Biblio

There is increased interest in smart vehicles acting as both data consumers and producers in smart cities. Vehicles can use smart city data for decision-making, such as dynamic routing based on traffic conditions. Moreover, the multitude of embedded sensors in vehicles can collectively produce a rich data set of the urban landscape that can be used to provide a range of services. Key to the success of this vision is a scalable and private architecture for trusted data sharing. This paper proposes a framework called SpeedyChain, that leverages blockchain technology to allow smart vehicles to share their data while maintaining privacy, integrity, resilience, and non-repudiation in a decentralized and tamper-resistant manner. Differently from traditional blockchain usage (e.g., Bitcoin and Ethereum), the proposed framework uses a blockchain design that decouples the data stored in the transactions from the block header, thus allowing fast addition of data to the blocks. Furthermore, an expiration time for each block is proposed to avoid large sized blocks. This paper also presents an evaluation of the proposed framework in a network emulator to demonstrate its benefits.

Emerging networking technologies, such as cloud and Software Defined Networking, provide flexibility, elasticity and functionalities to change the network configurations over time. However, changes also impose unpredictable security postures at different times, creating difficulties to the security assessment of the network. To address this issue, we propose a stateless security risk assessment, which combines the security posture of network states at different times to provide an overall security overview. This paper describes the methodologies of the stateless security risk assessment. Our approach is applicable to any emerging networking technologies with dynamic changes.

It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples.

Ransomware represents a significant threat to both individuals and organizations. Moreover, the emergence of ransomware that exploits kernel vulnerabilities poses a serious detection challenge. In this paper, we propose a novel ransomware detection mechanism at a storage device, especially a flash-based storage device. To this end, we design a new buffer management policy that allows our detector to identify ransomware behaviors. Our mechanism detects a realistic ransomware sample with little negative impacts on the hit ratios of the buffers internally located in a storage device.

In the present generation internet plays a major role. The data being sent by the user is created by the things like pc, mobiles, sensors etc. and these data are sent to the cloud system. When a data from the IOT devices are sent to the cloud, there is a question of privacy and security. To provide security for the data well-known security algorithms are used in fog layer and are successful in transferring the data without any damage. Here different techniques used for providing security for IOT data are discussed.

Along with the continuous progress of the information technology, Internet of Things is the inevitable way for realizing the fusion of communication and traditional network technology. Network coding, an important breakthrough in the field of communication, has many applied advantages in information network. This article analyses the eavesdropping problem of Internet of Things and presents an information secure network coding scheme against the eavesdropping adversaries. We show that, if the number of links the adversaries can eavesdrop on is less than the max-flow of a network, the proposed coding scheme not only `achieves the prefect information secure condition but also the max-flow of the network.

Nowadays public-key cryptography is based on number theory problems, such as computing the discrete logarithm on an elliptic curve or factoring big integers. Even though these problems are considered difficult to solve with the help of a classical computer, they can be solved in polynomial time on a quantum computer. Which is why the research community proposed alternative solutions that are quantum-resistant. The process of finding adequate post-quantum cryptographic schemes has moved to the next level, right after NIST's announcement for post-quantum standardization. One of the oldest quantum-resistant proposition goes back to McEliece in 1978, who proposed a public-key cryptosystem based on coding theory. It benefits of really efficient algorithms as well as a strong mathematical background. Nonetheless, its security has been challenged many times and several variants were cryptanalyzed. However, some versions remain unbroken. In this paper, we propose to give some background on coding theory in order to present some of the main flawless in the protocols. We analyze the existing side-channel attacks and give some recommendations on how to securely implement the most suitable variants. We also detail some structural attacks and potential drawbacks for new variants.

Providing fault tolerance in Mobile Ad hoc Networks (MANETs) is very tricky activity as nodes migrate from one place to other place and changes network topology. Also MANET is very susceptible for various attacks like DoS attacks etc. So providing security to MANET is also very difficult job. Multipath protocols provide better results than unipath protocols. Multipath protocols provide fault tolerance but many multipath protocols for MANETs not targeted security issues. Distributed and cooperative security that means Intrusion Detection System (IDS) gives better security to MANETs. In this paper we have discussed many confronts and concerns regarding fault tolerance and IDS.

