Biblio

Bluetooth Low Energy device is increasing in popularity due to its lower energy consumption and reliable connectivity compared to the classic Bluetooth. Some of these BLE devices collects and transmits health care data like the heart rate as in a Fitbit smart band. This paper will demonstrate that Bluetooth Low Energy devices that relies on BLE security has weak communication security and how to solve that problem using a private-key encryption algorithm.

IoT systems continue to grow in scale and exhibit similarities to complex systems seen in nature and biology: Systems are composed of heterogeneous entities (mobile devices, servers, sensors, data items, databases, etc.) coordinated in a Cloud environment forming a digital eco-system. Properties of such systems include variety, emergent outcome, self-organisation, etc. The scale of IoT systems, and the disparity in the capabilities of the devices on the market, means there needs to be a unifying model to enable a secure and assured interaction among those `things'. The authors propose conceptual designs for an efficient architecture, run-time decision models using assured models for such an interaction in a digital eco-system. This is done using the situation calculus modelling to represent the fundamental requirements for adjustable decentralised feedback control mechanisms necessary for the IoT-ready software systems: It is shown that complex properties and emergent outcomes of the system can be deduced, emanating from the simple distributed interaction models. A case study from the rail industry is used to assess the design and possible implementation.

Thanks to the occurrence of the Internet of Things (IoT), the devices are able to collect and transmit data via the Internet and contributing to our big data world. It will permit devices to exchange monitoring data content in real time. Real-time communication (RTC) with these devices was analyzed in respect to the Network delay. Network coding (NC) combines data packets and the output packet which is a mixture of the input packets. This technique can provide many potential gains to the network, including reducing Round-Trip Time (RTT), decreasing latency and improving Network delay (ND). In the present paper, the authors improve network delay metrics in the context of the remote management of renewable energy using a random NC with an efficient strategy technique.

The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.

Based on our previous work for a novel logging methodology, called flow-net, we propose an Intrusion Detection System (IDS) using Flow-Net Based Fingerprint (IDS-FF) in this paper. We apply the IDS-FF scheme in TCP/IP (Transmission Control Protocol/Internet Protocol) networks for intrusion detection. Experimental results show good performance of the proposed scheme.

We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.

Jasmin is a framework for developing high-speed and high-assurance cryptographic software. The framework is structured around the Jasmin programming language and its compiler. The language is designed for enhancing portability of programs and for simplifying verification tasks. The compiler is designed to achieve predictability and efficiency of the output code (currently limited to x64 platforms), and is formally verified in the Coq proof assistant. Using the supercop framework, we evaluate the Jasmin compiler on representative cryptographic routines and conclude that the code generated by the compiler is as efficient as fast, hand-crafted, implementations. Moreover, the framework includes highly automated tools for proving memory safety and constant-time security (for protecting against cache-based timing attacks). We also demonstrate the effectiveness of the verification tools on a large set of cryptographic routines.

We present the concept of Just-In-Time (JIT) static analysis that interleaves code development and bug fixing in an integrated development environment. Unlike traditional batch-style analysis tools, a JIT analysis tool presents warnings to code developers over time, providing the most relevant results quickly, and computing less relevant results incrementally later. In this paper, we describe general guidelines for designing JIT analyses. We also present a general recipe for transforming static data-flow analyses to JIT analyses through a concept of layered analysis execution. We illustrate this transformation through CHEETAH, a JIT taint analysis for Android applications. Our empirical evaluation of CHEETAH on real-world applications shows that our approach returns warnings quickly enough to avoid disrupting the normal workflow of developers. This result is confirmed by our user study, in which developers fixed data leaks twice as fast when using CHEETAH compared to an equivalent batch-style analysis.

The large scale and high complexity of modern software systems make perfectly precise static code analysis (SCA) infeasible. Therefore SCA tools often over-approximate, so not to miss any real problems. This, however, comes at the expense of raising false alarms, which, in practice, reduces the usability of these tools. To partially address this problem, we propose a novel learning process whose goal is to discover program structures that cause a given SCA tool to emit false error reports, and then to use this information to predict whether a new error report is likely to be a false positive as well. To do this, we first preprocess code to isolate the locations that are related to the error report. Then, we apply machine learning techniques to the preprocessed code to discover correlations and to learn a classifier. We evaluated this approach in an initial case study of a widely-used SCA tool for Java. Our results showed that for our dataset we could accurately classify a large majority of false positive error reports. Moreover, we identified some common coding patterns that led to false positive errors. We believe that SCA developers may be able to redesign their methods to address these patterns and reduce false positive error reports.

