Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Temperature and Shear on the Adsorption of Surfactants on Carbon Nanotubes

Carbon nanotube suspensions in water can be stabilized with the use of surfactants. However, when a suspension is exposed to harsh conditions, the surfactants might detach from the carbon nanotube (CNT) surface leading to CNT agglomeration and precipitation. In order to explore temperature effects on the suspension, the surfactant adsorption isotherms of an ionic surfactant (alkylpropoxy sulfate, alfoterra 123-8s) and a nonionic surfactant (secondary alcohol ethoxylate, tergitol 15-s-40) on a CNT are obtained at various temperatures using dissipative particle dynamics (DPD) simulations. The effects of shear stresses, such as those experienced by the CNT–surfactant suspension when it passes through pumps, are also explored by applying different levels of constant shear on the suspension. It is seen that there is good agreement between the DPD results and the Langmuir model for the adsorption of both alfoterra and tergitol on CNTs. The detachment of the surfactant from the nanotubes occurs with the detachme.
Digital twins as run-time predictive models for the resilience of cyber-physical systems: a conceptual framework

Digital twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, in this paper, we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that end, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argue that the convergence between DT and self-adaptation is the key to building smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and—ultimately—self-heal. The conceptual framework eases dependability assessment, which is essential for the certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) Nymph Survival and Adult Feeding Preferences for Crop Plants in Florida

Summary Florida crop plants, including okra, sunflower, tangerine, olive, plum, pomegranate, satsuma, pear, and apple, plus amaranthus were evaluated as hosts for Halyomorpha halys (Stål) (Hemiptera: Pentatomidae). This invasive stink bug fed on all plant species, except olive and pomegranate, but completed development only on apple, pear, and satsuma. However, a significant number of nymphs developed into fifth instars on okra, tangerine, and plum. Halyomorpha halys, therefore, has the potential to complete development on apple, pear, satsuma, plum, okra, and tangerine crops in Florida.
Getting to Production with Few-shot Natural Language Generation Models

In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems. We introduce a system consisting of iterative self-training and an extensible mini-template framework that textualizes the structured input data into semi-natural text to fully take advantage of pre-trained language models. We compare var-ious representations of NLG models’ input and output and show that transforming the input and output to be similar to what the language model has seen before during pre-training improves the model’s few-shot performance substantially. We show that neural mod-els can be trained with as few as 300 annotated examples while providing high fidelity, considerably lowering the resource requirements for standing up a new domain or language.This level of data efficiency removes the need for crowd-sourced data collection resulting in higher quality data annotated by expert linguists. In addition, model maintenance and debugging processes will improve in this few-shot setting. Finally, we explore distillation and using a caching system to satisfy latency requirements of real-world systems.
DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval

Automated Frequently Asked Question (FAQ) retrieval provides an effective procedure to provide prompt responses to natural language based queries, providing an efficient platform for large-scale service-providing companies for presenting readily available information pertaining to customers’ questions. We propose DTAFA, a novel multi-lingual FAQ retrieval system that aims at improving the top-1 retrieval accuracy with the least number of parameters. We propose two decoupled deep learning architectures trained for (i) candidate generation via text classification for a user question, and (ii) learning fine-grained semantic similarity between user questions and the FAQ repository for candidate refinement. We validate our system using real-life enterprise data as well as open source dataset. Empirically we show that DTAFA achieves better accuracy compared to existing state-of-the-art while requiring nearly 30× lesser number of training parameters.
Accurate and Robust Malware Analysis through Similarity of External Calls Dependency Graphs (ECDG)

Malware is a primary concern in cybersecurity, being one of the attacker’s favorite cyberweapons. Over time, malware evolves not only in complexity but also in diversity and quantity. Malware analysis automation is thus crucial. In this paper we present ECDGs, a shorter call graph representation, and a new similarity function that is accurate and robust. Toward this goal, we revisit some principles of malware analysis research to define basic primitives and an evaluation paradigm addressed for the setup of more reliable experiments. Our benchmark shows that our similarity function is very efficient in practice, achieving speedup rates of 3.30x and 354,11x wrt. radiff2 for the standard and the cache-enhanced implementations, respectively. Our evaluations generate clusters that produce almost unerring results - homogeneity score of 0.983 for the accuracy phase - and marginal information loss for a highly polluted dataset - NMI score of 0.974 between initial and final clusters of the robustness phase. Overall, ECDGs and our similarity function enable autonomous frameworks for malware search and clustering that can assist human-based analysis or improve classification models for malware analysis.
Participation of nanocrystalline TiO 2 surface in the electron transfer between semiconductor solid and adsorbed cobalt(III)-Rpy complex