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

Privacy issues can be difficult for end-users to understand and are therefore a key concern for information-sharing systems. This paper describes a deployment of fifteen Bluetooth-beacon-enabled 'creatures' spread across London's Queen Elizabeth Olympic Park, which initiate conversations on mobile phones in their vicinity via push notifications. Playing on the common assumption that neutral public settings promote anonymity, users' willingness to converse with personified chatbots is used as a proxy for understanding their inclination to share personal and potentially disclosing information. Each creature is linked to a conversational agent that asks for users' memories and their responses are then shared with other creatures in the network. This paper presents the design of an interactive device used to test users' awareness of how their information propagates to others.

Assuring integrity of information (e.g., data and/or software) is usually accomplished by cryptographic means, such as hash functions or message authentication codes (MACs). Computing such integrity-ensuring functions can be time-consuming if the amount of input data is large and/or the computing platform is weak. At the same time, in real-time or safety-critical settings, it is often impractical or even undesirable to guarantee atomicity of computing a time-consuming integrity-ensuring function. Meanwhile, standard correctness and security definitions of such functions assume that input data (regardless of its size) remains consistent throughout computation. However, temporal consistency may be lost if another process interrupts execution of an integrity-ensuring function and modifies portions of input that either or both: (1) were already processed, or (2) were not processed yet. Lack of temporal consistency might yield an integrity result that is non-sensical or simply incorrect. Such subtleties and discrepancies between (implicit) assumptions in definitions and implementations can be a source of inconsistenceies, which might lead to vulnerabilities. In this paper, we systematically explore the notion of temporal consistency of cryptographic integrity-ensuring functions. We show that its lack in implementations of such functions can lead to inconsistent results and security violations in protocols and systems using them, e.g., remote attestation, remote updates and secure resets. We consider several mechanisms that guarantee temporal consistency of implementations of integrity-ensuring functions in embedded systems with a focus on remote attestation. We also assess performance of proposed mechanisms on two commodity hardware platforms: I.MX6-SabreLite and ODROID-XU4.

Bluetooth Low Energy (BTLE) is pervasive in technology throughout all areas of our lives. In this research effort, experiments are performed to discover vulnerabilities in the Bluetooth protocol and given the right technology determine exploitation. Using a Bluetooth keyboard, practical examples of the Bluetooth Low Energy protocol were able to be provided. Because of the results garnered, it is recommended that Bluetooth Low Energy not be used for any connections that may transmit sensitive data, or with devices that may have access to sensitive networks.

Sentiment analysis and other practices for text analytics on social media rely on publicly available and editable collections of data for training and evaluation. These data collections are subject to poisoning and data contamination attacks by adversaries having an interest in misleading the results of the performed analysis. We present the problem of adversarial text mining with a focus on decision making and we suggest cross-discipline, cross-application and cross-model strategies for more robust analyses. Our approach is practitioner-centric and is based on broadly-used interpretable models with applications in decision making.

This paper proposed an advanced round modification fault analysis (RMFA) at the theoretical level on AEGIS-128, which is one of seven finalists in CAESAR competition. First, we clarify our assumptions and simplifications on the attack model, focusing on the encryption security. Then, we emphasize the difficulty of applying vanilla RMFA to AEGIS-128 in the practical case. Finally we demonstrate our advanced fault analysis on AEGIS-128 using machine-solver based algebraic techniques. Our enhancement can be used to conquer the practical scenario which is difficult for vanilla RMFA. Simulation results show that when the fault is injected to the initialization phase and the number of rounds is reduced to one, two samples of injections can extract the whole 128 key bits within less than two hours. This work can also be extended to other versions such as AEGIS-256.

