Biblio

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.

Security is the key concern, which allows safe communication between any two mobile nodes in an unfavorable environment. Wireless Ad Hoc can be unsecured against attacks by means of malicious nodes. Hence this study assesses the influence of wormhole attacks on Mobile Ad Hoc network (MANET) system that is evaluated and validated based on the QualNet simulator. The MANET performance is investigated utilizing the wormhole attacks. The simulation is performed on Mobile node's network layer and data link layer in the WANET (wireless Ad Hoc network). The MANET performance was examined using “what-if” analyses too. Results showed that for security purposes, it is indeed necessary to assess the Mobile Ad Hoc node deployment.

Malicious software (malware) creators are constantly mutating malware files in order to avoid detection, resulting in hundreds of millions of new malware every year. Therefore, most malware files are unlabeled due to the time and cost needed to label them manually. This makes it very challenging to perform malware detection, i.e., deciding whether a file is malware or not, and malware classification, i.e., determining the family of the malware. Most solutions use supervised learning (e.g., ResNet and VGG) whose accuracy degrades significantly with the lack of abundance of labeled data. To solve this problem, this paper proposes a semi-supervised learning model for image-based malware classification. In this model, malware files are represented as grayscale images, and semi-supervised learning is carefully selected to handle the plethora of unlabeled data. Our proposed model is an enhanced version of the ∏-model, which makes it more accurate and consistent. Experiments show that our proposed model outperforms the original ∏-model by 4% in accuracy and three other supervised models by 6% in accuracy especially when the ratio of labeled samples is as low as 20%.

Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.

The unmatched threat of Android malware has tremendously increased the need for analyzing prominent malware samples. There are remarkable efforts in static and dynamic malware analysis using static features and API calls respectively. Nonetheless, there is a void to classify Android malware by analyzing its behavior using multiple dynamic characteristics. This paper proposes EntropLyzer, an entropy-based behavioral analysis technique for classifying the behavior of 12 eminent Android malware categories and 147 malware families taken from CCCS-CIC-AndMal2020 dataset. This work uses six classes of dynamic characteristics including memory, API, network, logcat, battery, and process to classify and characterize Android malware. Results reveal that the entropy-based analysis successfully determines the behavior of all malware categories and most of the malware families before and after rebooting the emulator.

Code-reuse techniques have emerged as a way to defeat the control-flow defenses that prevent the injection and execution of new code, as they allow an adversary to hijack the control flow of a victim program without injected code. A well-known code-reuse attack technique is Return-OrientedProgramming (ROP), which considers and links together (relatively short) code snippets, named ROP gadgets, already present in the victim’s memory address space through a controlled use of the stack values of the victim program. Although ROP attacks are known to be Turing-complete, there are still open question such as the quantification of the executional power of an adversary, which is determined by whatever code exists in the memory of a victim program, and whether an adversary can build a ROP chain, made up of ROP gadgets, for any kind of algorithm. To fill these gaps, in this paper we first define a virtual language, dubbed ROPLANG, that defines a set of operations (specifically, arithmetic, assignment, dereference, logical, and branching operations) which are mapped to ROP gadgets. We then use it to evaluate the executional power of an adversary in Windows 7 and Windows 10, in both 32- and 64-bit versions. In addition, we have developed ROP3, a tool that accepts a set of program files and a ROP chain described with our language and returns the code snippets that make up the ROP chain. Our results show that there are enough ROP gadgets to simulate any virtual operation and that branching operations are the less frequent ones. As expected, our results also indicate that the larger a program file is, the more likely to find ROP gadgets within it for every virtual operation.

Logic locking is a well-explored defense mechanism against various types of hardware security attacks. Recent approaches to logic locking replace portions of a circuit with reconfigurable blocks such as look-up tables (LUTs) and switch boxes (SBs) to primarily achieve logic and routing obfuscation, respectively. However, these techniques may incur significant design overhead, and methods that can mitigate the implementation cost for a given security level are desirable. In this paper, we address this challenge by proposing an algorithm for deciding the location and inputs of the LUTs in LUT-based obfuscation to enhance security and reduce design overhead. We then introduce a locking method that combines LUTs with SBs to further robustify LUT-based obfuscation, largely independently of the specific LUT locations. We illustrate the effectiveness of the proposed approaches on a set of ISCAS benchmark circuits.