Cis-[Co III (tn) 2 ( R py)Br]Br 2 , ( R = 4 -CN, H, 4 -Bz, 4 -Me, 4 -Et, and 4 -MeNH), in aqueous 2-propanol exhibit varying adsorption characteristics and led to surface compound formation. UV ( λ = 254) excitation of the nano-TiO 2 //cobalt(III)-( R py) surface compound resulted in interfacial electron transfer (IFET) reaction. The IFET has been found to be dependent upon the coordination environment of the complex, more precisely due to the R py ligand. In addition, the proposed mechanism of the IFET reaction includes the formation of a Co II ion implanted in nanocrystalline TiO 2 . This photoreduction was found to be solvent controlled. The photoefficiency of the Co II aq formation was spectrally analyzed simultaneously as Co II :TiO 2 was isolated from the photolyte solution. The isolated solid was subjected to FTIR, DRS, PXRD, and SEM-EDX instrumental analysis. It is concluded that the removal of metal ion in the form of a complex is coordination structure dependent, hence, seems more specific in removal efficiency and in doping the anatase lattice.
VTDroid: Value-based Tracking for Overcoming Anti-Taint-Analysis Techniques in Android Apps

Bytecode-level taint tracking discovers suspicious apps on the Android platform; however, malicious apps can bypass it by transferring information via system layers in the Android. A context tainting countermeasure has been devised, but since it employs a list of flow-causing API methods, it will miss flows when unlisted methods are exploited and can also produce false positives. This paper presents a new taint-tracking technique operating value logging and matching based on the flows’ characteristics to detect such flows without relying on lists of API methods. We implemented it into our taint-tracking system called VTDroid and confirmed its effectiveness with our test suite. We also evaluated it with popular apps collected from Google Play. The results show that the precision of VTDroid is 37 points higher than the context tainting.
SoK: Automatic Deobfuscation of Virtualization-protected Applications

Malware authors often rely on code obfuscation to hide the malicious functionality of their software, making detection and analysis more difficult. One of the most advanced techniques for binary obfuscation is virtualization-based obfuscation, which converts the functionality of a program into the bytecode of a randomly generated virtual machine which is embedded into the protected program. To enable the automatic detection and analysis of protected malware, new deobfuscation techniques against virtualization-based obfuscation are constantly being developed and proposed in the literature. In this work, we systematize existing knowledge of automatic deobfuscation of virtualization-protected programs in a novel classification scheme and evaluate where we stand in the arms race between malware authors and code analysts in regards to virtualization-based obfuscation. In addition to a theoretical discussion of different types of deobfuscation methodologies, we present an in-depth practical evaluation that compares state-of-the-art virtualization-based obfuscators with currently available deobfuscation tools. The results clearly indicate the possibility of automatic deobfuscation of virtualization-based obfuscation in specific scenarios. Furthermore, however, the results highlight limitations of existing deobfuscation methods. Multiple challenges still lie ahead on the way towards reliable and resilient automatic deobfuscation of virtualization-based obfuscation.
Label Consistent Flexible Matrix Factorization Hashing for Efficient Cross-modal Retrieval

