Biblio

The Controller Area Network (CAN) is a widely used industry-standard intra-vehicle broadcast network that connects the Electronic Control Units (ECUs) which control most car systems. The CAN contains substantial vulnerabilities that can be exploited by attackers to gain control of the vehicle, due to its lack of security measures. To prevent an attacker from sending malicious messages through the CAN bus to take over a vehicle, we propose the addition of a secure hardware-based module, or Security ECU (SECU), onto the CAN bus. The SECU can perform key distribution and message verification, as well as corrupting malicious messages before they are fully received by an ECU. Only software modification is needed for existing ECUs, without changing the CAN protocol. This provides backward compatibility with existing CAN systems. Furthermore, we collect 6.673 million CAN bus messages from various cars, and find that the CAN messages collectively have low entropy, with an average of 11.915 bits. This finding motivates our proposal for CAN bus message compression, which allows us to significantly reduce message size to fit the message and its message authentication code (MAC) within one CAN frame, enabling fast authentication. Since ECUs only need to generate the MACs (and not verify them), the delay and computation overhead are also reduced compared to traditional authentication mechanisms. Our authentication mechanism is implemented on a realistic testbed using industry standard MCP2551 CAN transceivers and Raspberry Pi embedded systems. Experimental results demonstrate that our mechanism can achieve real-time message authentication on the CAN bus with minimal latency.

Many-core microprocessor architectures are quickly becoming prevalent in data centers, due to their demonstrated processing power and network flexibility. However, this flexibility comes at a cost; co-mingled data from disparate users must be kept secure, which forces processor cycles to be wasted on cryptographic operations. This paper introduces a novel, secure, stream processing architecture which supports efficient homomorphic authentication of data and enforces secrecy of individuals' data. Additionally, this architecture is shown to secure time-series analysis of data from multiple users from both corruption and disclosure. Hardware synthesis shows that security-related circuitry incurs less than 10% overhead, and latency analysis shows an increase of 2 clocks per hop. However, despite the increase in latency, the proposed architecture shows an improvement over stream processing systems that use traditional security methods.

Monitoring some sites using a wireless sensor network (WSN) may be hampered by the difficulty of recharging or renewing the batteries of the sensing devices. Mechanisms aiming at improving the energy usage at any moment while fulfilling the application requirements are thus key for maximizing the lifetime of such networks. Among the different methods for achieving such a goal, we focus on energy management methods based on duty-cycling allowing the sensors to switch between two modes: a high-energy mode (active) and a low-energy mode (sleep). In this paper we propose two new scheduling heuristics for addressing the problem of maximizing the lifetime of a WSN under the constraint of coverage of a subset of fixed targets. The first one is a stochastic greedy algorithm and the second one is based on applying Simulated Annealing (SA). Both heuristics use a specific knowledge about the problem. Experimental results show that while both algorithms perform well, greedy algorithm is preferable for small and medium sizes networks, and SA algorithm has competitive advantages for larger networks.

Recent research has proposed a moving target defense (MTD) approach that actively changes transmission line susceptance to preclude stealthy false data injection (FDI) attacks against the state estimation of a smart grid. However, existing studies were often conducted under a less adversarial setting, in that they ignore the possibility that an alert attacker can also try to detect the activation of MTD and then cancel any FDI attack until they learn the new system configuration after MTD. Indeed, in this paper, we show that this can be achieved easily by the attacker. To improve the stealthiness of MTD against the attacker, we propose a hidden MTD approach that maintains the power flows of the whole grid after MTD. We develop an algorithm to construct the hidden MTD and analyze its feasibility condition when only a subset of transmission lines can adjust susceptance. Simulations are conducted to demonstrate the effectiveness of the hidden MTD against alert attackers under realistic settings.

Malware analysis uses debuggers to understand and manipulate the behaviors of stripped binaries. To circumvent analysis, malware applies a variety of anti-debugging techniques, such as self-modifying, checking for or removing breakpoints, hijacking keyboard and mouse events, escaping the debugger, etc. Most state-of-the-art debuggers are vulnerable to these anti-debugging techniques. In this paper, we first systematically analyze the spectrum of possible anti-debugging techniques and compile a list of 79 attack vectors. We then propose a framework, called Apate, which detects and defeats each of these attack vectors, by performing: (1) just-in-time disassembling based on single-stepping, (2) careful monitoring of the debuggee's execution and, when needed, modification of the debuggee's states to hide the debugger's presence. We implement Apate as an extension to WinDbg and extensively evaluate it using five different datasets, with known and new malware samples. Apate outperforms other debugger-hiding technologies by a wide margin, addressing 58+–465+ more attack vectors.

