Algorithm Explainability for Malware Evolution with Search Trajectory Networks Kehinde Babaagba, Ritwik Murali, Sarah L. Thomson Gecco 2025 Companion Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion, 2025 We present a preliminary study investigating the application of fitness landscape analysis to malware evolution. This type of analysis, though widely used in evolutionary computation, has been underutilised in understanding the optimization processes underlying malware adaptation. We examine two types of evolving malware: Android and Windows-based programs, and we analyse an existing evolutionary algorithm from the literature for each setting. To gain deeper insights into the algorithm behaviour we construct, visualise, and analyse search trajectory networks. Our findings suggest that the two considered algorithms are associated with markedly different fitness landscape structure. We notice from the results that current search operators may struggle to effectively navigate malware fitness landscapes. In particular, further consideration of the presence of neutrality and plateaus may be needed in this domain.
LLM-aided Evolutionary Algorithms for Haiku Generation Vedant Dhaval Jobanputra, Basam Thilaknath Reddy, Sri Ganesh Bhojanapalli, Krishna Aditya S. V. S, Bagavathi Chandrasekara, Ritwik Murali Gecco 2025 Companion Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion, 2025 Given the rapid advancement of AI across various fields, it is crucial to bridge computational methods and creative poetry generation, pushing the boundaries of what artificial intelligence can achieve in creative domains. This work explores the potential of blending the NSGA-II evolutionary optimization, Gemini 1.5 and Monte Carlo Tree Search to generate and iteratively enhance poems while also incorporating human feedback. Since natural language generation can transform non linguistic inputs like data into expressive text, it opens up avenues to explore innovative poetic form. The results demonstrate the model's better poem generation over traditional methods, showcasing its ability to refine poetic quality dynamically through evolutionary cycles and user feedback, thus setting a new standard for automated poetic creativity. Future work will explore deeper semantic control mechanisms, refined evaluation strategies, and broader linguistic adaptability across diverse poetic traditions.
Securing the Software Package Supply Chain for Critical Systems Ritwik Murali, Akash Ravi Cyber Security and Data Science Innovations for Sustainable Development of Heicc Healthcare Education Industry Cities and Communities, 2025 Software-based systems have revolutionized industries, from nuclear power stations to spacecraft control. Ensuring the reliability and resilience of these systems is crucial. Emerging threats target software supply chains, as demonstrated by the widespread SolarWinds hack in late 2020. Cybercriminals exploited the supply chain to distribute Trojan software versions disguised as updates. The Apache Log4j vulnerability, with a Common Vulnerability Scoring System (CVSS) score of 10, had devastating consequences due to its widespread use in Java applications. Supply chains extend beyond patches and updates, involving distribution networks throughout the software lifecycle. Industries like smart grids, manufacturing, healthcare, and finance rely on interconnected software systems and their dependencies. Industry 4.0’s digital transformation and Industry 5.0’s fusion of cognitive computing with human intelligence increase the cyber-attack surface. Software is no longer standalone; it’s a collection of packages developed globally. To secure software modules and add-ons, robust distribution architectures are essential. The proposed chapter enhances existing delivery frameworks by implementing a permissioned ledger with Proof of Authority consensus and multi-party signatures. This system prevents attacks while allowing stakeholders to verify security. Critical systems can interface with the secure pipeline, preventing cascading attacks anywhere in the software supply chain.
Evolving binary masks to explain image classification Rudra Sarkar, Ritwik Murali Procedia Computer Science, 2025 The high dimensionality of image tensors and the complex architecture of Deep Convolutional Neural Networks render the task of image classification very non-intuitive, prompting the need for tools like eXplainable Artificial Intelligence(XAI) to comprehend the classification strategy. While notable techniques such as Gradient Weighted Class Activation Mapping, SHapely Additive exPlanations, and Local Interpretable Model-Agnostic Explanations have made significant strides in this domain, there remain some challenges concerning their applicability in scenarios where the model is entirely abstracted from the user and leveraging those explanations for understanding the classification strategy of the networks as inconsistencies prevail between the predictions for the original image and its corresponding explanations. A novel model-agnostic XAI algorithm with an evolutionary approach is introduced in this paper to provide local explanations for image classification tasks that address the drawbacks encountered by the mentioned algorithms. Central to this approach is the evolution of Binary Masks, which is then used to explain the classification strategy of the network. Sample results with benchmark datasets like CIFAR 10 and Imagenette have been documented along with a comparative study for the explanations obtained from the state-of-the-art techniques against the proposed algorithm. The results reveal the efficiency of the proposed algorithm against the existing algorithms in terms of the confidence and localization capabilities for the corresponding explanations.