Recent technological advancement demands organizations to have measures in place to manage their Information Technology (IT) systems. Enterprise Architecture Frameworks (EAF) offer companies an efficient technique to manage their IT systems aligning their business requirements with effective solutions. As a result, experts have developed multiple EAF's such as TOGAF, Zachman, MoDAF, DoDAF, SABSA to help organizations to achieve their objectives by reducing the costs and complexity. These frameworks however, concentrate mostly on business needs lacking holistic enterprise-wide security practices, which may cause enterprises to be exposed for significant security risks resulting financial loss. This study focuses on evaluating business capabilities in TOGAF, NIST, COBIT, MoDAF, DoDAF, SABSA, and Zachman, and identify essential security requirements in TOGAF, SABSA and COBIT19 frameworks by comparing their resiliency processes, which helps organization to easily select applicable framework. The study shows that; besides business requirements, EAF need to include precise cybersecurity guidelines aligning EA business strategies. Enterprises now need to focus more on building resilient approach, which is beyond of protection, detection and prevention. Now enterprises should be ready to withstand against the cyber-attacks applying relevant cyber resiliency approach improving the way of dealing with impacts of cybersecurity risks.

The circulating current in the D-winding may cause primary current waveform distortion, and the reliability of the restraint criterion based on the typical magnetizing inrush current characteristics will be affected. The magnetizing inrush current with typical characteristics is the sum of primary current and circulating current. Using the circulating current to compensate the primary current can improve the reliability of the differential protection. When the phase is not saturated, the magnetizing inrush current is about zero. Therefore, the primary current of unsaturated phase can be replaced by the opposite of the circulating current. Based on this, an engineering practical calculation method for circulating current is proposed. In the method, the segmented primary currents are used to replace the circulating current. Phasor analysis is used to demonstrate the application effect of this method when remanence coefficients are different. The method is simple and practical, and has strong applicability and high reliability. Simulation and recorded waveforms have verified the effectiveness of the method.

Drone based applications have progressed significantly in recent years across many industries, including agriculture. This paper proposes a sporadically connected cyber-physical system for assisting winemakers and minimizing the travel time to remote and poorly connected infrastructures. A set of representative diseases and conditions, which will be monitored by land-bound sensors in combination with multispectral images, is identified. To collect accurate data, a trustworthy and secured communication of the drone with the sensors and the base station should be established. We propose to use an Internet of Things framework for establishing a chain of trust by securely onboarding drones, sensors and base station, and providing self-adaptation support for the use case. Furthermore, we perform a security analysis of the use case for identifying potential threats and security controls that should be in place for mitigating them.

Increasingly, the transportation industry has moved towards automation to improve safety, fuel efficiency, and system productivity. However, the increased scrutiny that automated vehicles (AV) face over functional safety has hindered the industry's unbridled confidence in self-driving technologies. As AVs are cyber-physical systems, they utilize distributed control to accomplish a range of safety-critical driving tasks. The Operation Systems (OS) serve as the core of these control systems. Therefore, their designs and implementation must incorporate ways to protect AVs against what must be assumed to be inevitable cyberattacks to meet the overall AV functional safety requirements. This paper investigates the connection between functional safety and cybersecurity in the context of OS. This study finds that risks due to delays can worsen by potential cybersecurity vulnerabilities through a case example of an automated vehicle following. Furthermore, attack surfaces and cybersecurity countermeasures for protecting OSs from security breaches are addressed.

Web pages aggressively track users for a variety of purposes from targeted advertisements to enhanced authentication. As browsers move to restrict traditional cookie-based tracking, web pages increasingly move to tracking based on browser fingerprinting. Unfortunately, the state-of-the-art to detect fingerprinting in browsers is often error-prone, resorting to imprecise heuristics and crowd-sourced filter lists. This paper presents EssentialFP, a principled approach to detecting fingerprinting on the web. We argue that the pattern of (i) gathering information from a wide browser API surface (multiple browser-specific sources) and (ii) communicating the information to the network (network sink) captures the essence of fingerprinting. This pattern enables us to clearly distinguish fingerprinting from similar types of scripts like analytics and polyfills. We demonstrate that information flow tracking is an excellent fit for exposing this pattern. To implement EssentialFP we leverage, extend, and deploy JSFlow, a state-of-the-art information flow tracker for JavaScript, in a browser. We illustrate the effectiveness of EssentialFP to spot fingerprinting on the web by evaluating it on two categories of web pages: one where the web pages perform analytics, use polyfills, and show ads, and one where the web pages perform authentication, bot detection, and fingerprinting-enhanced Alexa top pages.