Domain squatting is a common adversarial practice where attackers register domain names that are purposefully similar to popular domains. In this work, we study a specific type of domain squatting called "combosquatting," in which attackers register domains that combine a popular trademark with one or more phrases (e.g., betterfacebook[.]com, youtube-live[.]com). We perform the first large-scale, empirical study of combosquatting by analyzing more than 468 billion DNS records - collected from passive and active DNS data sources over almost six years. We find that almost 60% of abusive combosquatting domains live for more than 1,000 days, and even worse, we observe increased activity associated with combosquatting year over year. Moreover, we show that combosquatting is used to perform a spectrum of different types of abuse including phishing, social engineering, affiliate abuse, trademark abuse, and even advanced persistent threats. Our results suggest that combosquatting is a real problem that requires increased scrutiny by the security community.

The process used to build an autonomous smart home system using Cyber-Physical Systems (CPS) principles has received much attention by researchers and developers. However, there are many challenges during the design and implementation of such a system, such as Portability, Timing, Prediction, and Integrity. This paper presents a novel modeling methodology for a smart home system in the scope of CyberPhysical interface that attempts to overcome these issues. We discuss a high-level design approach that simulates the first three levels of a 5C architecture in CPS layers in a smart home environment. A detailed description of the model design, architecture, and a software implementation via NetLogo simulation have been presented in this paper.

Over recent years lattice-based cryptography has received much attention due to versatile average-case problems like Ring-LWE or Ring-SIS that appear to be intractable by quantum computers. In this work, we evaluate and compare implementations of Ring-LWE encryption and the bimodal lattice signature scheme (BLISS) on an 8-bit Atmel ATxmega128 microcontroller. Our implementation of Ring-LWE encryption provides comprehensive protection against timing side-channels and takes 24.9ms for encryption and 6.7ms for decryption. To compute a BLISS signature, our software takes 317ms and 86ms for verification. These results underline the feasibility of lattice-based cryptography on constrained devices.

The proliferation of mobile technology promotes social activities without time and space limitation. Users share information about their interests and preferences through a social network service, blog, or community. However, sensitive personal information may be exposed with the use of social activities. For example, a specific person can be identified according to exposure of personal information on the web. In this paper, we shows that a nickname that is used in an online community can be tracked by analysis of a user's behavior even though the nickname is changed to avoid identification. Unlike existing studies about user identification in a social network service, we focus on online community, which has not been extensively studied. We analyze characteristics of the online community and propose a method to track a user's nickname change to identify the user. We validate the proposed method using data collected from the online community. Results show that the proposed method can track the user's nickname change and link the old nickname with the new one.

This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.

Cloud-centric cognitive cellular networks utilize dynamic spectrum access and opportunistic network access technologies as a means to mitigate spectrum crunch and network demand. However, furnishing a carrier with personally identifiable information for user setup increases the risk of profiling in cognitive cellular networks, wherein users seek secondary access at various times with multiple carriers. Moreover, network access provisioning - assertion, authentication, authorization, and accounting - implemented in conventional cellular networks is inadequate in the cognitive space, as it is neither spontaneous nor scalable. In this paper, we propose a privacy-enhancing user identity management system using blockchain technology which places due importance on both anonymity and attribution, and supports end-to-end management from user assertion to usage billing. The setup enables network access using pseudonymous identities, hindering the reconstruction of a subscriber's identity. Our test results indicate that this approach diminishes access provisioning duration by up to 4x, decreases network signaling traffic by almost 40%, and enables near real-time user billing that may lead to approximately 3x reduction in payments settlement time.

In cloud storage, remote data integrity checking is considered as a crucial technique about data owners who upload enormous data to cloud server provider. A majority of the existing remote data integrity checking protocols rely on the expensive public key infrastructure. In addition, the verification of certificates needs heavy computation and communication cost. Meanwhile, the existing some protocols are not secure under the quantum computer attacks. However, lattice-based constructed cryptography can resist quantum computer attacks and is fairly effective, involving matrix-matrix or matrix-vector multiplications. So, we propose an identity-based remote data integrity checking protocol from lattices, which can eliminate the certificate management process and resist quantum computer attacks. Our protocol is completeness and provably secure based on the hardness small integer solution assumption. The presented scheme is secure against cloud service provider attacks, and leaks no any blocks of the stored file to the third party auditor during verification stage, namely the data privacy against the curiosity third party auditor attacks. The cloud service provider attack includes lost attack and tamper attack. Furthermore, the performance analysis of some protocols demonstrate that our protocol of remote data integrity checking is useful and efficient.

