Biblio

In this paper, a generic model for a differential stripline is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential stripline is used to take into consideration the frequency-dependent dielectric loss. In order to demonstrate the expandability of this approach, the methodology was applied to two differential pairs of striplines in the frequency range of 10 MHz to 20 GHz.

This paper proposes a novel extension of blockchain systems with fuzzy ontologies. The main advantage is to let the users have flexible restrictions, represented using fuzzy sets, and to develop smart contracts where there is a partial agreement among the involved parts. We propose a general architecture based on four fuzzy ontologies and a process to develop and run the smart contracts, based on a reduction to a well-known fuzzy ontology reasoning task (Best Satisfiability Degree). We also investigate different operators to compute Pareto-optimal solutions and implement our approach in the Ethereum blockchain.

The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

Zigbee network security relies on symmetric cryptography based on a pre-shared secret. In the current Zigbee protocol, the network coordinator creates a network key while establishing a network. The coordinator then shares the network key securely, encrypted under the pre-shared secret, with devices joining the network to ensure the security of future communications among devices through the network key. The pre-shared secret, therefore, needs to be installed in millions or more devices prior to deployment, and thus will be inevitably leaked, enabling attackers to compromise the confidentiality and integrity of the network. To improve the security of Zigbee networks, we propose a new certificate-less Zigbee joining protocol that leverages low-cost public-key primitives. The new protocol has two components. The first is to integrate Elliptic Curve Diffie-Hellman key exchange into the existing association request/response messages, and to use this key both for link-to-link communication and for encryption of the network key to enhance privacy of user devices. The second is to improve the security of the installation code, a new joining method introduced in Zigbee 3.0 for enhanced security, by using public key encryption. We analyze the security of our proposed protocol using the formal verification methods provided by ProVerif, and evaluate the efficiency and effectiveness of our solution with a prototype built with open source software and hardware stack. The new protocol does not introduce extra messages and the overhead is as lows as 3.8% on average for the join procedure.

Internet of Things (IoT) is experiencing significant growth in the safety-critical applications which have caused new security challenges. These devices are becoming targets for different types of physical attacks, which are exacerbated by their diversity and accessibility. Therefore, there is a strict necessity to support embedded software developers to identify and remediate the vulnerabilities and create resilient applications against such attacks. In this paper, we propose a hardware security vulnerability assessment based on fault injection of an embedded application. In our security assessment, we apply a fault injection attack by using our clock glitch generator on a critical medical IoT device. Furthermore, we analyze the potential risks of ignoring these attacks in this embedded application. The results will inform the embedded software developers of various security risks and the required steps to improve the security of similar MCU-based applications. Our hardware security assessment approach is easy to apply and can lead to secure embedded IoT applications against fault attacks.

Brute-force login attempts are common for every host on the public Internet. While most of them can be discarded as low-threat attacks, targeted attack campaigns often use a dictionary-based brute-force attack to establish a foothold in the network. Therefore, it is important to characterize the attackers' behavior to prioritize defensive measures and react to new threats quickly. In this paper we present a set of metrics that can support threat hunters in characterizing brute-force login attempts. Based on connection metadata, timing information, and the attacker's dictionary these metrics can help to differentiate scans and to find common behavior across distinct IP addresses. We evaluated our novel metrics on a real-world data set of malicious login attempts collected by our honeypot Honeygrove. We highlight interesting metrics, show how clustering can be leveraged to reveal common behavior across IP addresses, and describe how selected metrics help to assess the threat level of attackers. Amongst others, we for example found strong indicators for collusion between ten otherwise unrelated IP addresses confirming that a clustering of the right metrics can help to reveal coordinated attacks.

Two-Dimensional barcodes are used as data authentication storage tool on several cryptographic architectures. This article describes a novel meaningful image authentication method for data validation using the Meaningless Reversible Degradation concept and QR Codes. The system architecture use the Meaningless Reversible Degradation algorithm, systematic Reed-Solomon error correction codes, meaningful images, and QR Codes. The encoded images are the secret key for visual validation. The proposed work encodes any secret image file up to 3.892 Bytes and is decoded using data stored in a QR Code and a digital file retrieved through a wireless connection on a mobile device. The QR Code carries partially distorted and stream ciphered bits. The QR Code version is defined in conformity with the secret image file size. Once the QR Code data is decoded, the authenticating party retrieves a previous created Reed-Solomon redundancy file to correct the QR Code stored data. Finally, the secret image is decoded for user visual identification. A regular QR Code reader cannot decode any meaningful information when the QR Code is scanned. The presented cryptosystem improves the redundancy download file size up to 50% compared to a plaintext image transmission.

