presents application developers with a simple publish-subscribe API for sending and receiving data streams, unburdening them from the task of selecting and coordinating communication channels. Our demonstration shows two Android applications, Disseminate and Prophet, running using our XD middleware for communication. We implemented BLE, WiFi Direct with TCP, and WiFi Direct with UDP communication stacks underneath XD.

Virtualization technology has become ubiquitous in the computing world. With it, a number of security concerns have been amplified as users run adjacently on a single host. In order to prevent attacks from both internal and external sources, the networking of such systems must be secured. Network intrusion detection systems (NIDSs) are an important tool for aiding this effort. These systems work by analyzing flow or packet information to determine malicious intent. However, it is difficult to implement a NIDS on a virtualized system due to their complexity. This is especially true for the Xen hypervisor: Xen has incredible heterogeneity when it comes to implementation, making a generic solution difficult. In this paper, we analyze the network data flow of a typical Xen implementation along with identifying features common to any implementation. We then explore the benefits of placing security checks along the data flow and promote a solution within the hypervisor itself.

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (\textbackslashemph\e.g.\, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \textbackslashemph\zero-shot hashing\ (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.

This paper illuminates the problem of non-secure DNS dynamic updates, which allow a miscreant to manipulate DNS entries in the zone files of authoritative name servers. We refer to this type of attack as to zone poisoning. This paper presents the first measurement study of the vulnerability. We analyze a random sample of 2.9 million domains and the Alexa top 1 million domains and find that at least 1,877 (0.065%) and 587 (0.062%) of domains are vulnerable, respectively. Among the vulnerable domains are governments, health care providers and banks, demonstrating that the threat impacts important services. Via this study and subsequent notifications to affected parties, we aim to improve the security of the DNS ecosystem.

Our position is that a key component of securing cyber-physical systems (CPS) is to develop a theory of accountability that encompasses both control and computing systems. We envision that a unified theory of accountability in CPS can be built on a foundation of causal information flow analysis. This theory will support design and analysis of mechanisms at various stages of the accountability regime: attack detection, responsibility-assignment (e.g., attack identification or localization), and corrective measures (e.g., via resilient control) As an initial step in this direction, we summarize our results on attack detection in control systems. We use the Kullback-Liebler (KL) divergence as a causal information flow measure. We then recover, using information flow analyses, a set of existing results in the literature that were previously proved using different techniques. These results cover passive detection, stealthy attack characterization, and active detection. This research direction is related to recent work on accountability in computational systems [1], [2], [3], [4]. We envision that by casting accountability theories in computing and control systems in terms of causal information flow, we can provide a common foundation to develop a theory for CPS that compose elements from both domains.

We are witnessing a huge growth of cyber-physical systems, which are autonomous, mobile, endowed with sensing, controlled by software, and often wirelessly connected and Internet-enabled. They include factory automation systems, robotic assistants, self-driving cars, and wearable and implantable devices. Since they are increasingly often used in safety- or business-critical contexts, to mention invasive treatment or biometric authentication, there is an urgent need for modelling and verification technologies to support the design process, and hence improve the reliability and reduce production costs. This paper gives an overview of quantitative verification and synthesis techniques developed for cyber-physical systems, summarising recent achievements and future challenges in this important field.

Cross Site Scripting attack (XSS) is the computer security threat which allows the attacker to get access over the sensitive information, when the javaScript, VBScript, ActiveX, Flash or HTML which is embedded in the malicious XSS link gets executed. In this paper, we authors have discussed about various impacts of XSS, types of XSS, checked whether the site is vulnerable towards the XSS or not, discussed about various tools for examining the XSS vulnerability and summarizes the preventive measures against XSS.

In this paper, we investigate detectability and identifiability of attacks on linear dynamical systems that are subjected to external disturbances. We generalize a concept for a security index, which was previously introduced for static systems. The index exactly quantifies the resources necessary for targeted attacks to be undetectable and unidentifiable in the presence of disturbances. This information is useful for both risk assessment and for the design of anomaly detectors. Finally, we show how techniques from the fault detection literature can be used to decouple disturbances and to identify attacks, under certain sparsity constraints.