The rapid deployment of IoT systems on the public Internet is not without concerns for the security and privacy of consumers. Security in IoT systems is often poorly engineered and engineering for privacy does notseemtobea concern for vendors at all. Thecombination of poor security hygiene and access to valuable knowledge renders IoT systems a much-sought target for attacks. IoT systems are not only Internet-accessible but also play the role of servers according to the established client-server communication model and are thus configured with static and/or easily predictable IPv6 addresses, rendering them an easy target for attacks. We present 6HOP, a novel addressing scheme for IoT devices. Our proposal is lightweight in operation, requires minimal administration overhead, and defends against reconnaissance attacks, address based correlation as well as denial-of-service attacks. 6HOP therefore exploits the ample address space available in IPv6 networks and provides effective protection this way.

Wearable devices can be potentially captured or accessed in an unauthorized manner because of their physical nature. In such cases, they are in white-box attack contexts, where the adversary may have total visibility on the implementation of the built-in cryptosystem, with full control over its execution platform. Dealing with white-box attacks on wearable devices is undoubtedly a challenge. To serve as a countermeasure against threats in such contexts, we propose a lightweight encryption scheme to protect the confidentiality of data against white-box attacks. We constructed the scheme's encryption and decryption algorithms on a substitution-permutation network that consisted of random secret components. Moreover, the encryption algorithm uses random padding that does not need to be correctly decrypted as part of the input. This feature enables non-bijective linear transformations to be used in each encryption round to achieve strong security. The required storage for static data is relatively small and the algorithms perform well on various devices, which indicates that the proposed scheme satisfies the requirements of wearable computing in terms of limited memory and low computational power.

The amount of digital data that requires long-term protection of integrity, authenticity, and confidentiality grows rapidly. Examples include electronic health records, genome data, and tax data. In this paper we present the secure storage system LINCOS, which provides protection of integrity, authenticity, and confidentiality in the long-term, i.e., for an indefinite time period. It is the first such system. It uses the long-term integrity scheme COPRIS, which is also presented here and is the first such scheme that does not leak any information about the protected data. COPRIS uses information-theoretic hiding commitments for confidentiality-preserving integrity and authenticity protection. LINCOS uses proactive secret sharing for confidential storage of secret data. We also present implementations of COPRIS and LINCOS. A special feature of our LINCOS implementation is the use of quantum key distribution and one-time pad encryption for information-theoretic private channels within the proactive secret sharing protocol. The technological platform for this is the Tokyo QKD Network, which is one of worlds most advanced networks of its kind. Our experimental evaluation establishes the feasibility of LINCOS and shows that in view of the expected progress in quantum communication technology, LINCOS is a promising solution for protecting very sensitive data in the cloud.

Securing sensitive digital documents (such as health records, legal reports, government documents, and financial assets) is a critical and challenging task. Unreliable Internet connections, viruses, and compromised file storage systems impose a significant risk on such documents and can compromise their integrity especially when shared across domains while they are shared in offline fashion. In this paper, we present a new framework for maintaining integrity in offline documents and provide a non-repudiation security feature without relying on a central repository of certificates. This framework has been implemented as a plug-in for the Microsoft Word application. It is portable because the plug-in is attached to the document itself and it is scalable because there are no fixed limits on the numbers of users who can collaborate in producing the document. Our framework provides integrity and non-repudiation guarantees for each change in the document's version history.

To address increasing problems caused by cyber attacks, we leverage Software Defined networks and Network Function Virtualisation governed by a SARNET-agent to enable autonomous response and attack mitigation. A Secure Autonomous Response Network (SARNET) uses a control loop to constantly assess the security state of the network by means of observables. Using a prototype we introduce the metrics impact and effectiveness and show how they can be used to compare and evaluate countermeasures. These metrics become building blocks for self learning SARNET which exhibit true autonomous response.

