Biblio

In this paper, we study the sensor placement problem in urban water networks that maximizes the localization of pipe failures given that some sensors give incorrect outputs. False output of a sensor might be the result of degradation in sensor's hardware, software fault, or might be due to a cyber attack on the sensor. Incorrect outputs from such sensors can have any possible values which could lead to an inaccurate localization of a failure event. We formulate the optimal sensor placement problem with erroneous sensors as a set multicover problem, which is NP-hard, and then discuss a polynomial time heuristic to obtain efficient solutions. In this direction, we first examine the physical model of the disturbance propagating in the network as a result of a failure event, and outline the multi-level sensing model that captures several event features. Second, using a combinatorial approach, we solve the problem of sensor placement that maximizes the localization of pipe failures by selecting m sensors out of which at most e give incorrect outputs. We propose various localization performance metrics, and numerically evaluate our approach on a benchmark and a real water distribution network. Finally, using computational experiments, we study relationships between design parameters such as the total number of sensors, the number of sensors with errors, and extracted signal features.

3D steganography is used in order to embed or hide information into 3D objects without causing visible or machine detectable modifications. In this paper we rethink about a high capacity 3D steganography based on the Hamiltonian path quantization, and increase its resistance to steganalysis. We analyze the parameters that may influence the distortion of a 3D shape as well as the resistance of the steganography to 3D steganalysis. According to the experimental results, the proposed high capacity 3D steganographic method has an increased resistance to steganalysis.

Hardware Malware Detectors (HMDs) have recently been proposed as a defense against the proliferation of malware. These detectors use low-level features, that can be collected by the hardware performance monitoring units on modern CPUs to detect malware as a computational anomaly. Several aspects of the detector construction have been explored, leading to detectors with high accuracy. In this paper, we explore the question of how well evasive malware can avoid detection by HMDs. We show that existing HMDs can be effectively reverse-engineered and subsequently evaded, allowing malware to hide from detection without substantially slowing it down (which is important for certain types of malware). This result demonstrates that the current generation of HMDs can be easily defeated by evasive malware. Next, we explore how well a detector can evolve if it is exposed to this evasive malware during training. We show that simple detectors, such as logistic regression, cannot detect the evasive malware even with retraining. More sophisticated detectors can be retrained to detect evasive malware, but the retrained detectors can be reverse-engineered and evaded again. To address these limitations, we propose a new type of Resilient HMDs (RHMDs) that stochastically switch between different detectors. These detectors can be shown to be provably more difficult to reverse engineer based on resent results in probably approximately correct (PAC) learnability theory. We show that indeed such detectors are resilient to both reverse engineering and evasion, and that the resilience increases with the number and diversity of the individual detectors. Our results demonstrate that these HMDs offer effective defense against evasive malware at low additional complexity.

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

Safety-critical system engineering and traditional safety analyses have for decades been focused on problems caused by natural or accidental phenomena. Security analyses, on the other hand, focus on preventing intentional, malicious acts that reduce system availability, degrade user privacy, or enable unauthorized access. In the context of safety-critical systems, safety and security are intertwined, e.g., injecting malicious control commands may lead to system actuation that causes harm. Despite this intertwining, safety and security concerns have traditionally been designed and analyzed independently of one another, and examined in very different ways. In this work we examine a new hazard analysis technique—Systematic Analysis of Faults and Errors (SAFE)—and its deep integration of safety and security concerns. This is achieved by explicitly incorporating a semantic framework of error "effects" that unifies an adversary model long used in security contexts with a fault/error categorization that aligns with previous approaches to hazard analysis. This categorization enables analysts to separate the immediate, component-level effects of errors from their cause or precise deviation from specification. This paper details SAFE's integrated handling of safety and security through a) a methodology grounded in—and adaptable to—different approaches from the literature, b) explicit documentation of system assumptions which are implicit in other analyses, and c) increasing the tractability of analyzing modern, complex, component-based software-driven systems. We then discuss how SAFE's approach supports the long-term goals of of increased compositionality and formalization of safety/security analysis. 

