Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Methods for Prevention and Detection of IP Address Spoofing

The IP address spoofing is a serious threat to the legitimate use of the Internet. Many Preventive mechanisms are thwarted by the ability of attackers to forge or spoof the source addresses in IP packets. Attackers can evade detection and put a substantial burden on the destination network for policing attack packets. Detection and prevention of IP address spoofing can be classified into host-based and router based methods. The host based methods are implemented on a host and aim to allow a host to recognize spoofed packets. The advantage of these methods is their easy deployment on the existing infrastructure without any need to change the networking infrastructure. On the other hand, they may act too late, since the spoofed packets must reach the host before they are detected. The router based methods are mostly implemented on routers and spoofed packets are recognized before they reach the destination. Router based methods are more efficient than host based methods, but the efficiency of the system depends on the participating routers. It is impossible for every router on the internet to participate in the system as different ISPs must deploy the same method for detection to work. In this paper a study of methods for prevention and detection of IP address spoofing is undertaken. It compares various host based methods such as IPsec, the OS Fingerprinting, TCP probing, SYN Cookies and IP puzzles with router based methods such as ingress and egress filtering, Reverse Path Forwarding (RPF), Router based Filtering (RBF), Spoofing Prevention Method (SPM), Passport, Distributed Packet Filtering (DPF), Inter Domain Packet Filtering (IDPF), Ex-IDPF, SAVE, BASE, Peer to Peer Anti Spoofing Method, Spoof Prevention Based Hierarchal Co-Ordination (SP-HCM), a Packet Marking with Bloom Filtering, Hop Count Filtering (HCF), Probabilistic Packet Marking (PPM), Pi and StackPi on the bases of their performances and effectiveness.
Lessons Learned from the Development of a Mechanical Ventilator for COVID-19

During the COVID-19 pandemic, many researchers all over the world have offered their time and competencies to face the heavy consequences of the disease. This is the case of a group of physicists, engineers, and physicians that around the middle of March 2020 started to develop a simplified mechanical lung ventilator, called MVM (Mechanical Ventilator Milano), to answer the high request of ventilators for Acute Respiratory Distress Syndrome (ARDS) in intensive care units. A prototype was ready in around one month. Since medical software malfunctions can lead to injuries or death of patients, before marketing MVM ventilators and distributing them in hospitals, software certification in accordance with the IEC 62304 standard was mandatory to guarantee system reliability. The team was then complemented by computer scientists specifically devoted to this task. The software re-engineering process, which lasted around two months from the end of the prototype, brought to a strong re-implementation of the device software components, which involved all the stakeholders in a continuous integration setting. In this paper, we report the experience of the MVM control SW re-engineering necessary to show evidence that the SW adheres to the standards and to consequently obtain the certification. We share results and lessons learned from this social project, where more than 100 volunteer researchers worked towards software certification at the extreme of their strength to get a real device finished in a rush since strongly required to support physicians in treating COVID-19 patients.
RHC: Cluster based Feature Reduction for Network Intrusion Detections

Intrusion Detection Systems (IDSs) play an important part in securing computer networks from different malicious threats and attacks. Modern IDSs leverage machine learning algorithms for effective intrusion detection. However, network traffic flows contain a large number of redundant features in a high-dimensional feature space which decreases the performance of such data-driven approaches drastically. Existing feature reduction methods lack to effectively remove redundant features as well as to retain features with additional information (if there is any). In this paper, we propose a Redundancy based Hierarchical Clustering (RHC) method that groups redundant features in the same cluster based on mutual information. We use both feature selection and extraction for finding the final feature set. We conduct rigorous experiments on three benchmark security datasets and our results demonstrate that the proposed methods outperform the state-of-the-art methods in terms of accuracy, f-score and false positive rate. We show the superiority of our proposed methods in both binary class (normal vs attack) and multi-class classification.
Unsupervised root-cause identification of software bugs in 5G RAN

Developers of complex system like 5G Radio Access Networks (RAN) need algorithms that can automatically locate the root causes of software bugs. Existing methods mainly use supervised learning to track down root causes and only a few of these provide enough information to identify the function in which a software bug occurs. Supervised learning methods work well when scenarios can be repeated, and the normal behavior is somewhat similar. In RAN, where thousands of different configurations are used, software is updated frequently, and each node has its own traffic intensity, using unsupervised learning that does not require any pre-training can be more suitable. The few existing methods that use unsupervised learning to locate the root cause of software bugs can only detect delays or software hangs, and are not able to identify the many types of bugs that occur in RAN. We propose a multi-step method that uses unsupervised learning to analyze kernel and user space traces in system logs. The methods can guide developers by suggesting top-k candidate functions that are likely to contain a software bug. Our methods, MultiSpace and CallGraph were evaluated using an advanced 5G testbed in which many different software bugs that are common in RAN, were injected. The results shows that MultiSpace and CallGraph, can detect a wider range of software bugs than previous methods and only adds an average CPU load of 1.3% on the testbed. An important aspect is also that our methods scale well with large amount of data produced by real time systems, like RAN, and can analyze the data much faster.
Creating a Dataset and Models Based on Convolutional Neural Networks to Improve Fruit Classification