Information-Centric Networking (ICN) 1 is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

For systems composed of many rapidly-deployed microservices that cross networks and span trust domains, strong authentication between microservices is a prerequisite for overall system trustworthiness. We examine standard authentication mechanisms in this context, and we introduce new comprehensive, automated, and fine-grained mutual authentication mechanisms that rely on attestation, with particular attention to provisioning and managing secrets. Prototype implementations and benchmark results indicate that mutual attestation introduces only modest overheads and can be made to meet or exceed the performance of common but weaker authentication mechanisms in many scenarios.

Random Subdomain DDoS attacks on the Domain Name System (DNS) infrastructure are becoming a popular vector in recent attacks (e.g., recent Mirai attack on Dyn). In these attacks, many queries are sent for a single or a few victim domains, yet they include highly varying non-existent subdomains generated randomly. Motivated by these attacks we designed and implemented novel and efficient algorithms for distinct heavy hitters (dHH). A (classic) heavy hitter (HH) in a stream of elements is a key (e.g., the domain of a query) which appears in many elements (e.g., requests). When stream elements consist of ¡key, subkey¿ pairs, (¡domain, subdomain¿) a distinct heavy hitter (dhh) is a key that is paired with a large number of different subkeys. Our algorithms dominate previous designs in both the asymptotic (theoretical) sense and practicality. Specifically the new fixed-size algorithms are simple to code and with asymptotically optimal space accuracy tradeoffs. Based on these algorithms, we build and implement a system for detection and mitigation of Random Subdomain DDoS attacks. We perform experimental evaluation, demonstrating the effectiveness of our algorithms.

In this paper1 is proposed a graph model, designed to solve security challenges of information systems (IS). The model allows to describe information systems at two levels. The first is the transport layer, represented by the graph, and the second is functional level, represented by the semantic network. Proposed model uses "subject-object" terms to establish a security policy. Based on the proposed model, one can define information system security features location, and choose their deployment in the best way. In addition, it is possible to observe data access control security features inadequacy and calculate security value for the each IS node. Novelty of this paper is that one can get numerical evaluation of IS security according to its nodes communications and network structure.

Reactive systems respond to requests from an environment with appropriate timing. Because reactive systems are used widely in infrastructure, it is necessary that they are developed without flaws. Automatic synthesis of reactive systems from particular specifications is an ideal technique for ensuring development without flaws. Several tools for synthesis have been proposed, e.g., Lily, AcaciaPlus and Unbeast. Among them, AcaciaPlus can synthesize systems compositionally, and enables synthesis from large-scale specifications that could not previously be treated. However, the modularization of specifications depends largely on the computation time required for synthesis; this is not a trivial problem. In this paper, we discuss the modularization of specifications to enable efficient synthesis of reactive systems.

The invent of distributed programming frameworks like Hadoop paved way for processing voluminous data known as big data. Due to exponential growth of data, enterprises started to exploit the availability of cloud infrastructure for storing and processing big data. Insider attacks on outsourced data causes leakage of sensitive data. Therefore, it is essential to sanitize data so as to preserve privacy or non-disclosure of sensitive data. Privacy Preserving Data Publishing (PPDP) and Privacy Preserving Data Mining (PPDM) are the areas in which data sanitization plays a vital role in preserving privacy. The existing anonymization techniques for MapReduce programming can be improved to have a misusability measure for determining the level of sanitization to be applied to big data. To overcome this limitation we proposed a framework known as M-Sanit which has mechanisms to exploit misusability score of big data prior to performing sanitization using MapReduce programming paradigm. Our empirical study using the real world cloud eco system such as Amazon Elastic Cloud Compute (EC2) and Amazon Elastic MapReduce (EMR) reveals the effectiveness of misusability score based sanitization of big data prior to publishing or mining it.

