Modern 5G networks offer a network-sliced infrastructure where each network slice contains a dedicated 5G core software service layer. The 5G core software services in each slice shares common core network resources to meet specific customer needs. A primary challenge in 5G network slicing involves resource sharing and efficient network slice orchestration. Container-based methodologies, including tools like Docker and Kubernetes, have become popular for orchestrating 5G network slice services and managing configurations in microservices-based cloud-native service deployment. However, despite their utility, these tools present significant challenges. Their complexity often necessitates dedicated DevOps teams for effective management, while configuration management can prove arduous, and end-to-end supply chain oversight is lacking. To address these challenges, this paper introduces “Llama-Recipe,” a cloud-native 5G-core service deployment and orchestration platform integrating Generative AI, SBOM, PBOM and NFT. 5G-core service configurations across different network slices are represented as “HOCON (Human-Optimized Config Object Notation)” config objects adhering to the GitOps paradigm. Leveraging custom-trained Meta's Llama2 LLM, Llama-Recipe generates the Kubernetes manifests for network-sliced 5G-core services based on the defined HOCON configurations. The generated Kubernetes manifests of the 5G-core services are deployed in designated Kubernetes clusters utilizing GitOps tools (e.g., ArgoCD), ensuring seamless and automated deployment processes. Additionally, Llama-Recipe introduced a novel mechanism to handle end-to-end supply chain verification of 5G-core software services using Software-Bill of Materials (SBOM) and Pipeline-Bill of Materials (PBOM). SBOMs track all the dependencies and PBOMs facilitate the comprehensive tracking of end-to-end supply chain data for 5G-core software services, enhancing transparency and security. These PBOMs are also generated using the fine-tuned Meta's Llama-2 LLM and are encoded as NFT tokens with a novel NFT token schema. This schema enables easy verification and validation of supply-chain data during deployments, thus helping to prevent various supply-chain attacks. To fine-tune the Meta's Llama2 LLM, we've undertaken a meticulous training process, collaborating with Qlora to transform a 4-bit quantized pre-trained language model into Low-Rank Adapters(LoRA). The effectiveness of the Llama-Recipe is demonstrated through a real-world test-bed deployment in a sliced network scenario, utilizing multiple 5G cores (i.e., Open5GS) across Ericsson's new Radio Access Network (RAN).

Current wind energy data platforms face significant challenges in securing and managing extensive data from both offshore and onshore wind farms. These challenges include vulnerabilities to cyber-attacks, data tampering, breaches, complex data-sharing issues due to privacy concerns and regulatory compliance, and a lack of scalability and flexibility in analytical tools for real-time data processing. This paper proposes a novel multilayered data security architecture, termed "VindSec-Llama," to address these challenges. It integrates Generative AI, blockchain, federated learning, and Pipeline Bill of Materials (PBOM) to enhance data analytics, model development, and security across several layers, including Infrastructure, Data Lake, Federated Learning, MLOps, Data Provenance, and LLM. Each layer is designed to meet specific functional requirements, such as handling large datasets, facilitating secure federated learning, automating risk management, and ensuring data provenance and traceability. The platform, deployable in server environments (cloud or on-premises), complies with the Risk Management Framework (RMF) guidelines and security standards. It features a blockchain-enabled, coordinator-less federated learning system to enhance data privacy and security by enabling the development of privacy-preserving machine learning models with data from different wind farms. Automation plays a pivotal role throughout VindSec-Llama, with Meta’s custom-trained Llama-3 LLM used for generating remediation scripts in the Infrastructure Layer and for producing PPBOM in the MLOps Layer. The Llama-3 LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The MLOps pipeline setup, a critical functionality of VindSec-Llama, ensures seamless integration and deployment of machine learning models, embodying best practices in continuous integration and delivery. This setup is geared towards maximizing security, compliance, and operational efficiency. A prototype of the platform has been implemented within a wind-energy testbed with the collaboration of Department of Energy US, illustrating its practical applications and benefits.