Cloud computing is becoming the main computing model in the future due to its advantages such as high resource utilization rate and save high cost of performance. The public environments is become necessary to secure their storage and transmission against possible attacks such as known-plain-text attack and semantic security. How to ensure the data security and the privacy preserving, however, becomes a huge obstacle to its development. The traditional way to solve Secure Multiparty Computation (SMC) problem is using Trusted Third Party (TTP), however, TTPs are particularly hard to achieve and compute complexity. To protect user's privacy data, the encrypted outsourcing data are generally stored and processed in cloud computing by applying homomorphic encryption. According to above situation, we propose Elliptic Curve Cryptography (ECC) based homomorphic encryption scheme for SMC problem that is dramatically reduced computation and communication cost. It shows that the scheme has advantages in energy consumption, communication consumption and privacy protection through the comparison experiment between ECC based homomorphic encryption and RSA&Paillier encryption algorithm. Further evidence, the scheme of homomorphic encryption scheme based on ECC is applied to the calculation of GPS data of the earthquake and prove it is proved that the scheme is feasible, excellent encryption effect and high security.

The theory of robust control models the controller-disturbance interaction as a game where disturbance is nonstrategic. The proviso of a deliberately malicious (strategic) attacker should be considered to increase the robustness of infrastructure systems. This has become especially important since many IT systems supporting critical functionalities are vulnerable to exploits by attackers. While the usefulness of game theory methods for modeling cyber-security is well established in the literature, new game theoretic models of cyber-physical security are needed for deriving useful insights on "optimal" attack plans and defender responses, both in terms of allocation of resources and operational strategies of these players. This whitepaper presents some progress and challenges in using game-theoretic models for security of infrastructure networks. Main insights from the following models are presented: (i) Network security game on flow networks under strategic edge disruptions; (ii) Interdiction problem on distribution networks under node disruptions; (iii) Inspection game to monitor commercial non-technical losses (e.g. energy diversion); and (iv) Interdependent security game of networked control systems under communication failures. These models can be used to analyze the attacker-defender interactions in a class of cyber-physical security scenarios.

In this paper a novel set-theoretic control framework for Cyber-Physical Systems is presented. By resorting to set-theoretic ideas, an anomaly detector module and a control remediation strategy are formally derived with the aim to contrast cyber False Data Injection (FDI) attacks affecting the communication channels. The resulting scheme ensures Uniformly Ultimate Boundedness and constraints fulfillment regardless of any admissible attack scenario.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

This work presents a new class of Covert Hardware Trojan Horses (Covert HTHs), which can be algorithmically implanted with no change to their host circuit's functional behavior and without the need for additional unrelated logic. As a result, Covert HTHs are invulnerable to functional detection methods. This work also proposes a formal methodology for implantation of Covert HTHs, which allows covert hardware to be embedded in any sufficiently-sized synchronous circuit. Synthesis results indicate that covert implantation results in nearly a 75% reduction in integrated circuit area used by the HTH. Furthermore, the covert implantation causes no increase in the host circuit's delay and, compared to the effect of an overtly implanted HTH on its host, the covert implantation results in a significantly lower dynamic and leakage power. These significant reductions in area, delay and power make a covertly implanted HTH highly resistant to existing non-functional detection methods.

In this work, Automatic-Repeat-Request (ARQ) and Maximal Ratio Combination (MRC), have been jointly exploited to enhance the confidentiality of wireless services requested by a legitimate user (Bob) against an eavesdropper (Eve). The obtained security performance is analyzed using Packet Error Rate (PER), where the exact PER gap between Bob and Eve is determined. PER is proposed as a new practical security metric in cross layers (Physical/MAC) security design since it reflects the influence of upper layers mechanisms, and it can be linked with Quality of Service (QoS) requirements for various digital services such as voice and video. Exact PER formulas for both Eve and Bob in i.i.d Rayleigh fading channel are derived. The simulation and theoretical results show that the employment of ARQ mechanism and MRC on a signal level basis before demodulation can significantly enhance data security for certain services at specific SNRs. However, to increase and ensure the security of a specific service at any SNR, adaptive modulation is proposed to be used along with the aforementioned scheme. Analytical and simulation studies demonstrate orders of magnitude difference in PER performance between eavesdroppers and intended receivers.