Finding identical digital objects (or artifacts) during a forensic analysis is commonly achieved by means of cryptographic hashing functions, such as MD5, SHA1, or SHA-256, to name a few. However, these functions suffer from the avalanche effect property, which guarantees that if an input is changed slightly the output changes significantly. Hence, these functions are unsuitable for typical digital forensics scenarios where a forensics memory image from a likely compromised machine shall be analyzed. This memory image file contains a snapshot of processes (instances of executable files) which were up on execution when the dumping process was done. However, processes are relocated at memory and contain dynamic data that depend on the current execution and environmental conditions. Therefore, the comparison of cryptographic hash values of different processes from the same executable file will be negative. Bytewise approximation matching algorithms may help in these scenarios, since they provide a similarity measurement in the range [0,1] between similar inputs instead of a yes/no answer (in the range 0,1). In this paper, we introduce ProcessFuzzyHash, a Volatility plugin that enables us to compute approximation hash values of processes contained in a Windows memory dump.

While cyberattacks pose a relatively new challenge for power grid control systems, commercial cloud systems have needed to address similar threats for many years. However, technology and approaches developed for cloud systems do not necessarily transfer directly to the power grid, due to important differences between the two domains. We discuss our experience adapting intrusion-tolerant cloud technologies to the power domain and describe the challenges we have encountered and potential directions for overcoming those obstacles.

Due to the abundance of security breaches we continue to see, the software development community is recently paying attention to a more proactive approach towards security. This includes predicting vulnerability before exploitation employing static code analysis and machine learning techniques. Such mechanisms, however, are designed to detect post-implementation vulnerabilities. As the root of a vulnerability can often be traced back to the requirement specification, and vulnerability discovered later in the development life cycle is more expensive to fix, we need additional preventive mechanisms capable of predicting vulnerability at a much earlier stage. In this paper, we propose a novel framework providing an automated support to predict vulnerabilities for a requirement as early as during requirement engineering. We further present a preliminary demonstration of our framework and the promising results we observe clearly indicate the value of this new research idea.

Nowadays, image captchas are being widely used across the Internet to defend against abusive programs. However, the ever-advancing capabilities of computer vision techniques are gradually diminishing the security of image captchas; yet, little is known thus far about the vulnerability of image captchas deployed in real-world settings. In this paper, we conduct the first systematic study on the security of image captchas in the wild. We classify the currently popular image captchas into three categories: selection-, slide- and click-based captchas. We propose three effective and generic attacks, each against one of these categories. We evaluate our attacks against 10 real-world popular image captchas, including those from tencent.com, google.com, and 12306.cn. Furthermore, we compare our attacks with 9 online image recognition services and human labors from 8 underground captcha-solving services. Our studies show that: (1) all of those popular image captchas are vulnerable to our attacks; (2) our attacks significantly outperform the state-of-the-arts in almost all the scenarios; and (3) our attacks achieve effectiveness comparable to human labors but with much higher efficiency. Based on our evaluation, we identify the design flaws of those popular schemes, the best practices, and the design principles towards more secure captchas.

The heart of any enterprise is its databases where the application data is stored. Organizations frequently place certain access control mechanisms to prevent access by unauthorized employees. However, there is persistent concern about malicious insiders. Anomaly-based intrusion detection systems are known to have the potential to detect insider attacks. Accurate modelling of insiders behaviour within the framework of Relational Database Management Systems (RDBMS) requires attention. The majority of past research considers SQL queries in isolation when modelling insiders behaviour. However, a query in isolation can be safe, while a sequence of queries might result in malicious access. In this work, we consider sequences of SQL queries when modelling behaviours to detect malicious RDBMS accesses using frequent and rare item-sets mining. Preliminary results demonstrate that the proposed approach has the potential to detect malicious RDBMS accesses by insiders.

Locking the functionality of an integrated circuit (IC) thwarts attacks such as intellectual property (IP) piracy, hardware Trojans, overbuilding, and counterfeiting. Although functional locking has been extensively investigated, locking the performance of an IC has been little explored. In this paper, we develop provably-secure performance locking, where only on applying the correct key the IC shows superior performance; for an incorrect key, the performance of the IC degrades significantly. This leads to a new business model, where the companies can design a single IC capable of different performances for different users. We develop mathematical definitions of security and theoretically, and experimentally prove the security against the state-of-the-art-attacks. We implemented performance locking on a FabScalar microprocessor, achieving a degradation in instructions per clock cycle (IPC) of up to 77% on applying an incorrect key, with an overhead of 0.6%, 0.2%, and 0% for area, power, and delay, respectively.