We are often curious to find out what fruit is found in a tree when we are walking through parks or a botanical garden. Fruit classification is a complex activity that depends on the datasets used and the deep learning methods applied to them. This paper aims to present the steps by which we created a dataset with 262 fruits, and then how we created several models based on it. Evaluation and testing have shown us that the resource and models created can be useful to those who want to create applications that recognize fruits.
HawkEye: User-Guided Enumeration of Scenarios

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built, to automated testing and debugging of their implementations after they are built. Alloy is a declarative modeling language that is well suited for verifying object-oriented designs. A key strength of Alloy is its scenario-finding toolset the Analyzer, which outputs all valid scenarios that adhere to the model's constraints up to a user-provided scope. However, in order for scenario-finding toolsets to be useful and not an undue burden, scenario-finding toolsets need to generate a relatively small but valuable collection of scenarios. This paper outlines Hawkeye, a novel interactive enumeration technique for the Analyzer that empowers the user to select which elements of a scenario the user wants to keep the same or differ in the next enumeration. Experimental results show that our technique can modify scenario enumeration without significant overhead on the size and complexity of the underlying SAT problem. Moreover, we highlight Hawkeye's ability to help users explore faulty models. Hawkeye is available at: https://github.com/alloy-hawkeye/Hawkeye.git
Towards Security Decision Support for large-scale Heterogeneous Distributed Information Systems

The paper considers the challenge of security decision support for automated intrusion prevention within large-scale heterogeneous distributed information systems. The authors outline the main types of modern information systems, their features, and interconnections. They analyse existing research and solutions in the area of security decision support for the different types of large-scale heterogeneous distributed information systems, their functionality, used models, methods and metrics, advantages and disadvantages, and compare them. Finally, the authors outline the main challenges and tasks in the area and propose a common approach for security decision support. In the future work the authors plan to detail and implement the proposed approach.
Ingestible Wireless Capsule Sensor Made from Edible Materials for Gut Bacteria Monitoring

This report describes an ingestible capsule sensor that can wirelessly detect the activity of gut bacteria by irradiating microwaves from outside the body. The proposed sensor is composed of only edible materials: the critical sensing material is a dietary fiber, guar gum, that is decomposed by gut bacteria. We confirmed that the guar gum film can be decomposed by gut bacteria, bifidobacteria. For wireless communication, we fabricated an ingestible antenna made from rice paper and gold with a split-ring resonator (SRR), one of the elements of electromagnetic metamaterials. Our sensor fulfills swallowable size and wireless communication requirements for the first time and would be a safety sensor for gastrointestinal (GI) tract monitoring. We believe that our ingestible sensor could be an effective tool for non-invasive biological substances monitoring without retention risk.
Challenges and Solutions for Antennas in Vehicle-to-Everything Services

Autonomous vehicle is being developed for widespread deployment. Its reliability and safety are critically dependent on advanced wireless technologies, e.g., vehicle-to-everything (V2X) communication. The frontend of a V2X system needs an antenna module that enables the vehicle to reliably connect to all other networks. Designing V2X antenna is challenging due to the complex in-vehicle environment, trend for hidden antenna solution, long simulation time and need for omnidirectional coverage. In this article, we survey these challenges as well as existing V2X antenna solutions. In view of the drawbacks in the existing solutions, we propose an efficient design methodology for V2X antennas to provide the desired coverage. The method utilizes a simple geometrical model of the vehicle that captures the shadowing effects of the vehicle body to obtain candidate antenna locations that offer the best coverage via multi-antenna diversity. Hence, complex full-wave simulation can be avoided. The approach is validated through comprehensive full-wave simulations and pattern measurements on two car models. The results confirm that, at 5.9GHz, line-of-sight shadowing has more dominant effect on the received power than multipath propagation due to the car body. In cases of strong diffraction and surface waves, a simple rule-of-thumb can be devised to improve the accuracy of the method.
Analysis of the Performance of a Cloud Computing Processing Queue with Correlated Reneging of Tasks and Resubmission

