6,881 research outputs found

    GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

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    Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively

    Mobile Device Background Sensors: Authentication vs Privacy

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    The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

    A reinforcement learning recommender system using bi-clustering and Markov Decision Process

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    Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

    Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)

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    Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis. 1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks. 2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s. 3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications. 4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats. 5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification. 6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations. The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total

    Forschungsbericht / Hochschule Mittweida

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    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    The role of the oral microbiome in the immunobullous diseases pemphigus vulgaris and mucous membrane pemphigoid and oral lichen planus

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    Saliva is formed from contributions of salivary glands and the serum exudates principally from gingival margins or damaged mucosa combined with components derived from the environment, including a community of microorganisms - the microbiome. I postulate that changes in microbial diversity and population structure play key roles in the modulation of host- microbial interactions which influence both the hypersensitive autoimmune responses and inflammation seen in these inflammatory mucocutaneous disorders. For my research, a total of 186 participants were recruited: 48 mucous membrane pemphigoid (MMP), 48 pemphigus vulgaris (PV), 50 oral lichen planus (OLP) patients, and 40 healthy controls. Unstimulated whole saliva, subgingival plaque, serum, and plasma samples were collected from 186 participants. In addition, metadata were collected on the following covariates: age, gender, ethnicity, type of the diet, disease history and therapeutic intervention in the preceding six months. Oral disease severity scores (ODSS) were assessed, and periodontal status was examined using a periodontal six pocket chart. To characterise microbiome profiles, saliva and subgingival plaque were processed for sequencing genomic DNA using the NGS Shotgun metagenomics sequencing technique. Inflammatory cytokines and proteases were investigated in saliva and serum using Human Magnetic Luminex Screening Assay (R&D Systems). Selected cytokines were analysed by enzyme-linked immunosorbent assay (ELISA) technique (R&D Systems) to determine host inflammatory responses in saliva and serum samples. Additionally, saliva and plasma samples were analysed for metabolites by nuclear magnetic resonance (NMR). Significant increases in periodontal score (PISA) in all three groups of disease were identified compared to healthy control group with significant positive correlation between oral disease severity (ODSS) and PISA in OLP and PV groups. All three groups of diseases had significantly higher levels of inflammatory Th2/Th17 cytokines (IL-6, IL-13 and IL-17 in saliva samples), as well as higher levels of MMP-3 matrixins in saliva. In addition, there were positive correlations between ODSS and salivary IL-6, IL-13 and MMP-3 in saliva of OLP, salivary and serum levels of IL-6 and MMP-3 in MMP group, and significant association of salivary IL-6, IL-1β and MMP-3 in PV group. Metabolomic data showed that saliva is a better biofluid for correlation of the metabolomic profile with oral disease severity than plasma. Salivary ethanol was corelated with disease severity in the OLP group, whereas in PV was a strong correlation of ODSS with choline. Finally, a unique microbial community was found in each group of diseases. In the MMP group, ODSS was significantly correlated with L. hofstadii, C. sputigena, N. meningitidis, N. cinerea and P. sacchar0lytica. In PV, a positive correlation was found with F. nucleatum, G. morbillorum, and E. corrodens, G. elegans, H. sapiens and T. vincentii. In OLP, the disease tends to worsen when there was reduced abundance of X. cellulosilytica, Actinomyces ICM 47, S. parasanguinis, S. salivarius, L. mirabilis and O. sinus. Lower microbial diversity was correlated with ODSS in saliva and plaque of the OLP group. In conclusion, this study provides strong evidence of the complex interplay between the oral microbiome, immunological factors, and metabolites in the context of immunobullous diseases and OLP. The findings highlight the integral role of oral bacteria in disease progression, the significance of immune dysregulation, and the potential impact of specific microbial species and metabolic pathways. These insights give the way for further research and clinical applications, offering the promise of personalized approaches for diagnosis, and management of OLP, MMP and PV. Future investigations should focus on discovering the mechanistic details underlying these associations and validating the identified biomarkers in larger patient cohorts, ultimately contributing to a deeper understanding of the pathogenesis of these conditions

    Surface-Enhanced Coherent Raman scattering (SE-CRS) with Noble Metal Nanoparticles