With the deep research on coherent optical OFDM offset quadrature amplitude modulation OFDM/OQAM in these years, and the communication system exposed to potential threat from various capable attackers, which prompt people lay emphasis on encryption methods for transmission. Therefore, in this paper, we systematically discuss an encryption project with the main purpose of improving security in coherent optical OFDM/OQAM (CO-OFDM/OQAM) system, and the scheme applied the chaotic constellation scrambling (CCS) which founded on chaotic cross mapping to encrypt transmitted information. Besides, we also systematically discuss the basic principle of the encryption scheme for CO-OFDM/OQAM system. According to numerous studies and analysis on experiment data with caution, such as the performance of entropy, bit error rate (BER). It's conforms that the security of CO-OFDM/OQAM system have been enhanced.

For streaming applications, we consider parallel burst erasure channels in the presence of an eavesdropper. The legitimate receiver must perfectly recover each source symbol subject to a decoding delay constraint without the eavesdropper gaining any information from his observation. For a certain class of code parameters, we propose delay-optimal M-link codes that recover multiple bursts of erasures of a limited length, and where the codes provide perfect security even if the eavesdropper can observe a link of his choice. Our codes achieve the maximum secrecy rate for the channel model.

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.

It is now possible to synthesize highly realistic images of people who do not exist. Such content has, for example, been implicated in the creation of fraudulent socialmedia profiles responsible for dis-information campaigns. Significant efforts are, therefore, being deployed to detect synthetically-generated content. One popular forensic approach trains a neural network to distinguish real from synthetic content.We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near- 0% accuracy. We develop five attack case studies on a state- of-the-art classifier that achieves an area under the ROC curve (AUC) of 0.95 on almost all existing image generators, when only trained on one generator. With full access to the classifier, we can flip the lowest bit of each pixel in an image to reduce the classifier's AUC to 0.0005; perturb 1% of the image area to reduce the classifier's AUC to 0.08; or add a single noise pattern in the synthesizer's latent space to reduce the classifier's AUC to 0.17. We also develop a black-box attack that, with no access to the target classifier, reduces the AUC to 0.22. These attacks reveal significant vulnerabilities of certain image-forensic classifiers.

Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-1 label of the classifier. Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples. We propose an effective iterative algorithm to generate query-efficient black-box perturbations with small p norms which is confirmed via experimental evaluations on state-of-the-art natural image classifiers. Moreover, for p=2, we theoretically show that our algorithm actually converges to the minimal perturbation when the curvature of the decision boundary is bounded. We also obtain the optimal distribution of the queries over the iterations of the algorithm. Finally, experimental results confirm that our principled black-box attack algorithm performs better than state-of-the-art algorithms as it generates smaller perturbations with a reduced number of queries.

For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.

Session management is a fundamental component of web applications: despite the apparent simplicity, correctly implementing web sessions is extremely tricky, as witnessed by the large number of existing attacks. This motivated the design of formal methods to rigorously reason about web session security which, however, are not supported at present by suitable automated verification techniques. In this paper we introduce the first security type system that enforces session security on a core model of web applications, focusing in particular on server-side code. We showcase the expressiveness of our type system by analyzing the session management logic of HotCRP, Moodle, and phpMyAdmin, unveiling novel security flaws that have been acknowledged by software developers.

The major energy-hungry components in today's battery-operated embedded devices are mostly peripheral modules like LTE, WiFi, GPS, etc. Inefficient use of these modules causes energy hotspots, namely segments of the embedded software in which the module wastes energy. We study two such hotspots in the current paper, and provide the corresponding detection and removal algorithms based on static analysis techniques. The program code hotspots occur due to unnecessary releasing and re-acquiring of a module (which puts the module in power saving mode for a while) and misplaced acquiring of the module (which makes the module or processor to waste energy in idle mode). The detections are performed according to some relation between extreme (worst-case/best-case) execution times of some program segments and time/energy specifications of the module. The experimental results on our benchmarks show about 28 percent of energy reduction after the hotspot removals.

File sharing applications using cloud storage are increasingly popular for personal and business use. Due to data protection concerns, end-to-end encryption is often a desired feature of these applications. Many attempts at designing cryptographic solutions fail to be adopted due to missing relevant features. We present SeGShare, a new architecture for end-to-end encrypted, group-based file sharing using trusted execution environments (TEE), e.g., Intel SGX. SeGShare is the first solution to protect the confidentiality and integrity of all data and management files; enforce immediate permission and membership revocations; support deduplication; and mitigate rollback attacks. Next to authentication, authorization and file system management, our implementation features an optimized TLS layer that enables high throughput and low latency. The encryption overhead of our implementation is extremely small in computation and storage resources. Our enclave code comprises less than 8500 lines of code enabling efficient mitigation of common pitfalls in deploying code to TEEs.