The Quality of Service (QoS) experienced by the packets that constitute a cloud computing task does not only depend on the performance of the cloud computing network infrastructure at the data centre but also on the performance of the access networks and the internet core networks traversed by the packets before the task is fully submitted to the cloud computing or processing servers for execution or processing. Sometimes, some of the tasks that are submitted and are waiting in queues to be processed could be removed from the queues without being processed (task reneging or dropping). Removing tasks from the queue could result from the user's impatience, missing execution deadline, security reasons, or an active queue management strategy. The reneged or dropped tasks could be resubmitted depending on the reason for which it was dropped. The resubmission of tasks decreases QoS and increases the energy consumption overhead as more energy is required to transport the resubmitted tasks through the access, core, and data centre networks. In this paper, we propose a multiserver queueing model with correlated reneging and resubmission to analyse the performance of a cloud computing server.
Retaining High Q Factors in Electrode-Less Aln-On-Si Bulk Mode Resonators with Non-Contact Electrical Drive

This paper reports on an experimental study of actuation techniques of aluminum nitride on silicon (AlN/Si) heterostructure resonant microelectromechanical systems (MEMS) with both bulk and flexural modes, and comparison of their quality factors (Qs) and energy losses induced by different resonant motion excitation schemes. For AlN/Si MEMS resonators without deposited top metal electrodes, we have devised a new scheme to electrically drive the device, enabled by a non-contact overhanging electrode inducing gradient forces, to replace the otherwise required optical excitation for electrode-less devices. For the bulk mode resonance of AlN/Si resonator at 10MHz, almost equal Q of ~26,000 is obtained for both the new electrical drive and the optical drive, whereas the electrical drive enables the detection of a ~1MHz mode that is not visible when driven optically. For the Si-only device, Q of 257,300 is attained with the non-contact electrical drive, noticeably higher than the Q of 212,225 with the optical drive, suggesting that non-contact electrical excitation results in lower energy dissipation than optical actuation. The present work motivates further studies of transduction mechanisms to achieve higher resonator Qs.
CentAir: Centrality Based Cross Layer Routing for Software-Defined Aerial Networks

Application domain of Unmanned Aerial Vehicles(UAVs) expands every day. Multiple of these devices can simply be put together to form versatile and powerful Mobile ad hoc Networks (MANETs). Unfortunately, the existing protocols suffer to maintain efficient routes and channel assignments in such dynamic environments, where network topology and channel characteristic are prone to constant changes. In this paper, we propose a cross-layer protocol, utilizing well-known path finding algorithm, D* Lite, to achieve joint optimization of routing and dynamic channel assignment in software-defined aerial networks. Our protocol, CentAir, first uses graph centrality measures to adjust transmit power of the central nodes to both reduce interference rate and congestion around the central nodes. Then, it determines which channel to utilize along the path while taking the estimated collision probabilities into account. Our extensive simulation results show that CentAir provides 132% better channel capacity with the cost of 3% longer paths on e2e routes compared to traditional shortest path routing algorithms with random channel assignments.
Comparative Study of Malware Detection Techniques for Industrial Control Systems

Industrial Control Systems are essential to managing national critical infrastructure, yet the security of these systems historically relies on isolation. The adoption of modern software solutions, and the unique challenges presented by legacy systems, has made securing industrial networks increasingly difficult. With malware identified as the leading cause of cyber incident in industrial systems, this work presents a comparative study of existing malware detection techniques, to compare both accuracy and suitability for use in the defence of industrial systems.
Quadratic Programming based Energy Management in a Multi-Stack Fuel Cell Hybrid Electric Vehicle

This paper proposes a two-layer online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (MFC-HEV). In the first layer, an online estimator is responsible for constantly predicting maximum efficiency and maximum power of each FC. The second layer is in charge of power distribution where it combines quadratic programming (QP) with a rule-based strategy to minimize hydrogen consumption and power sources degradation while maintaining a similar degradation rate among FCs by changing their order of operation. The results of the suggested EMS have been compared with an adaptive daisy chain in this paper.
Differential effects of prediction error and adaptation along the auditory cortical hierarchy during deviance processing

Neural mismatch responses have been proposed to rely on different mechanisms, including prediction error-related activity and adaptation to frequent stimuli. However, the cortical hierarchical structure of these mechanisms is unknown. To investigate this question, we used functional magnetic resonance imaging (fMRI) and an auditory oddball design with a suited control condition that enabled us to delineate the contributions of prediction error- or adaptation-related brain activation during deviance processing. We found that while prediction-error related processes increased with the hierarchical position of the brain area, adaptation declined. This suggests that the relative contribution of different mechanisms in deviance processing varies across the cortical hierarchy.
An Architecture for Autonomous Control of a Robotic Satellite Grappling Mission