Hashing methods have sparked a great revolution on large-scale cross-media search due to its effectiveness and efficiency. Most existing approaches learn unified hash representation in a common Hamming space to represent all multimodal data. However, the unified hash codes may not characterize the cross-modal data discriminatively, because the data may vary greatly due to its different dimensionalities, physical properties, and statistical information. In addition, most existing supervised cross-modal algorithms preserve the similarity relationship by constructing an n×n pairwise similarity matrix, which requires a large amount of calculation and loses the category information. To mitigate these issues, a novel cross-media hashing approach is proposed in this article, dubbed label flexible matrix factorization hashing (LFMH). Specifically, LFMH jointly learns the modality-specific latent subspace with similar semantic by the flexible matrix factorization. In addition, LFMH guides the hash learning by utilizing the semantic labels directly instead of the large n×n pairwise similarity matrix. LFMH transforms the heterogeneous data into modality-specific latent semantic representation. Therefore, we can obtain the hash codes by quantifying the representations, and the learned hash codes are consistent with the supervised labels of multimodal data. Then, we can obtain the similar binary codes of the corresponding modality, and the binary codes can characterize such samples flexibly. Accordingly, the derived hash codes have more discriminative power for single-modal and cross-modal retrieval tasks. Extensive experiments on eight different databases demonstrate that our model outperforms some competitive approaches.
Network Intrusion Detection in the Wild - the Orange use case in the SIMARGL project

There is a profuse abundance of network security incidents around the world every day. Increasingly, services and data stored on servers fall victim to sophisticated techniques that cause all sorts of damage. Hackers invent new ways to bypass security measures and modify the existing viruses in order to deceive defense systems. Therefore, in response to these illegal procedures, new ways to defend against them are being developed. In this paper, a method for anomaly detection based on machine learning technique is presented and a near real-time processing system architecture is proposed. The main contribution is a test-run of ML algorithms on real-world data coming from a world-class telecom operator. This work investigates the effectiveness of detecting malicious behaviour in network packets using several machine learning techniques. The results achieved are expressed with a set of metrics. For better clarity on the classifier performance, 10-fold cross-validation was used.
An exploratory analysis on the impact of Shodan scanning tool on the network attacks

Network flexibility, openness and systems integration has brought in the last years many advantages in the society in terms of communication and information sharing. Beside that, new issues are emerging related to vulnerabilities in the Internet, which can affect not only virtual environments in an isolated way but this can have serious repercussions in the real world. That is why, the identification of new system vulnerabilities represents an important information for malicious parties. Currently, several tools, known as Online Public Scanning Tools (OPSTs) represent for attackers an attractive source of information from which to draw in order to plan and launch attacks. Indeed, they can automatically scan services, platforms and IoT devices connected to the Internet in order to retrieve information related to them, by including those related to vulnerabilities. In this context, this work aims to investigate how such OPSTs impact the launch of attacks on the network. To this purpose, a model centered on 3 main actors, (i.e. the attack, the hacker and the OPST) has been proposed by defining a set of features which aims to support the evaluation. Shodan was chosen as the OPST, as it is the most popular based on the related review works, while a honey-based approach was adopted to support monitoring and information extraction related to attacks. The results of these analyzes, which show how Shodan influence the attackers in carrying out network attacks are presented and discussed.
Reliable Data Transmission using Low Power Wide Area Networks (LPWAN) for Agricultural Applications

Reliable IT-based communication in agriculture is becoming increasingly important for regular operations. For example, if a farmer is in the field during a network outage, such as a failure of the mobile network, an alternative communication channel is needed to continue to connect to IT components and required data. With increasing digitalization, Low Power Wide Area Network (LPWAN) technologies are being used more and more frequently, e.g. for sensor networks. The LPWAN technologies offer a high range and can be used autonomously for the most part, but do not allow classic TCP/IP communication. In this work, a popular LPWAN technology, namely LoRaWAN, is experimentally supplemented by AX.25 on OSI layer 2 (Data Link Layer) to allow end devices TCP/IP-based communication over long distances. The evaluation shows that classic low-bandwidth applications are thus functional and can enable reliable, crisis-capable data transmission.
A Medium-Interaction Emulation and Monitoring System for Operational Technology

We present LOGistICS, a monitoring-framework with the aim to study the security of industrial PLC systems. The architecture encompasses different processing components and probes, with different tasks. In particular, this paper focuses on the description of a new medium-interaction honeypot attracting Modbus and S7comm traffic. With respect to related open-projects (e.g. Conpot), our proposal is highly extensible, configurable, and it allows for interacting more with an attacker while remaining less detectable. With LOGistICS the main objective is to study the behaviour of hosts that are interested in attacking industrial services.
Shock wave: a graph layout algorithm for text analyzing

