Biblio

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.

With the rapid progress of informatization construction in power business, data resource has become the basic strategic resource of the power industry and innovative element in power production. The security protection of data in power business is particularly important in the informatization construction of power business. In order to implement data security protection, transparent encryption is one of the fifteen key technical standards in the Construction Guideline of the Standard Network Data Security System. However, data storage in the encrypted state is bound to affect the security audit of data to a certain extent. Based on this problem, this paper proposes a scheme to audit the sensitivity of the power business data under the protection of encryption to achieve an efficient sensitivity audit of ciphertext data with the premise of not revealing the decryption key or data information. Through a security demonstration, this paper fully proves that this solution is secure under the known plaintext attacks.

Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.

Individualization of anonymous identities using artificial intelligence - enables innovative human-computer interaction through the personalization of communication which is, at the same time, individual and anonymous. This paper presents possible approach for individualization of anonymous identities in real time. It uses computer vision and artificial intelligence to automatically detect and recognize person's age group, gender, human body measures, proportions and other specific personal characteristics. Collected data constitutes the so-called person's biometric footprint and are linked to a unique (but still anonymous) identity that is recorded in the computer system, along with other information that make up the profile of the person. Identity anonymization can be achieved by appropriate asymmetric encryption of the biometric footprint (with no additional personal information being stored) and integrity can be ensured using blockchain technology. Data collected in this manner is GDPR compliant.

Cloud-based payments, virtual car keys, and digital rights management are examples of consumer electronics applications that use secure software. White-box implementations of the Advanced Encryption Standard (AES) are important building blocks of secure software systems, and the attack of Billet, Gilbert, and Ech-Chatbi (BGE) is a well-known attack on such implementations. A drawback from the adversary’s or security tester’s perspective is that manual reverse engineering of the implementation is required before the BGE attack can be applied. This paper presents a method to automate the BGE attack on a class of white-box AES implementations with a specific type of external encoding. The new method was implemented and applied successfully to a CHES 2016 capture the flag challenge.

Similarity digests have gained popularity for many security applications like blacklisting/whitelisting, and finding similar variants of malware. TLSH has been shown to be particularly good at hunting similar malware, and is resistant to evasion as compared to other similarity digests like ssdeep and sdhash. Searching and clustering are fundamental tools which help the security analysts and security operations center (SOC) operators in hunting and analyzing malware. Current approaches which aim to cluster malware are not scalable enough to keep up with the vast amount of malware and goodware available in the wild. In this paper, we present techniques which allow for fast search and clustering of TLSH hash digests which can aid analysts to inspect large amounts of malware/goodware. Our approach builds on fast nearest neighbor search techniques to build a tree-based index which performs fast search based on TLSH hash digests. The tree-based index is used in our threshold based Hierarchical Agglomerative Clustering (HAC-T) algorithm which is able to cluster digests in a scalable manner. Our clustering technique can cluster digests in O (n logn) time on average. We performed an empirical evaluation by comparing our approach with many standard and recent clustering techniques. We demonstrate that our approach is much more scalable and still is able to produce good cluster quality. We measured cluster quality using purity on 10 million samples obtained from VirusTotal. We obtained a high purity score in the range from 0.97 to 0.98 using labels from five major anti-virus vendors (Kaspersky, Microsoft, Symantec, Sophos, and McAfee) which demonstrates the effectiveness of the proposed method.

The rapid growth of the Internet is, in part, powered by the broad participation of numerous vendors building network components. All these network devices require that they be properly configured and maintained, which creates a challenge for system administrators of complex networks with a growing variety of heterogeneous devices. This challenge is true for today's networks, as well as for the networking architectures of the future, such as Named Data Networking (NDN). This paper gives a preliminary design of an NDNconf framework, an adaptation of a recently developed NETCONF protocol, to realize unified configuration and management for NDN. The presented design is built leveraging the benefits provided by NDN, including the structured naming shared among network and application layers, stateful data retrieval with name-based interest forwarding, in-network caching, data-centric security model, and others. Specifically, the configuration data models, the heart of NDNconf, the elements of the models and models themselves are represented as secured NDN data, allowing fetching models, fetching configuration data that correspond to elements of the model, and issuing commands using the standard Interest-Data exchanges. On top of that, the security of models, data, and commands are realized through native data-centric NDN mechanisms, providing highly secure systems with high granularity of control.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

The intensity of DNA figuring will reinforce the current security on frameworks by opening up another probability of a half and half cryptographic framework. Here, we are exhibiting the DNA S-box for actualizing cryptographic algorithm. The DNA based S-Box is designed using vivado software and implemented using Artix-7 device. The main aim is to design the DNA based S-box to increase the security. Also pipelining and parallelism techniques are to be implement in future to increase the speed.