We introduce ImAxes immersive system for exploring multivariate data using fluid, modeless interaction. The basic interface element is an embodied data axis. The user can manipulate these axes like physical objects in the immersive environment and combine them into sophisticated visualisations. The type of visualisation that appears depends on the proximity and relative orientation of the axes with respect to one another, which we describe with a formal grammar. This straight-forward composability leads to a number of emergent visualisations and interactions, which we review, and then demonstrate with a detailed multivariate data analysis use case.

3D imaging has been a hot research topic recently due to a high demand from various applications of security, health, autonomous vehicle and robotics. Yet Stereoscopic 3D imaging is limited due to its principles which mimics the human eye technique thus the camera separation baseline defines amount of 3D depth can be captured. Holoscopic 3D (H3D) Imaging is based on the “Fly's eye” technique that uses coherent replication of light to record a spatial image of a real scene using a microlens array (MLA) which gives the complete 3D parallax. H3D Imaging has been considered a promising 3D imaging technique which pursues the simple form of 3D acquisition using a single aperture camera therefore it is the most suited for scalable digitization, security and autonomous applications. This paper proposes 360-degree holoscopic 3D imaging system design for immersive 3D acquisition and stitching.

In this paper, we present an immersive image database navigation system. Images are visualised in a spherical visualisation space and arranged, on a grid, by colour so that images of similar colour are located close to each other, while access to large image sets is possible through a hierarchical browsing structure. The user is wearing a 3-D head mounted display (HMD) and is immersed inside the image sphere. Navigation is performed by head movement using a 6-degree-of-freedom tracker integrated in the HMD in conjunction with a wiimote remote control.

This paper presents principles of Defensive Programming and examines the growing concern that these principles are not effectively incorporated into Computer Science and related computing degree programs' curricula. To support this concern, Defensive Programming principles are applied to a case study - Cross-site Scripting cybersecurity attacks. This paper concludes that Defensive Programming plays an important role in preventing these attacks and should thus be more aggressively integrated into CS courses such as Programming, Algorithms, Databases, Computer Architecture and Organization, and Computer Networks.

IPv6 Segment Routing is a major IPv6 extension that provides a modern version of source routing that is currently being developed within the Internet Engineering Task Force (IETF). We propose the first open-source implementation of IPv6 Segment Routing in the Linux kernel. We first describe it in details and explain how it can be used on both endhosts and routers. We then evaluate and compare its performance with plain IPv6 packet forwarding in a lab environment. Our measurements indicate that the performance penalty of inserting IPv6 Segment Routing Headers or encapsulating packets is limited to less than 15%. On the other hand, the optional HMAC security feature of IPv6 Segment Routing is costly in a pure software implementation. Since our implementation has been included in the official Linux 4.10 kernel, we expect that it will be extended by other researchers for new use cases.

In smart city construction, wireless sensor networks (WSNs) are normally deployed to collect and transmit real-time data. The nodes of the WSN are embedded facility that integrated sensors and data processing modules. For security and privacy concerns, cryptography methods are required for data protection. However, the Rivest-Shamir-Adleman (RSA) cryptosystem, known as the the most popular and deployed public key algorithm, is still hardly implemented on embedded devices because of the intense computation required from its inherent arithmetic operations. Even though, different methods have being proposed for more efficient RSA implementations such as utilizing the Chinese remainder theorem, various modular exponentiation methods, and optimized modular arithmetic methods. In this paper, we propose an efficient multiplication for long integers on the sensor nodes equipped with 16-bit microcontrollers. Combined with this efficient multiplication, we obtain a faster Montgomery multiplication. The combined optimized Montgomery multiplication, the Chinese remainder theorem, and the m-ary exponentiation method allowed for execution times of less than 44.6 × 106 clock cycles for RSA decryption, a new speed record for the RSA implementation on MSP430 microcontrollers.

In this paper, an improved cooperative jamming (CJ) strategy is developed for physical layer (PHY) security in a multi-hop wireless communication system which employs beamforming in the last hop. Users are assigned to independent groups based on the merger-and-split rule in a coalition game. The secrecy capacity for a valid coalition is a non-convex optimization problem which cannot easily be solved. Therefore, restrictions are added to transform this into a convex problem, and this is solved to obtain a suboptimal closed-form solution for the secrecy capacity. Simulation results are presented which show that the proposed strategy outperforms other methods such as non-cooperation, relay cooperation, and previous CJ approaches in terms of the secrecy capacity. Further, it is shown that the proposed multi-hop solution is suitable for long distance communication systems.

In order to improve the efficiency of the existing homomorphic encryption method, based on the DGHV scheme, an improved fully homomorphic scheme over the integer is proposed. Under the premise of ensuring data owner and user data security, the scheme supports the addition and multiplication operations of ciphertext, and ensures faster execution efficiency and meets the security requirements of cloud computing. Security analysis shows that our scheme is safe. Performance assessment demonstrates that our scheme can more efficiently implement data than DGHV scheme.