Field Programmable Gate Arrays (FPGAs) are being increasingly deployed in diverse applications including the emerging Internet of Things (IoT), biomedical, and automotive systems. However, security of the FPGA configuration file (i.e. bitstream), especially during in-field reconfiguration, as well as effective safeguards against unauthorized tampering and piracy during operation, are notably lacking. The current practice of bitstreram encryption is only available in high-end FPGAs, incurs unacceptably high overhead for area/energy-constrained devices, and is susceptible to side channel attacks. In this paper, we present a fundamentally different and novel approach to FPGA security that can protect against all major attacks on FPGA, namely, unauthorized in-field reprogramming, piracy of FPGA intellectual property (IP) blocks, and targeted malicious modification of the bitstream. Our approach employs the security through diversity principle to FPGA, which is often used in the software domain. We make each device architecturally different from the others using both physical (static) and logical (time-varying) configuration keys, ensuring that attackers cannot use a priori knowledge about one device to mount an attack on another. It therefore mitigates the economic motivation for attackers to reverse engineering the bitstream and IP. The approach is compatible with modern remote upgrade techniques, and requires only small modifications to existing FPGA tool flows, making it an attractive addition to the FPGA security suite. Our experimental results show that the proposed approach achieves provably high security against tampering and piracy with worst-case 14% latency overhead and 13% area overhead.

Human actuation is the idea of using people to provide large-scale force feedback to users. The Haptic Turk system, for example, used four human actuators to lift and push a virtual reality user; TurkDeck used ten human actuators to place and animate props for a single user. While the experience of human actuators was decent, it was still inferior to the experience these people could have had, had they participated as a user. In this paper, we address this issue by making everyone a user. We introduce mutual human actuation, a version of human actuation that works without dedicated human actuators. The key idea is to run pairs of users at the same time and have them provide human actuation to each other. Our system, Mutual Turk, achieves this by (1) offering shared props through which users can exchange forces while obscuring the fact that there is a human on the other side, and (2) synchronizing the two users' timelines such that their way of manipulating the shared props is consistent across both virtual worlds. We demonstrate mutual human actuation with an example experience in which users pilot kites though storms, tug fish out of ponds, are pummeled by hail, battle monsters, hop across chasms, push loaded carts, and ride in moving vehicles.

Immersive, stereoscopic visualization enables scientists to better analyze structural and physical phenomena compared to traditional display mediums. Unfortunately, current head-mounted displays (HMDs) with the high rendering quality necessary for these complex datasets are prohibitively expensive, especially in educational settings where their high cost makes it impractical to buy several devices. To address this problem, we develop two tools: (1) An authoring tool allows domain scientists to generate a set of connected, 360° video paths for traversing between dimensional keyframes in the dataset. (2) A corresponding navigational interface is a video selection and playback tool that can be paired with a low-cost HMD to enable an interactive, non-linear, storytelling experience. We demonstrate the authoring tool's utility by conducting several case studies and assess the navigational interface with a usability study. Results show the potential of our approach in effectively expanding the accessibility of high-quality, immersive visualization to a wider audience using affordable HMDs.

Internet Protocol version 6 (IPv6) over Low-power Wireless Personal Area Networks (6LoWPAN) is extensively used in wireless sensor networks due to its capability to transmit IPv6 packets with low bandwidth and limited resources. 6LoWPAN has several operations in each layer. Most existing security challenges are focused on the network layer, which is represented by the Routing Protocol for Low-power and Lossy Networks (RPL). 6LoWPAN, with its routing protocol (RPL), usually uses nodes that have constrained resources (memory, power, and processor). In addition, RPL messages are exchanged among network nodes without any message authentication mechanism, thereby exposing the RPL to various attacks that may lead to network disruptions. A sinkhole attack utilizes the vulnerabilities in an RPL and attracts considerable traffic by advertising falsified data that change the routing preference for other nodes. This paper proposes the neighbor-passive monitoring technique (NPMT) for detecting sinkhole attacks in RPL-based networks. The proposed technique is evaluated using the COOJA simulator in terms of power consumption and detection accuracy. Moreover, NPMT is compared with popular detection mechanisms.

The traditional block-based compressive sensing (BCS) approach considers the image to be segmented. However, there is not much literature available on how many numbers of blocks or segments per image would be the best choice for the compression and recovery methods. In this article, we propose a BCS method to find out the optimal way of image retrieval, and the number of the blocks to which into image should be divided. In the theoretical analysis, we analyzed the effect of noise under compression perspective and derived the range of error probability. Experimental results show that the number of blocks of an image has a strong correlation with the image recovery process. As the sampling rate M/N increases, we can find the appropriate number of image blocks by comparing each line.