We determine the semantic security capacity for quantum wiretap channels. We extend methods for classical channels to quantum channels to demonstrate that a strongly secure code guarantees a semantically secure code with the same secrecy rate. Furthermore, we show how to transform a non-secure code into a semantically secure code by means of biregular irreducible functions (BRI functions). We analyze semantic security for classical-quantum channels and for quantum channels.

The Classification of devices involved in authentication and classification of authentication systems by type and combination of protocols used are proposed. The system architecture for soft multi-factor authentication designed and simulated.

Information Flow Control (IFC) is a collection of techniques for ensuring a no-write-down no-read-up style security policy known as noninterference. Traditional methods for both static (e.g. type systems) and dynamic (e.g. runtime monitors) IFC suffer from untenable numbers of false alarms on real-world programs. Secure Multi-Execution (SME) promises to provide secure information flow control without modifying the behaviour of already secure programs, a property commonly referred to as transparency. Implementations of SME exist for the web in the form of the FlowFox browser and as plug-ins to several programming languages. Furthermore, SME can in theory work in a black-box manner, meaning that it can be programming language agnostic, making it perfect for securing legacy or third-party systems. As such SME, and its variants like Multiple Facets (MF) and Faceted Secure Multi-Execution (FSME), appear to be a family of panaceas for the security engineer. The question is, how come, given all these advantages, that these techniques are not ubiquitous in practice? The answer lies, partially, in the issue of runtime and memory overhead. SME and its variants are prohibitively expensive to deploy in many non-trivial situations. The natural question is why is this the case? On the surface, the reason is simple. The techniques in the SME family all rely on the idea of multi-execution, running all or parts of a program multiple times to achieve noninterference. Naturally, this causes some overhead. However, the predominant thinking in the IFC community has been that these overheads can be overcome. In this paper we argue that there are fundamental reasons to expect this not to be the case and prove two key theorems: (1) All transparent enforcement is polynomial time equivalent to multi-execution. (2) All black-box enforcement takes time exponential in the number of principals in the security lattice. Our methods also allow us to answer, in the affirmative, an open question about the possibility of secure and transparent enforcement of a security condition known as Termination Insensitive Noninterference.

Physical Unclonable Functions (PUFs) are a promising technology to secure low-cost devices. A PUF is a function whose values depend on the physical characteristics of the underlying hardware: the same PUF implemented on two identical integrated circuits will return different values. Thus, a PUF can be used as a unique fingerprint identifying one specific physical device among (apparently) identical copies that run the same firmware on the same hardware. PUFs, however, are tricky to implement, and a number of attacks have been reported in the literature, often due to wrong assumptions about the provided security guarantees and/or the attacker model. In this paper, we present the first mechanized symbolic model for PUFs that allows for precisely reasoning about their security with respect to a variegate set of attackers. We consider mutual authentication protocols based on different kinds of PUFs and model attackers that are able to access PUF values stored on servers, abuse the PUF APIs, model the PUF behavior and exploit error correction data to reproduce the PUF values. We prove security properties and we formally specify the capabilities required by the attacker to break them. Our analysis points out various subtleties, and allows for a systematic comparison between different PUF-based protocols. The mechanized models are easily extensible and can be automatically checked with the Tamarin prover.

Internet Service Providers (ISPs) have an economic and operational interest in detecting malicious network activity relating to their subscribers. However, it is unclear what kind of traffic data an ISP has available for cyber-security research, and under which legal conditions it can be used. This paper gives an overview of the challenges posed by legislation and of the data sources available to a European ISP. DNS and NetFlow logs are identified as relevant data sources and the state of the art in anonymization and fingerprinting techniques is discussed. Based on legislation, data availability and privacy considerations, a practically applicable anonymization policy is presented.

These days the digitization process is everywhere, spreading also across central governments and local authorities. It is hoped that, using open government data for scientific research purposes, the public good and social justice might be enhanced. Taking into account the European General Data Protection Regulation recently adopted, the big challenge in Portugal and other European countries, is how to provide the right balance between personal data privacy and data value for research. This work presents a sensitivity study of data anonymization procedure applied to a real open government data available from the Brazilian higher education evaluation system. The ARX k-anonymization algorithm, with and without generalization of some research value variables, was performed. The analysis of the amount of data / information lost and the risk of re-identification suggest that the anonymization process may lead to the under-representation of minorities and sociodemographic disadvantaged groups. It will enable scientists to improve the balance among risk, data usability, and contributions for the public good policies and practices.

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named Ausera, which leverages static program analysis techniques and sensitive keyword identification. By leveraging Ausera, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. We further find that weaknesses derived from outdated versions of banking apps or third-party libraries are highly prone to being exploited by attackers. To date, we highlight that 21 banks have confirmed the weaknesses we reported (including 126 weaknesses in total). We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.