Technologies such as Industry 4.0 or assisted/autonomous driving are relying on highly customized cyber-physical realtime systems. Those systems are designed to match functional safety regulations and requirements such as EN ISO 13849, EN IEC 62061 or ISO 26262. However, as systems - especially vehicles - are becoming more connected and autonomous, they become more likely to suffer from new attack vectors. New features may meet the corresponding safety requirements but they do not consider adversaries intruding through security holes with the purpose of bringing vehicles into unsafe states. As research goal, we want to bridge the gap between security and safety in cyber-physical real-time systems by investigating real-time-aware intrusion-tolerant architectures for automotive use-cases.

Trademarks are recognizable images and/or words used to distinguish various products or services. They become associated with the reputation, innovation, quality, and warranty of the products. Countries around the world have offices for industrial/intellectual property (IP) registration. A new trademark image in application for registration should be distinct from all the registered trademarks. Due to the volume of trademark registration applications and the size of the databases containing existing trademarks, it is impossible for humans to make all the comparisons visually. Therefore, technological tools are essential for this task. In this work we use a pre-trained, publicly available Convolutional Neural Network (CNN) VGG19 that was trained on the ImageNet database. We adapted the VGG19 for the trademark image retrieval (TIR) task by fine tuning the network using two different databases. The VGG19v was trained with a database organized with trademark images using visual similarities, and the VGG19c was trained using trademarks organized by using conceptual similarities. The database for the VGG19v was built using trademarks downloaded from the WEB, and organized by visual similarity according to experts from the IP office. The database for the VGG19c was built using trademark images from the United States Patent and Trademarks Office and organized according to the Vienna conceptual protocol. The TIR was assessed using the normalized average rank for a test set from the METU database that has 922,926 trademark images. We computed the normalized average ranks for VGG19v, VGG19c, and for a combination of both networks. Our method achieved significantly better results on the METU database than those published previously.

Wireless Mesh Network (WMN) is a promising networking technology, which is cost effective, robust, easily maintainable and provides reliable service coverage. WMNs do not rely on a centralized administration and have a distributed nature in which nodes can participate in routing packets. Such design and structure makes WMNs vulnerable to a variety of security threats. Therefore, to ensure secure route discovery in WMNs, we propose a trust model which is based on Evidence- Based Subjective Logic (EBSL). The proposed trust model computes trust values of individual nodes and manages node reputation. We use watchdog detection mechanism to monitor selfish behavior in the network. A node's final trust value is calculated by aggregating the nodes direct and recommendation trust information. The proposed trust model ensures secure routing of packets by avoiding paths with untrusted nodes. The trust model is able to detect selfish behavior such as black-hole and gray-hole attacks.

With the advent of network connectivity and complex software applications, life-critical systems like medical devices are subject to a plethora of security risks and vulnerabilities. Security threats and attacks exploiting these vulnerabilities have been shown to compromise patient safety by hampering essential functionality. This necessitates incorporating security from the very design of software. Isolation of software functionality into different modes and switching between them based on risk assessment, while maintaining a fail-safe mode ensuring device's essential functionality is a compelling design. Formal modeling is an essential ingredient for verification of such a design. Hence, in this paper, we formally model a trustworthy multi-modal framework for life-critical systems security and in turn safety. We formalize a multiple mode based software design approach of operation with a fail-safe mode that maintains critical functionality. We ensure trustworthyness by formalizing a composite risk model incorporated into the design for run-time risk assessment and management.

Wireless mesh networking (WMN) is a new technology aimed to introduce the benefits of using multi-hop and multi-path to the wireless world. However, the absence of a fast and reliable handoff protocol is a major drawback especially in a technology designed to feature high mobility and scalability. We propose a fast and efficient handoff authentication protocol for wireless mesh networks. It is a token-based authentication protocol using pre-distributed parameters. We provide a performance comparison among our protocol, UFAP, and other protocols including EAP-TLS and EAP-PEAP tested in an actual setup. Performance analysis will prove that our proposed handoff authentication protocol is 250 times faster than EAP-PEAP and 500 times faster than EAP-TLS. The significant improvement in performance allows UFAP to provide seamless handoff and continuous operation even for real-time applications which can only tolerate short delays under 50 ms.