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

The Internet of Things (IoT) is the latest Internet evolution that interconnects billions of devices, such as cameras, sensors, RFIDs, smart phones, wearable devices, ODBII dongles, etc. Federations of such IoT devices (or things) provides the information needed to solve many important problems that have been too difficult to harness before. Despite these great benefits, privacy in IoT remains a great concern, in particular when the number of things increases. This presses the need for the development of highly scalable and computationally efficient mechanisms to prevent unauthorised access and disclosure of sensitive information generated by things. In this paper, we address this need by proposing a lightweight, yet highly scalable, data obfuscation technique. For this purpose, a digital watermarking technique is used to control perturbation of sensitive data that enables legitimate users to de-obfuscate perturbed data. To enhance the scalability of our solution, we also introduce a contextualisation service that achieve real-time aggregation and filtering of IoT data for large number of designated users. We, then, assess the effectiveness of the proposed technique by considering a health-care scenario that involves data streamed from various wearable and stationary sensors capturing health data, such as heart-rate and blood pressure. An analysis of the experimental results that illustrate the unconstrained scalability of our technique concludes the paper.

Cyber-physical systems connect the physical world and the information world by sensors and actuators. These sensors are usually small embedded systems which have many limitations on wireless communication, computing and storage. This paper proposes a lightweight coding method for secure and reliable transmission over a wireless communication links in cyber-physical systems. The reliability of transmission is provided by forward error correction. And to ensure the confidentiality, we utilize different encryption matrices at each time of coding which are generated by the sequence number of packets. So replay attacks and other cyber threats can be resisted simultaneously. The issues of the prior reliable transmission protocols and secure communication protocols in wireless networks of a cyber-physical system are reduced, such as large protocol overhead, high interaction delay and large computation cost.

The area of secure compilation aims to design compilers which produce hardened code that can withstand attacks from low-level co-linked components. So far, there is no formal correctness criterion for secure compilers that comes with a clear understanding of what security properties the criterion actually provides. Ideally, we would like a criterion that, if fulfilled by a compiler, guarantees that large classes of security properties of source language programs continue to hold in the compiled program, even as the compiled program is run against adversaries with low-level attack capabilities. This paper provides such a novel correctness criterion for secure compilers, called trace-preserving compilation (TPC). We show that TPC preserves a large class of security properties, namely all safety hyperproperties. Further, we show that TPC preserves more properties than full abstraction, the de-facto criterion used for secure compilation. Then, we show that several fully abstract compilers described in literature satisfy an additional, common property, which implies that they also satisfy TPC. As an illustration, we prove that a fully abstract compiler from a typed source language to an untyped target language satisfies TPC.

Subscriber Identity Module (SIM) is the backbone of modern mobile communication. SIM can be used to store a number of user sensitive information such as user contacts, SMS, banking information (some banking applications store user credentials on the SIM) etc. Unfortunately, the current SIM model has a major weakness. When the mobile device is lost, an adversary can simply steal a user's SIM and use it. He/she can then extract the user's sensitive information stored on the SIM. Moreover, The adversary can then pose as the user and communicate with the contacts stored on the SIM. This opens up the avenue to a large number of social engineering techniques. Additionally, if the user has provided his/her number as a recovery option for some accounts, the adversary can get access to them. The current methodology to deal with a stolen SIM is to contact your particular service provider and report a theft. The service provider then blocks the services on your SIM, but the adversary still has access to the data which is stored on the SIM. Therefore, a secure scheme is required to ensure that only legal users are able to access and utilize their SIM.

Artificial software diversity is an effective way to prevent software vulnerabilities and errors to be exploited in code-reuse attacks. This is achieved by lowering the individual probability of a successful attack to a level that makes the attack unfeasible. Unfortunately, the existing approaches are not applicable to safety-critical real-time systems as they induce unacceptable performance overheads, they violate safety and timing guarantees, or they assume hardware resources which are typically not available in embedded systems. To overcome these problems, we propose a safe diversity approach that preserves the timing properties of real-time processes by controlling its impact on the worst case execution time (WCET). Our main idea is to use block-level diversity with a large, but fixed set of movable instruction sequences, and to use static WCET analysis to identify non-critical areas of code where it can safely be split into more movable instruction sequences.