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    Early cancer detection remains challenging due to numerous complex tempo-spatial metabolic changes in cell physiology. Based on their ability to recognise molecular structures and pathological changes at molecular levels, spectroscopic have recently emerged as promising non-invasive, non-ionising, and cost-efficient tools to help detect cancer, and other human pathologies. Raman spectroscopy is a valuable technique that provides information regarding the chemical properties of materials. Nevertheless, it has limitations due to the limited amount of Raman light scattered. Strategies for cancer diagnostics and therapies are based on the hypothesis that nanoparticles (NPs) can be precisely tailored to target cancer cells. However, the tools required to image NPs at cellular levels remain scarce in the literature. The work outlined in this thesis, for the first time, utilises noble metal NPs and Raman reporters, with the mechanisms of surface enhanced Raman scattering (SERS) and coherent anti-Stokes Raman scattering (CARS), in cancer cells and tumour spheroids to address the demerits of low spatial resolution, signal-to-noise ratio, and chemical specificity. SERS and CARS have broadly been explored in this regard. To increase the effectiveness of Raman scattering, a variety of techniques have been devised to boost its intensity. Primarily, I studied four techniques to increase Raman scattering intensity with the ultimate objective of improving sensitivity and assessing limits of various Raman methods: SERS, surface-enhanced coherent anti-Stokes Raman scattering (SE-CARS), surface-enhanced stimulated Raman scattering (SE-SRS), and broadband coherent anti-Stokes Raman scattering (BCARS). Coherent Raman scattering (CRS) is utilised to enhance weak Raman bands. The signal is enhanced by nonlinear interaction of the excitation lasers within the sample. Despite the advantages offered over Raman, CRS has been relatively unexploited for image Raman tagged NPs. This challenge has recently been addressed using surface plasmon enhancement, which gives significantly enhanced inelastic scattering signals as well as reduced signal-to-noise ratio. Surface-enhanced coherent Raman scattering (SE-CRS) has been characterised by using a variety of techniques such as SERS, CARS, and SE-CARS. This work provides a step forward to develop plasmon enhanced SRS and CARS in addressing critical biological questions using nonlinear bio-photonics. In the first part of this thesis, I developed a reproducible substrate that mimics gold nanoparticles (AuNPs) and allows forward detection which is critical for CRS. I investigated the effects of annealing on gold films deposited on glass substrates with thicknesses from 3 nm to 15 nm as described in depth in chapter 5. In addition to this, it provides an explanation of the work that was performed to explore the interaction between Raman tags BPT (biphenyl-4-thiol), BPE trans-1,2-bis(4-pyridyl) ethylene, and IR 820 (new indocyanine green) on gold films substrates using 785 nm laser excitation. In the second part of this thesis, I investigated the interactions between Raman tags of BPT on gold films substrates using CRS and broadband CARS techniques. These experiments also offer the SE-CRS enhancement signal. The research done to examine gold thin film substrates and to offer SE-SRS and SE-CARS enhancement signals in the fingerprint region as described in chapter 6. Using CRS microscopy, the investigations in this chapter study these interactions. In the third part of this thesis, I developed a novel imaging methodology for the visualisation of AuNPs inside cellular structures and spheroids, with the intention of acquiring distinct spectroscopic fingerprints. Consequently, I undertook the task of devising protocols for visualising AuNPs and Raman reporter molecules within cancer cell models, spheroids, and animal tissues as described in chapter 7. The aim was to attain distinctive spectroscopic profiles by employing the SE-CRS technique, achieved by illuminating AuNPs along with Raman reporter molecules (BPT, BPE, IR 820) using low intensity infrared light, with both the pump and Stokes beams operating at intensities below 0.2 mW. In summary, this thesis sheds light on the development of surface plasmon resonance phenomena based on metallic nanostructures for use in nonlinear inelastic scattering systems, including surface-enhanced Raman scattering (SERS), coherent Raman scattering (CRS), and surface-enhanced coherent Raman scattering (SE- CRS). The primary focus is to use this system for disease diagnostics, rooted in SERS, reflects a commitment to advancing cancer diagnostics, based on SERS thereby enhancing the precision and discrimination of molecular signals, making a significant stride towards more effective and nuanced cancer diagnostics
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