With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.

Style transfer produces a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer.To do so requires quantitative evaluation procedures, but current evaluation is qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. Our procedure relies on two statistics: the Effectiveness (E) statistic measures the extent that a given style has been transferred to the target, and the Coherence (C) statistic measures the extent to which the original image's content is preserved. Our statistics are calibrated to human preference: targets with larger values of E and C will reliably be preferred by human subjects in comparisons of style and content, respectively.We use these statistics to investigate relative performance of a number of Neural Style Transfer (NST) methods, revealing a number of intriguing properties. Admissible methods lie on a Pareto frontier (i.e. improving E reduces C, or vice versa). Three methods are admissible: Universal style transfer produces very good C but weak E; modifying the optimization used for Gatys' loss produces a method with strong E and strong C; and a modified cross-layer method has slightly better E at strong cost in C. While the histogram loss improves the E statistics of Gatys' method, it does not make the method admissible. Surprisingly, style weights have relatively little effect in improving EC scores, and most variability in transfer is explained by the style itself (meaning experimenters can be misguided by selecting styles). Our GitHub Link is available1.

The current exploratory study examined software programmer trust in binary analysis techniques used to evaluate and understand binary code components. Experienced software developers participated in knowledge elicitations to identify factors affecting trust in tools and methods used for understanding binary code behavior and minimizing potential security vulnerabilities. Developer perceptions of trust in those tools to assess implementation risk in binary components were captured across a variety of application contexts. The software developers reported source security and vulnerability reports provided the best insight and awareness of potential issues or shortcomings in binary code. Further, applications where the potential impact to systems and data loss is high require relying on more than one type of analysis to ensure the binary component is sound. The findings suggest binary analysis is viable for identifying issues and potential vulnerabilities as part of a comprehensive solution for understanding binary code behavior and security vulnerabilities, but relying simply on binary analysis tools and binary release metadata appears insufficient to ensure a secure solution.

Rapidly growing shared information for threat intelligence not only helps security analysts reduce time on tracking attacks, but also bring possibilities to research on adversaries' thinking and decisions, which is important for the further analysis of attackers' habits and preferences. In this paper, we analyze current models and frameworks used in threat intelligence that suited to different modeling goals, and propose a three-layer model (Goal, Behavior, Capability) to study the statistical characteristics of APT groups. Based on the proposed model, we construct a knowledge network composed of adversary behaviors, and introduce a similarity measure approach to capture similarity degree by considering different semantic links between groups. After calculating similarity degrees, we take advantage of Girvan-Newman algorithm to discover community groups, clustering result shows that community structures and boundaries do exist by analyzing the behavior of APT groups.

The JavaCard multi-application platform is now deployed to over twenty billion smartcards, used in various applications ranging from banking payments and authentication tokens to SIM cards and electronic documents. In most of those use cases, access to various cryptographic primitives is required. The standard JavaCard API provides a basic level of access to such functionality (e.g., RSA encryption) but does not expose low-level cryptographic primitives (e.g., elliptic curve operations) and essential data types (e.g., Integers). Developers can access such features only through proprietary, manufacturer-specific APIs. Unfortunately, such APIs significantly reduce the interoperability and certification transparency of the software produced as they require non-disclosure agreements (NDA) that prohibit public sharing of the applet's source code.We introduce JCMathLib, an open library that provides an intermediate layer realizing essential data types and low-level cryptographic primitives from high-level operations. To achieve this, we introduce a series of optimization techniques for resource-constrained platforms that make optimal use of the underlying hardware, while having a small memory footprint. To the best of our knowledge, it is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API.Without any disclosure limitations, JCMathLib has the potential to increase transparency by enabling open code sharing, release of research prototypes, and public code audits. Moreover, JCMathLib can help resolve the conflict between strict open-source licenses such as GPL and proprietary APIs available only under an NDA. This is of particular importance due to the introduction of JavaCard API v3.1, which targets specifically IoT devices, where open-source development might be more common than in the relatively closed world of government-issued electronic documents.

YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.

Chaos-based encryption is one of the promising cryptography techniques that can be used. Although chaos-based encryption provides excellent security, the finite precision of number representation in computers affects decryption accuracy negatively. In this paper, a way to mitigate some problems regarding finite precision is analyzed. We show that the use of maps with small Lyapunov exponents can improve the performance of chaotic encryption scheme, making it suitable for image encryption.

Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.