The Border Gateway Protocol (BGP) has been used for decades as the de facto protocol to exchange reachability information among networks in the Internet. However, little is known about how this protocol is used to restrict reachability to selected destinations, e.g., that are under attack. While such a feature, BGP blackholing, has been available for some time, we lack a systematic study of its Internet-wide adoption, practices, and network efficacy, as well as the profile of blackholed destinations. In this paper, we develop and evaluate a methodology to automatically detect BGP blackholing activity in the wild. We apply our method to both public and private BGP datasets. We find that hundreds of networks, including large transit providers, as well as about 50 Internet exchange points (IXPs) offer blackholing service to their customers, peers, and members. Between 2014–2017, the number of blackholed prefixes increased by a factor of 6, peaking at 5K concurrently blackholed prefixes by up to 400 Autonomous Systems. We assess the effect of blackholing on the data plane using both targeted active measurements as well as passive datasets, finding that blackholing is indeed highly effective in dropping traffic before it reaches its destination, though it also discards legitimate traffic. We augment our findings with an analysis of the target IP addresses of blackholing. Our tools and insights are relevant for operators considering offering or using BGP blackholing services as well as for researchers studying DDoS mitigation in the Internet.

The Cyber Transport Systems (CTSs) have made significant advancement along with the development of the information technology and transportation industries worldwide. The rapid proliferation of cyber transportation technology provides rich information and infinite possibilities for our society to understand and use the complex inherent mechanism, which governs the novel intelligence world. In addition, applying information technology to cyber transportation applications open a range of new application scenarios, such as vehicular safety, energy efficiency, reduced pollution, and intelligent maintenance services. However, while enjoying the services and convenience provided by CTS, users, vehicles, even the systems might lose privacy during information transmitting and processing. This paper summarizes the state-of-art research findings on information privacy issues in a broad range. We firstly introduce the typical types of information and the basic mechanisms of information communication in CTS. Secondly, considering the information privacy issues of CTS, we present the literature on information privacy issues and privacy protection approaches in CTS. Thirdly, we discuss the emerging challenges and the opportunities for the information technology community in CTS.

Ambient infrasound with frequency ranges well below 20 Hz is known to carry robust navigation cues that can be exploited to authenticate the location of a speaker. Unfortunately, many of the mobile devices like smartphones have been optimized to work in the human auditory range, thereby suppressing information in the infrasonic region. In this paper, we show that these ultra-low frequency cues can still be extracted from a standard smartphone recording by using acceleration-based cepstral features. To validate our claim, we have collected smartphone recordings from more than 30 different scenes and used the cues for scene fingerprinting. We report scene recognition rates in excess of 90% and a feature set analysis reveals the importance of the infrasonic signatures towards achieving the state-of-the-art recognition performance.

Information Security in cloud storage is a key trepidation with regards to Degree of Trust and Cloud Penetration. Cloud user community needs to ascertain performance and security via QoS. Numerous models have been proposed [2] [3] [6][7] to deal with security concerns. Detection and prevention of insider threats are concerns that also need to be tackled. Since the attacker is aware of sensitive information, threats due to cloud insider is a grave concern. In this paper, we have proposed an authentication mechanism, which performs authentication based on verifying facial features of the cloud user, in addition to username and password, thereby acting as two factor authentication. New QoS has been proposed which is capable of monitoring and detection of insider threats using Machine Learning Techniques. KNN Classification Algorithm has been used to classify users into legitimate, possibly legitimate, possibly not legitimate and not legitimate groups to verify image authenticity to conclude, whether there is any possible insider threat. A threat detection model has also been proposed for insider threats, which utilizes Facial recognition and Monitoring models. Security Method put forth in [6] [7] is honed to include threat detection QoS to earn higher degree of trust from cloud user community. As a recommendation, Threat detection module should be harnessed in private cloud deployments like Defense and Pharma applications. Experimentation has been conducted using open source Machine Learning libraries and results have been attached in this paper.

New generations of industrial control systems offer higher performance, they are distributed, and it is very likely that they are internet connected in one way or another. These trends raise new challenges in the contexts of reliability and security. We propose a novel approach that tackles the complexity of industrial control systems at design time and run time. At design time our target is to ease the configuration and verification of controller configurations through model-driven engineering techniques together with the contract-based design paradigm. At run time the information from design time is reused in order to support a modular and distributed self-adaptive software system that aims to increase reliability and security. The industrial setting of the presented approach are control devices for hydropower plant units.