The EVOQ tool offers researchers in social sciences a set of text analysis tools relying on the post-structuralist approach. This analysis approach relies on the identification of association and opposition relations between terms (words or expressions). The so-defined graph is presented in EVOQ by a node-link diagram. The Shock Wave is a placement algorithm specifically designed to be combined with a classical force-directed algorithm to produce a graph layout which meets the interpretability needs of the text analysts while preserving efficiency on large numbers of nodes. It structures the nodes on a circular placement with transversal opposition relations to highlight oppositions within the text concepts. Beyond our use case, the interest of Shock Wave lies in the fact that it is a novel method to present graphs of text with a strong emphasis on underlying semantic fields.
Hunting Shadows: Towards Packet Runtime-based Detection Of Computational Intensive Reversible Covert Channels

The appearance of novel ideas for network covert channels leads to an urge for developing new detection approaches. One of these new ideas are reversible network covert channels that are able to restore the original overt information without leaving any direct evidence of their appearance. Some of these reversible covert channels are based upon computational intensive operations, like for example encoding hidden information in the authentication hashes of a hash chain based one-time password. For such a covert channel implementation, the hash function has to be called repeatedly to extract the hidden message and to restore the original information. In this paper, we investigate the influence of repeated MD5 and SHA3 hash operations on the runtime of an authentication request-response. We first define two alphabets, one which leads to the fewest hash operations and one which leads to the most hash operations to be performed. Further, for each alphabet, we carry out three experiments. One without a covert channel, one with a covert channel altering all hashes, and finally, one with a covert channel altering every second hash. We further investigate the detection rates of computational intensive reversible covert channels for all scenarios by applying a threshold-based detection upon the average packet runtime without encoded covert information. Finally, we describe countermeasures and the limitations of this detection approach.
Detection of Brute-Force Attacks in End-to-End Encrypted Network Traffic

Network intrusion detection systems (NIDSs) can detect attacks in network traffic. However, the increasing ratio of encrypted connections on the Internet restricts their ability to observe such attacks. This paper proposes a completely passive method that allows to detect brute-force attacks in encrypted traffic without the need to decrypt it. For that, we propose five novel metrics for attack detection which quantify metadata like packet size or packet timing. We evaluate the performance of our method with synthetically generated but realistic traffic as well as on real-world traffic from a Tor exit node on the Internet. Our results indicate that the proposed metrics can reliably detect brute-force attacks in encrypted traffic in protocols like HTTPS, FTPS, IMAPS, SMTPS, and SSH. Simultaneously, our approach causes only a few false positives, achieving an F-measure between 75% and 100%.
Secret Sharing-based Authenticated Key Agreement Protocol

In this article, we present two novel authenticated key agreement (AKA) schemes that are easily implementable and efficient even on constrained devices. Both schemes are constructed over elliptic curves and extend Schonorr’s signature of knowledge protocol. To the best of our knowledge, we introduce a first AKA protocol based on the proof of knowledge concept. This concept allows a client to prove its identity to a server via secret information while the server can learn nothing about the secret. Furthermore, we extend our protocol via secret sharing to support client multi-device authentication and multi-factor authentication features. In particular, the secret of the client can be distributed among the client’s devices. The experimental analysis shows that our secret sharing AKA (SSAKA) can establish a secure communication channel in less than 600 ms for one secondary device and 128-bit security strength. The protocol is fast even on very constrained secondary devices, where in most of cases takes less than 500 ms. Note that the time consumption depends on the computational capabilities of the hardware.
A Serious Game Design Framework for Software Developers to Put GDPR into Practice

The growth of the internet has significantly increased data breaches (i.e. privacy breaches) in software systems. It could be argued that software developers failed to implement privacy into software systems with the appropriate privacy guidelines or laws such as the General Data Protection Regulation (GDPR). GDPR has a set of guidelines that enables software developers to implement privacy into software systems. Nevertheless, these guidelines have been developed with lawyers in mind, rather than software developers. This could hinder developers from putting GDPR into practice and eventually lead to data breaches through the systems they develop. On the other hand, software developers also need help (e.g. tooling support or educational interventions). Therefore, this paper proposes a game design framework, as an educational intervention, to teach software developers to implement privacy-preserving software systems taking GDPR on-board. The proposed framework focuses on improving developers’ security coding behavior through their motivation. It also ensures software developers can put GDPR into practice when developing privacy-preserving software systems.