Exploring Evolution for Aesthetic & Abstract 3D Art Ritwik Murali, Veeramanohar Avudaiappan Gecco 2024 Companion Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024 Generating 3D objects to produce art that diverges from conventional or existing artistic patterns is a major challenge. Given the growing prevalence of three-dimensional (3D) art in various domains such as architecture, game design, augmented reality, virtual reality, etc., it becomes imperative to devise strategies for generating diverse forms of 3D objects. This research explores the evolution of 3D artistic objects that are complex, aesthetically pleasing and abstract in nature. Eleven different fitness metrics adapted from existing 2D image evolution metrics are first used to generate the 3D models and subsequently 3 new metrics are proposed specifically for the 3D model generation. Then, 4 different methods are used to set the initial population of each of the 14 different metrics resulting in 56 different 3D model evolution techniques. Finally, user feedback (as a survey) is used to evaluate the aesthetics of the different models evolved by these fitness metrics. Results show that the models evolved by the proposed 3D specific fitness metrics were rated higher than others by the users. In a broader context, this research pushes the boundaries of 3D art generation and evaluation techniques, while also providing insights and opportunities for future in-depth exploration of 3D art generation.
Exploring the use of fitness landscape analysis for understanding malware evolution Kehinde O. Babaagba, Ritwik Murali, Sarah Thomson Gecco 2024 Companion Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024 We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis. We consider Android-based malware evolution and an existing evolutionary algorithm from the literature is used. We construct and visualise search trajectory networks (STNs), which are a new tool aimed at investigation of algorithm behaviour. The STNs indicate that the considered malware spaces may be difficult to navigate under current search operators and that new ones may warrant consideration.
Optimal Feature Selection for Non-Network Malware Classification KannanMani S. ManiArasuSekar, Paveethran Swaminathan, Ritwik Murali, Govind K. Ratan, Surya V. Siva Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020
Localizing Assets in an Indoor Environment Using Sensor Fusion Ritwik Murali, Dhivya Nachimuthu, Dhansri Varsha SenthilKumar, Malarvizhi Shanmuga Pandian, Dhareni Krishnen 2018 International Conference on Advances in Computing Communications and Informatics Icacci 2018, 2018
Identifying third-party-influenced vulnerabilities in massively multi-player online role-playing games International Journal of Applied Engineering Research, 2015
A novel advertisement recommendation system for online video portals International Journal of Applied Engineering Research, 2015
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MOST CITED SCHOLAR PUBLICATIONS
A comprehensive but not complicated survey on quantum computing PS Menon, M Ritwik IERI Procedia 10, 144-152 , 2014 2014 Citations: 44
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Evolving malware variants as antigens for antivirus systems R Murali, P Thangavel, CS Velayutham Expert Systems with Applications 226, 120092 , 2023 2023 Citations: 22
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A Simple and Robust End-to-End Encryption Architecture for Anonymous and Secure Whistleblowing H Jayakrishnan, R Murali 2019 Twelfth International Conference on Contemporary Computing (IC3) , 2019 2019 Citations: 15
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Adapting novelty towards generating antigens for antivirus systems R Murali, CS Velayutham Proceedings of the Genetic and Evolutionary Computation Conference, 1254-1262 , 2022 2022 Citations: 10
A preliminary investigation into automatically evolving computer viruses using evolutionary algorithms R Murali, C Shunmuga Velayutham Journal of Intelligent & Fuzzy Systems 38 (5), 6517 – 6526 , 2020 2020 Citations: 9
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