Large Language Models (LLMs) are widely recognized for their applications in natural language processing tasks, but their potential extends far beyond traditional use cases. This paper introduces "Bassa-Llama," a novel platform that harnesses LLMs for predictive tasks in the realm of network security. Specifically, we propose a platform for real-time network attack detection in Wind Power Plants, leveraging a fine-tuned version of Meta’s Llama-3 LLM alongside blockchain and NFT-based data storage. Using a network PCAP dataset containing both malicious and benign packets, we fine-tune the Llama-3 LLM, with Quantized Low-Rank Adapter (QLoRA), to detect anomalies in network traffic. This approach ensures optimal performance on consumer-grade hardware while significantly enhancing the model’s ability to accurately analyze PCAP data and identify attack patterns. The end-to-end orchestration of the real-time network attack detection flow for Wind Power Plants is fully automated through blockchain smart contracts, and NFTs for storing identified attack data from the PCAP. To the best of our knowledge, this research represents the first effort to utilize a fine-tuned LLM for real-time network attack detection tasks. The results highlight the transformative potential of combining fine-tuned LLMs with blockchain and NFTs to build robust and secure network defense systems for Wind Power Plants. A prototype of the proposed platform was developed in collaboration with the U.S. Department of Energy, utilizing a simulated Wind Power Plant as a testbed.

The main challenges in the 5G/6G network slicing are resource sharing, network slice orchestration, and network optimization in the 5G ecosystem. This paper proposes a novel architecture for a dynamic network slice broker and marketplace named “SliceGPT” that leverages Custom-Trained OpenAI GPT-3.5 LLM, blockchain and NFTs to enable collaboration between different stakeholders in the 5G ecosystem to address these challenges. The platform enables different stakeholders in 5G network slicing (e.g., cloud providers, network operators, RAN providers, and transport network providers) to share and rent their resources to create customized network slices that meet the specific requirements of 5G applications. The orchestration of network slices is managed through blockchain smart contracts, and the resulting network slices are encoded as NFT tokens and made available for purchase in a decentralized NFT marketplace. Customers can select and purchase the network slices that best meet their needs by paying either crypto or flat currency. Revenue generated through the sale of network slices is distributed among different providers, facilitating a fair and efficient marketplace. Intelligent network slice optimization is accomplished through the utilization of a custom-trained GPT-3.5 LLM(which powers the ChatGPT). This LLM can generate valuable insights and recommendations from extensive network datasets, contributing to the optimization of network slices for enhanced performance and efficiency. A prototype of SliceGPT has been implemented with FreedomFi 5G gateway, OpenAirInterface 5G core, OpenAI GPT-3.5-turbo model, LlamaIndex and Langchain. To the best of our knowledge, this is the very first research endeavor to incorporate the GPT LLMs for optimizing 5G/6G network slicing,

Traditional and indigenous medicine, deeply rooted in ancient traditions and wisdom, plays a crucial role in global healthcare and cultural identity. These practices provide treatments for illnesses such as cancer and bone injuries, which often lack effective remedies in Western medicine. However, these valuable systems face challenges like potential knowledge loss, undervaluation of practitioners’ expertise, and the risk of fraud due to the absence of credential verification mechanisms. In this research, we introduce "WedaGPT," a Generative AI-enabled platform that utilizes a custom-trained Meta’s Llama2 Large Language Model (LLM), Blockchain, self-sovereign identity (SSI), Non-Fungible Tokens (NFTs), and model cards to share traditional medical knowledge and address these issues. WedaGPT creates a collaborative ecosystem connecting doctors, medicine providers, therapists, patients, and technology experts, all committed to preserving and advancing traditional healing practices. This platform enables secure and transparent contributions from all stakeholders to patient well-being. Ancient medical recipe books are translated into English and digitized into PDF formats to enrich the platform’s knowledge base. These texts are used to fine-tune the Llama2 LLM, which has been quantized and optimized with Qlora for performance on consumer-grade hardware. Through a chat-based interface in the SSI-enabled mobile wallet, users can interact with the LLM and access detailed information on treatments, recipes, prescriptions, and healing methods. Additionally, users can consult remotely with doctors who prescribe treatments through this wallet. A key feature of WedaGPT is transforming ancient medicinal recipes into NFT tokens for sale on NFT marketplaces, giving traditional knowledge digital authenticity and economic value. Revenue from these sales is distributed among platform contributors, promoting equitable ownership and recognition. Medical recipe data, including treatment histories and physician details, are encapsulated in Model Cards and securely stored on the blockchain. This system offers mechanisms to verify doctors and treatments in a privacy-preserving way, potentially reducing fraud and medication errors.