Humans can easily find themselves in high cost situations where they must choose between suggestions made by an automated decision aid and a conflicting human decision aid. Previous research indicates that humans often rely on automation or other humans, but not both simultaneously. Expanding on previous work conducted by Lyons and Stokes (2012), the current experiment measures how trust in automated or human decision aids differs along with perceived risk and workload. The simulated task required 126 participants to choose the safest route for a military convoy; they were presented with conflicting information from an automated tool and a human. Results demonstrated that as workload increased, trust in automation decreased. As the perceived risk increased, trust in the human decision aid increased. Individual differences in dispositional trust correlated with an increased trust in both decision aids. These findings can be used to inform training programs for operators who may receive information from human and automated sources. Examples of this context include: air traffic control, aviation, and signals intelligence.

Several applications adopt electromagnetic sensors, that base their principle on the presence of magnets realized with specific magnetic materials that show a rather high remanence, but low coercivity. This work concerns the production, analysis and characterization of hybrid composite materials, with the use of metal powders, which aim to reach those specific properties. In order to obtain the best coercivity and remanence characteristics various "recipes" have been used with different percentages of soft and hard magnetic materials, bonded together by a plastic binder. The goal was to find out the interdependence between the magnetic powder composition and the characteristics of the final material. Soft magnetic material (special Fe powder) has been used to obtain a low coercivity value, while hard materials were primarily used for maintaining a good induction remanence; by increasing the soft proportion a higher magnetic permeability has been also obtained. All the selected materials have been characterized and then tested; in order to verify the validity of the proposed materials two practical tests have been performed. Special magnets have been realized for a comparison with original ones (AlNiCo and ferrite) for two experimental cases: the first is consisting in an encoder realized through a toothed wheel, the second regards the special system used for the electric guitars.

Object Injection Vulnerability (OIV) is an emerging threat for web applications. It involves accepting external inputs during deserialization operation and use the inputs for sensitive operations such as file access, modification, and deletion. The challenge is the automation of the detection process. When the application size is large, it becomes hard to perform traditional approaches such as data flow analysis. Recent approaches fall short of narrowing down the list of source files to aid developers in discovering OIV and the flexibility to check for the presence of OIV through various known APIs. In this work, we address these limitations by exploring a concept borrowed from the information retrieval domain called Latent Semantic Indexing (LSI) to discover OIV. The approach analyzes application source code and builds an initial term document matrix which is then transformed systematically using singular value decomposition to reduce the search space. The approach identifies a small set of documents (source files) that are likely responsible for OIVs. We apply the LSI concept to three open source PHP applications that have been reported to contain OIVs. Our initial evaluation results suggest that the proposed LSI-based approach can identify OIVs and identify new vulnerabilities.

Attacks of Ransomware are increasing, this form of malware bypasses many technical solutions by leveraging social engineering methods. This means established methods of perimeter defence need to be supplemented with additional systems. Honeypots are bogus computer resources deployed by network administrators to act as decoy computers and detect any illicit access. This study investigated whether a honeypot folder could be created and monitored for changes. The investigations determined a suitable method to detect changes to this area. This research investigated methods to implement a honeypot to detect ransomware activity, and selected two options, the File Screening service of the Microsoft File Server Resource Manager feature and EventSentry to manipulate the Windows Security logs. The research developed a staged response to attacks to the system along with thresholds when there were triggered. The research ascertained that witness tripwire files offer limited value as there is no way to influence the malware to access the area containing the monitored files.

Notions like security, trust, and privacy are crucial in the digital environment and in the future, with the advent of technologies like the Internet of Things (IoT) and Cyber-Physical Systems (CPS), their importance is only going to increase. Trust has different definitions, some situations rely on real-world relationships between entities while others depend on robust technologies to gain trust after deployment. In this paper we focus on these robust technologies, their evolution in past decades and their scope in the near future. The evolution of robust trust technologies has involved diverse approaches, as a consequence trust is defined, understood and ascertained differently across heterogeneous domains and technologies. In this paper we look at digital trust technologies from the point of view of security and examine how they are making secure computing an attainable reality. The paper also revisits and analyses the Trusted Platform Module (TPM), Secure Elements (SE), Hypervisors and Virtualisation, Intel TXT, Trusted Execution Environments (TEE) like GlobalPlatform TEE, Intel SGX, along with Host Card Emulation, and Encrypted Execution Environment (E3). In our analysis we focus on these technologies and their application to the emerging domains of the IoT and CPS.