Users rely on their web browser to provide information about the websites they are visiting, such as the security state of the web page their viewing. Current browsers do not differentiate between the live Web and the past Web. If a user loads an archived web page, known as a memento, they have to rely on user interface (UI) elements within the page itself to inform them that the page they are viewing is not the live Web. Memento-awareness extends beyond recognizing a page that has already been archived. The browser should give users the ability to easily archive live web pages as they are browsing. This report presents a proof-of-concept browser that is memento-aware and is created by extending Google's open-source web browser Chromium.

Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment-one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how agile developers may act when behaving strategically. We support our argument by presenting a general model for self-assignment of development tasks based on and extending concepts drawn from established game theory research. We further introduce the software engineering community to two metrics drawn from game theory-the price-of-stability and price-of-anarchy-which can be used to gauge the efficiencies of self-organizing teams compared to centralized management. We demonstrate how these metrics can be used in a case study evaluating the hypothesis that smaller teams self-organize more efficiently than larger teams, with conditional support for that hypothesis. Our game-theoretic framework provides new perspective for the software engineering community, opening many avenues for future research.

Software Defined Networking (SDN) is an innovative technology, which can be applied in a plethora of applications and areas. Recently, SDN has been identified as one of the most promising solutions for industrial applications as well. The key features of SDN include the decoupling of the control plane from the data plane and the programmability of the network through application development. Researchers are looking at these features in order to enhance the Quality of Service (QoS) provisioning of modern network applications. To this end, the following work presents the development of an SDN application, capable of mitigating attacks and maximizing the network’s QoS, by implementing mixed integer linear programming but also using genetic algorithms. Furthermore, a low-cost, physical SDN testbed was developed in order to evaluate the aforementioned application in a more realistic environment other than only using simulation tools.

The Internet of Things technology is one of the important directions of Smart Grid research, involving many wireless sensors and communication facilities, and has high requirements for security. The physical layer security technology can effectively solve the security problems under wireless communication. As the most common application scenario of wireless communication is multi-node wireless network communication, group secret key (GSK) based on physical layer security and information theory security is gradually attracting investigator’s interest. In this paper, a novel physical layer GSK generation scheme based on code-domain exchange of channel information in mesh network is proposed. Instead of traditional side-information exchange in symbol-domain, error-correcting code is applied to finish information exchange and reconciliation simultaneously in code-domain. Each node processes the known channel bit sequence and then encodes it to generate a check sequence. After broadcasting the check bit sequence to other nodes, each node decodes the received check bit sequences to obtained the unknown channel information. The simulation results show that the scheme can effectively reduce the times of information exchanges while keeping a good performance including low bit error rate and low block error rate.

Structural analysis is the study of finding component functions for a given function. In this paper, we proceed with structural analysis of structures consisting of the S (nonlinear Substitution) layer and the A (Affine or linear) layer. Our main interest is the S1AS2 structure with different substitution layers and large input/output sizes. The purpose of our structural analysis is to find the functionally equivalent oracle F* and its component functions for a given encryption oracle F(= S2 ∘ A ∘ S1). As a result, we can construct the decryption oracle F*−1 explicitly and break the one-wayness of the building blocks used in a White-box implementation. Our attack consists of two steps: S layer recovery using multiset properties and A layer recovery using differential properties. We present the attack algorithm for each step and estimate the time complexity. Finally, we discuss the applicability of S1AS2 structural analysis in a White-box Cryptography environment.