The Front-End Robotics Enabling Near-Term Demonstration (FREND) program requires the autonomous grapple of a simulated geostationary satellite. To achieve the required autonomy, while maintaining repeatable, deterministic performance and the capability for ground control of the system, the Naval Research Laboratory has developed a finite state machine-based Mission Sequencer. The Mission Sequencer offers increased system self-awareness and coordinated fault response without the development of extensive databases requiring expert knowledge. Simple in conception and implementation, the Mission Sequencer has nonetheless been a critical component in achieving reliable system autonomy.
The Prevalence and Molecular Biology of Staphylococcus aureus Isolated from Healthy and Diseased Equine Eyes in Egypt

This work aimed to characterize S. aureus isolates from the eyes of healthy and clinically affected equines in the Kafrelsheikh Governorate, Egypt. A total of 110 animals were examined for the presence of S. aureus, which was isolated from 33 animals with ophthalmic lesions and 77 healthy animals. We also investigated the antimicrobial resistance profile, oxacillin resistance mechanism, and the major virulence factors implicated in many studies of the ocular pathology of pathogenic S. aureus. The association between S. aureus eye infections and potential risk factors was also investigated. The frequency of S. aureus isolates from clinically affected equine eyes was significantly higher than in clinically healthy equids. A significant association was found between the frequency of S. aureus isolation from clinically affected equine eyes and risk factors including age and season but not with sex or breed factors. Antimicrobial resistance to common antibiotics used to treat equine eyes was also tested. Overall, the isolates showed the highest sensitivity to sulfamethoxazole (100%) and the highest resistance to cephalosporin (90.67%) and oxacillin (90.48%). PCR was used to demonstrate that mecA was present in 100% of oxacillin- and β-lactam-resistant S. aureus strains. The virulence factor genes Spa (x region), nuc, and hlg were identified in 62.5%, 100%, and 56%, of isolates, respectively, from clinically affected equines eyes. The severity of the eye lesions increased in the presence of γ-toxin-positive S. aureus. The phylogenetic tree of the Spa (x region) gene indicated a relationship with human reference strains isolated from Egypt as well as isolates from equines in Iran and Japan. This study provides insight into the prevalence, potential risk factors, clinical pictures, zoonotic potential, antimicrobial resistance, and β-lactam resistance mechanism of S. aureus strains that cause eye infection in equines from Egypt.
Searching for Low Probability Opening Events in a DNA Sliding Clamp

The β subunit of E. coli DNA polymererase III is a DNA sliding clamp associated with increasing the processivity of DNA synthesis. In its free form, it is a circular homodimer structure that can accomodate double-stranded DNA in a nonspecific manner. An open state of the clamp must be accessible before loading the DNA. The opening mechanism is still a matter of debate, as is the effect of bound DNA on opening/closing kinetics. We use a combination of atomistic, coarse-grained, and enhanced sampling strategies in both explicit and implicit solvents to identify opening events in the sliding clamp. Such simulations of large nucleic acid and their complexes are becoming available and are being driven by improvements in force fields and the creation of faster computers. Different models support alternative opening mechanisms, either through an in-plane or out-of-plane opening event. We further note some of the current limitations, despite advances, in modeling these highly charged systems with implicit solvent.
Speech recognition based on front-end noise removal algorithms

One of the biggest obstacles that hinder the widespread use of automatic speech recognition technology is the inability to handle noise, which includes environmental noise, channel distortion and speaker variability, etc. Towards this end, we propose several feature compensation approach to improve the robustness of automatic speech recognition (ASR) systems. The human auditory system can work properly in very adverse environments, e.g. in a crowded shopping mall where thousands of people are talking loudly together with the background commercial broadcast. Therefore, analyzing and modeling human auditory system is a straightforward and logical approach to improve the performance of ASR systems. The first part of this thesis focuses on the study of masking effects, which describes how a clearly audible sound (maskee) becomes less audible because of the presence of another sound (masker). Masking effects can be classified as temporal masking and frequency masking (a.k.a. simultaneous masking). Temporal masking describes the situation where the masker (the stronger sound that diminishes others) and maskee (the weaker sound that is made less audible) possess the same frequency but different commencing time, while frequency masking describes the situation where masker and maskee possess the same commencing time but different frequency. Chapter 3 introduces a novel Mel-Frequency Cepstral Coefficients (MFCC) based algorithm which simulates the properties of the human auditory system. It sequentially implements temporal masking and frequency masking in the time domain and frequency domain, respectively. Temporal spectral averaging (TSA) and cepstral mean & variance normalization (CMVN) are used for post processing. Experimental results show that our proposed algorithm ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library