We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world’s population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-of-the-art Random Forest methods and empirical approaches.

People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person’s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including ‘mental illness’ (e.g., depression) and emotions (e.g., ‘sadness’, ‘anger’). EmoMent corpus achieved ‘very good’ inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss’ Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.

Given the widespread prevalence of vulnerabilities, remediation is a critical phase that every software project has to go through. When comparing the studies on understanding the security vulnerabilities in software, such as vulnerability discovery and patterns, there is a lack of studies on the vulnerability remediation phase. To address this, we have done a timeline analysis for 130 of the most dependent upon open source projects written in JavaScript language, hosted on GitHub to understand the nature and the lifetime of the vulnerabilities in those projects. We used a static code analyzer on 501K commits from the repositories to identify commits that introduced new vulnerabilities to the code and fixed existing vulnerabilities in the code. In 90% of the projects, we identified that a commit that fixed an existing vulnerability had introduced one or more new vulnerabilities into the code. On average, 16% of the commits intended to fix vulnerabilities have introduced one or more new vulnerabilities from the analyzed projects. We also found that 18% of the total vulnerabilities found in those projects have originated from a commit meant to fix an existing vulnerability, and 78% of those vulnerabilities could have been avoided of introduction if the developers were to use proper internal testing. Here, we demonstrate Sequza, a visualization tool to help organizations detect such instances at the earliest possible.

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform “Casper” to address the issues on centralized identity systems. “Casper ” is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers’ mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms’ Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.

Blockchain is a peer-to-peer distributed storage that stores chronological series of transactions in a tamper-resistant manner. Blockchain became popular in various industries due to its decentralized trust ecosystem. When integrating blockchain with big data, one encounters many challenges. Current public blockchain does not support high transaction throughput; it does not scale in terms of big data storage and management; it does not provide keyword-based search and retrieval; and so on. As a result, it is hard to incorporate existing blockchain systems for big data applications. In this research, we propose a new blockchain storage "Mystiko" that is built over the Apache Cassandra distributed database to incorporate big data. Mystiko supports high transaction throughput, high scalability, high availability and full text search features. With Mystiko, we make big data more secure, structured and meaningful, and allows further data analytics on big data to be more easily performed.

A significant number of human and elephant lives have been lost due to the human-elephant conflict in Sri Lanka. To save lives of humans and elephants, it is therefore important to minimize encounters between them. In this paper, we present Eloc, a system that detects the presence of elephants using their infrasonic emissions near human habitats and then localize their positions. The high cost of infrasonic detectors is an important challenge to the real-world deployment of such localization systems, in particular in developing countries where the human-elephant conflict occurs. In order to address this problem, we design a low cost infrasonic detector that can be easily built using commodity off-the-shelf hardware. We present promising results in localizing an artificial infrasonic source and real-world experiments that suggest that we can localize free ranging elephants in the wild using this low cost infrasonic detector with an accuracy of around 10 m at distances of several hundred meters.

Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas.

The human-elephant conflict is causing significant damages to the life of human and elephants in Sri Lanka. Minimizing encounters between humans and elephants is hence crucial in alleviating the human-elephant conflict. We have earlier introduced Eloc, a cost-effective localization system that is based on infrasonic emissions from wild elephants. One of the remaining challenges is a resource-efficient method to detect the elephants' infrasonic emissions. We present our efforts on devising a support vector machine (SVM) that is able to detect elephant rumbles on Eloc nodes.

Radio Tomographic Imaging (RTI) enables device free localization of physical objects by using signal attenuation in wireless networks. In this paper, we explore how existing RTI methods can be used in WiFi networks to do tomographic imaging. Moreover we analyze and evaluate the properties that affect the accuracy of WiFi tomographic imaging process.

We argue that, a wireless sensor does not have to implement a full networking stack in order to be a part of an energy efficient sensing application as long as it can deliver its sensed data to an Internet connected device over a single hop. Current hardware industry trends indicate the possibility of implementing wireless sensors for a low cost with simple capabilities. This poster highlights our preliminary work aligned with this trend and aims to open a discussion on this topic.