The Science DMZ (SDMZ) is a special purpose network infrastructure that is engineered to cater to the ultra-high bandwidth needs of the scientific and high performance computing (HPC) communities. These networks are isolated from stateful security devices such as firewalls and deep packet inspection (DPI) engines to allow HPC data transfer nodes (DTNs) to efficiently transfer petabytes of data without associated bandwidth and performance bottlenecks. This paper presents our ongoing effort toward the development of more fine-grained data flow access control policies to manage SDMZ networks that service large-scale experiments with varying data sensitivity levels and privacy constraints. We present a novel system, called CoordiNetZ (CNZ), that provides coordinated security monitoring and policy enforcement for sites participating in SDMZ projects by using an intent-based policy framework for effectively capturing the high-level policy intents of non-admin SDMZ project users (e.g., scientists, researchers, students). Central to our solution is the notion of coordinated situational awareness that is extracted from the synthesis of context derived from SDMZ host DTN applications and the network substrate. To realize this vision, we present a specialized process-monitoring system and flow-monitoring tool that facilitate context-aware data-flow intervention and policy enforcement in ultra-highspeed data transfer environments. We evaluate our prototype implementation using case studies that highlight the utility of our framework and demonstrate how security policy could be effectively specified and implemented within and across SDMZ networks.

For over two decades the OpenPGP format has provided the mainstay of email confidentiality and authenticity, and is currently being relied upon to provide authenticated package distributions in open source Unix systems. In this work, we provide the first language theoretical analysis of the OpenPGP format, classifying it as a deterministic context free language and establishing that an automatically generated parser can in principle be defined. However, we show that the number of rules required to describe it with a deterministic context free grammar is prohibitively high, and we identify security vulnerabilities in the OpenPGP format specification. We identify possible attacks aimed at tampering with messages and certificates while retaining their syntactical and semantical validity. We evaluate the effectiveness of these attacks against the two OpenPGP implementations covering the overwhelming majority of uses, i.e., the GNU Privacy Guard (GPG) and Symantec PGP. The results of the evaluation show that both implementations turn out not to be vulnerable due to conser- vative choices in dealing with malicious input data. Finally, we provide guidelines to improve the OpenPGP specification

Technology specific expert knowledge is often required to analyse security configurations and determine potential vulnerabilities, but it becomes difficult when it is a new technology such as Fog computing. Furthermore, additional knowledge is also required regarding how the security configuration has been constructed in respect to an organisation's security policies. Traditionally, organisations will often manage their access control permissions relative to their employees needs, posing challenges to administrators. This problem is even exacerbated in Fog computing systems where security configurations are implemented on a large amount of devices at the edges of Internet, and the administrators are required to retain adequate knowledge on how to perform complex administrative tasks. In this paper, a novel approach of translating object-based security configurations in to a graph model is presented. A technique is then developed to autonomously identify vulnerabilities and perform security auditing of large systems without the need for expert knowledge. Throughout the paper, access control configuration data is used as a case study, and empirical analysis is performed on synthetically generated access control permissions.

RPL is a lightweight IPv6 network routing protocol specifically designed by IETF, which can make full use of the energy of intelligent devices and compute the resource to build the flexible topological structure. This paper analyzes the security problems of RPL, sets up a test network to test RPL network security, proposes a RPL based security routing protocol M-RPL. The routing protocol establishes a hierarchical clustering network topology, the intelligent device of the network establishes the backup path in different clusters during the route discovery phase, enable backup paths to ensure data routing when a network is compromised. Setting up a test prototype network, simulating some attacks against the routing protocols in the network. The test results show that the M-RPL network can effectively resist the routing attacks. M-RPL provides a solution to ensure the Internet of Things (IoT) security.

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.

This paper presents a new approach for a dynamic curtailment method for renewable energy sources that guarantees fulfilling of (n-1)-security criteria of the system. Therefore, it is applicable to high voltage distribution grids and has compliance to their planning guidelines. The proposed dynamic curtailment method specifically reduces the power feed-in of renewable energy sources up to a level, where no thermal constraint is exceeded in the (n-1)-state of the system. Based on AC distribution factors, a new formulation of line outage distribution factors is presented that is applicable for outages consisting of a single line or multiple segment lines. The proposed method is tested using a planning study of a real German high voltage distribution grid. The results show that any thermal loading limits are exceeded by using the dynamic curtailment approach. Therefore, a significant reduction of the grid reinforcement can be achieved by using a small amount of curtailed annual energy from renewable energy sources.

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.