Adaptive authentication systems identify and enforce suitable methods to verify that someone (user) or something (device) is eligible to access a service or a resource. An authentication method is usually adapted in response to changes in the security risk or the user's behaviour. Previous work on adaptive authentication systems provides limited guidance about i) what and how contextual factors can affect the selection of an authentication method; ii) which requirements are relevant to an adaptive authentication system and iii) how authentication methods can affect the satisfaction of the relevant requirements. In this paper, we provide a holistic framework informed by previous research to characterize the adaptive authentication problem and support the development of an adaptive authentication system. Our framework explicitly considers the contextual factors that can trigger an adaptation, the requirements that are relevant during decision making and their trade-offs, as well as the authentication methods that can change as a result of an adaptation. From the gaps identified in the literature, we elicit a set of challenges that can be addressed in future research on adaptive authentication.

Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work.

Photo Response Non-Uniformity (PRNU) noise obtained from images or videos is used as a camera fingerprint to attribute visual objects captured by a camera. The PRNU-based source attribution method, however, fails when there is misalignment between the fingerprint and the query object. One example of such a misalignment, which has been overlooked in the field, is caused by the in-camera resizing technique that a video may have been subjected to. This paper investigates the attribution of visual media in the context of matching a video query object to an image fingerprint or vice versa. Specifically this paper focuses on improving camera attribution performance by taking into account the effects of binning, a commonly used in-camera resizing technique applied to video. We experimentally show that the True Positive Rate (TPR) obtained when binning is considered is approximately 3% higher.

Lattice-based Attribute-based encryption is a well-known cryptographic mechanism that can resist quantum attacks and has the ability of fine-grained access control, and it has a wide range of application scenarios in current Internet of Thing (IoT) era. However, lack of efficiency and existing the problem of large ciphertext expansion rate are the main disadvantages impede the applications of this mechanism. Thus, we propose an efficient and practical ciphertext policy attribute-based encryption (CP-ABE) scheme from lattices in the paper. In this scheme, to make the secret key reusable, we adjust access tree and propose a basic access tree structure, which can be converted from disjunctive normal form, and combine it with a light post-quantum scheme of Kyber. In addition, the compression method and plaintext expansion method are introduced to optimize the scheme. Our CP-ABE scheme is secure against chosen plaintext attack under the hardness of module learning with errors problem. We implement our scheme and compare it with three recent related schemes in terms of security, function and communication cost. Experiments and comparisons show that our CP-ABE scheme has advantages in high encryption efficiency, small matrix dimension, small key sizes, and low ciphertext expansion rate, which has some merit in practice.

Replacing real life with the virtual space has long ceased to be a theory. Among the whole variety of visualization, systems that allow projecting non-existent objects into real-world space are especially distinguished. Thus, augmented reality technology has found its application in many different fields. The article discusses the general concepts and principles of building augmented reality systems.

Augmented Reality has a long history and has seen major technical advantages in the last years. With WebXR, a new web standard, Mobile Augmented Reality (MAR) applications are now available in the web browser. With our work, we implemented an Augmented Reality Information System and conducted a case study to evaluate the user acceptance of such an application build with WebXR. Our results indicate that the user acceptance regarding web-based MAR applications for our specific use case seems to be given. With our proposed architecture we also lay the foundation for other AR information systems.

With the rapid development of traffic encryption technology and the continuous emergence of various network services, the classification of encrypted zero-day applications has become a major challenge in network supervision. More seriously, many attackers will utilize zero-day applications to hide their attack behaviors and make attack undetectable. However, there are very few existing studies on zero-day applications. Existing works usually select and label zero-day applications from unlabeled datasets, and these are not true zero-day applications classification. To address the classification of zero-day applications, this paper proposes an Encrypted Zero-day Applications Classification (EZAC) method that combines Convolutional Neural Network (CNN) and K-Means, which can effectively classify zero-day applications. We first use CNN to classify the flows, and for the flows that may be zero-day applications, we use K-Means to divide them into several categories, which are then manually labeled. Experimental results show that the EZAC achieves 97.4% accuracy on a public dataset (CIC-Darknet2020), which outperforms the state-of-the-art methods.

This work proposes an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators. Firstly, by employing the command filtered backstepping technology, the ``explosion of complexity'' problem is overcame. Then, the event-triggered strategy is utilized which makes that the control input is updated aperiodically when the event-trigger occurs. The utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Moreover, it can be proved that all the variables in the closed-loop system are bounded and the tracking error converges asymptotically to zero. Finally, the simulation studies are included to show the effectiveness of the proposed control scheme.