In VANETs, pseudonyms are often used to replace the identity of vehicles in communication. When vehicles drive out of the network or misbehave, their pseudonym certificates need to be revoked by the certificate authority (CA). The certificate revocation lists (CRLs) are usually used to store the revoked certificates before their expiration. However, using CRLs would incur additional storage, communication and computation overhead. Some existing schemes have proposed to use Bloom Filter to compress the original CRLs, but they are unable to delete the expired certificates and introduce the false positive problem. In this paper, we propose an improved pseudonym certificates revocation scheme, using Cuckoo Filter for compression to reduce the impact of these problems. In order to optimize deletion efficiency, we propose the concept of Certificate Expiration List (CEL) which can be implemented with priority queue. The experimental results show that our scheme can effectively reduce the storage and communication overhead of pseudonym certificates revocation, while retaining moderately low false positive rates. In addition, our scheme can also greatly improve the lookup performance on CRLs, and reduce the revocation operation costs by allowing deletion.

Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations (RP2), generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP2 attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP2 algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this lowpass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.

Bluetooth (BR/EDR) is a pervasive technology for wireless communication used by billions of devices. The Bluetooth standard includes a legacy authentication procedure and a secure authentication procedure, allowing devices to authenticate to each other using a long term key. Those procedures are used during pairing and secure connection establishment to prevent impersonation attacks. In this paper, we show that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment. Such vulnerabilities include the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade. We describe each vulnerability in detail, and we exploit them to design, implement, and evaluate master and slave impersonation attacks on both the legacy authentication procedure and the secure authentication procedure. We refer to our attacks as Bluetooth Impersonation AttackS (BIAS).Our attacks are standard compliant, and are therefore effective against any standard compliant Bluetooth device regardless the Bluetooth version, the security mode (e.g., Secure Connections), the device manufacturer, and the implementation details. Our attacks are stealthy because the Bluetooth standard does not require to notify end users about the outcome of an authentication procedure, or the lack of mutual authentication. To confirm that the BIAS attacks are practical, we successfully conduct them against 31 Bluetooth devices (28 unique Bluetooth chips) from major hardware and software vendors, implementing all the major Bluetooth versions, including Apple, Qualcomm, Intel, Cypress, Broadcom, Samsung, and CSR.

Bluetooth technologies have widespread applications in personal area networks, device-to-device communications and forming ad hoc networks. Studying Bluetooth devices security is a challenging task as they lack support for monitor mode available with other wireless networks (e.g. 802.11 WiFi). In addition, the frequency-hoping spread spectrum technique used in its operation necessitates special hardware and software to study its operation. This investigation examines methods for analyzing Bluetooth devices' security and presents a proof-of-concept DoS attack on the Link Manager Protocol (LMP) layer using the InternalBlue framework. Through this study, we demonstrate a method to study Bluetooth device security using existing tools without requiring specialized hardware. Consequently, the methods proposed in the paper can be used to study Bluetooth security in many applications.

The Robot Operating System (ROS) is a widely adopted standard robotic middleware. However, its preliminary design is devoid of any network security features. Military grade unmanned systems must be guarded against network threats. ROS 2 is built upon the Data Distribution Service (DDS) standard and is designed to provide solutions to identified ROS 1 security vulnerabilities by incorporating authentication, encryption, and process profile features, which rely on public key infrastructure. The Department of Defense is looking to use ROS 2 for its military-centric robotics platform. This paper seeks to demonstrate that ROS 2 and its DDS security architecture can serve as a functional platform for use in military grade unmanned systems, particularly in unmanned Naval aerial swarms. In this paper, we focus on the viability of ROS 2 to safeguard communications between swarms and a ground control station (GCS). We test ROS 2's ability to mitigate and withstand certain cyber threats, specifically that of rogue nodes injecting unauthorized data and accessing services that will disable parts of the UAV swarm. We use the Gazebo robotics simulator to target individual UAVs to ascertain the effectiveness of our attack vectors under specific conditions. We demonstrate the effectiveness of ROS 2 in mitigating the chosen attack vectors but observed a measurable operational delay within our simulations.

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing misbehavior detection systems are static and do not adapt to the context of vehicles. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a context-aware MDS. Based on the context, our proposed system can tune security parameters to provide accurate detection with low false positives. Our system is Sybil attack-resistant and compliant with vehicular privacy standards. The simulation results show that, under different contexts, our system provides a high detection ratio and low false positives compared to a static MDS.

Blockchains and distributed ledgers have brought renewed interest in Byzantine fault-tolerant protocols and decentralized systems, two domains studied for several decades. Recent promising works have in particular proposed to use epidemic protocols to overcome the limitations of popular Blockchain mechanisms, such as proof-of-stake or proof-of-work. These works unfortunately assume a perfect peer-sampling service, immune to malicious attacks, a property that is difficult and costly to achieve. We revisit this fundamental problem in this paper, and propose a novel Byzantine-tolerant peer-sampling service that is resilient to Sybil attacks in open systems by exploiting the underlying structure of wide-area networks.

Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.