Ritwik Murali

@amrita.edu

Assistant Professor (Sr)
Amrita Vishwa Vidyapeetham University



                          

https://researchid.co/ritwikm

RESEARCH INTERESTS

Cyber Security, Bio-Inspired Computing, Artificial Intelligence, Computing Education

FUTURE PROJECTS

EA for gamification and malware detection


Applications Invited
Collaborators
21

Scopus Publications

125

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications



  • Exploring Evolution for Aesthetic & Abstract 3D Art
    Ritwik Murali and Veeramanohar Avudaiappan
    ACM

  • Can Technical Festivals Help in Attaining Undergraduate Engineering Programme Outcomes?
    Dhanya M Dhanalakshmy and Ritwik Murali
    IEEE
    The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively.

  • Unveiling the invisible struggles: Exploring student perspectives on mental health in universities
    Ritwik Murali and Veeramanohar Avudaiappan
    Institute of Electrical and Electronics Engineers (IEEE)
    In the current academic environment, the increasing prevalence of stress among students has become a critical concern, significantly affecting both mental health and academic outcomes. Factors such as heavy academic workloads, interpersonal conflicts, financial challenges, and societal pressures contribute to cognitive impairments, procrastination, and declining academic performance. Unaddressed, this stress can escalate into severe mental health conditions, including anxiety, depression, and self-harming tendencies, with marginalized or overwhelmed students being particularly vulnerable. Familial expectations, financial instability, imposter syndrome, fear of missing out, cyberbullying, and unsupportive institutional cultures further intensify these challenges. While mental health support mechanisms exist within institutions, they often remain reactive, addressing issues only after they manifest. To address this growing crisis, a shift toward proactive measures is essential, encompassing counseling, meditation, peer support systems, and engagement in extracurricular activities to foster emotional resilience. A holistic and preventative approach that emphasizes early intervention, open communication, and supportive academic environments is imperative to promote students' well-being and academic success.

  • Augmenting Virtual Labs with Artificial Intelligence for Hybrid Learning
    Ritwik Murali, Nitin Ravi, and Amruthiyu Surendran
    IEEE
    The shift towards online learning through websites has been instrumental in enhancing accessibility. However, while virtual labs have been promising in generating interactive simulations of science experiments, they fall short in delivering the dynamic and immersive experiences essential for effective learning, especially in computing education courses. Recognizing this challenge, the integration of artificial intelligence (AI) into virtual labs emerges as a promising solution to aid student learning. AI-augmented virtual labs can simulate real-world scenarios, providing students with hands-on experiences in a controlled and safe environment. This work explores the impact on student learning (with focus on user engagement, skill development, comprehension, retention, and overall experience) when integrating a virtual labs module with and without AI support. Student perspectives on the effectiveness and satisfaction with the learning modules were collected using surveys that probe not only the perceived improvement in learning outcomes, but also the subjective user experience and engagement levels. Additionally, user performance indicators, based on assessments/evaluations and discussions (within the virtual labs, with and without AI integration), serve as a measure of the students' skills development, comprehension, and knowledge retention. This approach provides an objective metric to evaluate the efficacy of each learning approach. Based on data from surveys, performance indicators, and feedback analysis, the study aims to draw comprehensive conclusions and suggest potential avenues of further inquiry, regarding the effectiveness of integrating virtual labs both with AI. This integration not only enhances the learning experience, but also fosters critical thinking and problem-solving skills in students. This innovative approach offers a distinctive combination of interactivity, adaptability, and versatility. The goal of this study is not to argue for AI supported virtual lab-based learning approaches, but rather, to give empirical information about its usefulness. The findings of this study will contribute to the ongoing discussion on innovative educational practices by providing useful insights into the possible benefits and challenges of adopting hybrid learning approaches powered by AI.

  • Towards Assessing the Credibility of Chatbot Responses for Technical Assessments in Higher Education
    Ritwik Murali, Dhanya M. Dhanalakshmy, Veeramanohar Avudaiappan, and Gayathri Sivakumar
    IEEE
    The recent challenge in higher education is to convey the importance of understanding concepts over rote learning. This challenge has increased in complexity with the arrival of large language model (LLM) based chatbots. Students are increasingly looking to such AI based chatbots as “sources of wisdom” instead of utilizing the same as learning aids. Despite disclaimers by the LLM creators, many students turn to the chatbot for answers to almost all learning assignments. This research work explores the level to which the LLM responses can be utilized for student learning in technical education. By understanding the contradictions between student answers and the responses generated by the LLMs, this work explores the limitations of the LLM based environments towards providing acceptable answers for assessments - specifically within the computer science engineering domain. While numerous studies have concentrated on ChatGPT, it is essential to consider the diverse range of alternative chat-bots accessible online that students may also utilize. Therefore, this work considers 5 popular AI-based chatbots for the study. With the “prompt” being the prime factor that impacts the response from chat-bots, the responses of the chatbots were collected using 2 different prompting techniques. The chatbot responses were evaluated against actual student responses by multiple reviewers to gauge its effectiveness as appropriate student answers. Both students and all chatbots were given questions aligned with the Blooms taxonomy levels (BTL) 1 to 4 in three different subjects. Each of the courses included a diverse range of questions including text-based questions, mathematical problems, and programming questions. The results show that the chatbot responses were acceptable for low BT level questions but failed to answer convincingly when asked for an algorithm. Overall, the chatbot performance (across the tested LLMs) was below average when the question set covered the BTL range 1–4. However, since the answers up to BTL2 were acceptable, LLM based chatbot answers were able to barely pass 1–2 of the 3 subjects (with the best performers scoring near the pass mark). Based on these results, it is possible to conclude that LLM based chatbots cannot be depended on for higher order learning but can be used to aid students who are struggling to pass basic courses.

  • Student Perception of Online Judges in Learning Problem Solving through Introductory Programming
    Ritwik Murali, Akash Ravi, Kailashnath Nagendran, and Veeramanohar Avudaiappan
    ACM
    Multiple strategies exist while attempting to introduce problem-solving to engineering students. One of the popular strategies is to encourage problem-solving through the introductory programming course. This paper presents an in-depth analysis of the extent to which online judging-based evaluation and training strategies can be employed to effectively deliver introductory programming courses. The paper focuses on student perceptions of learning as a measure to gauge their confidence in problem-solving and programming. With responses from over 400 students across departments and in various stages of the undergraduate program, this paper discusses a detailed analysis of student perceptions of learning, their comfort levels, and confidence when using online judge based evaluation strategies. Results show that a majority of students believe that online judges assist them to improve their problem solving and programming skills.

  • Empowering Novice Programmers with Visual Problem Solving tools
    Ritwik Murali, Rajkumar Sukumar, Mary Sanjana Gali, and Veeramanohar Avudaiappan
    ACM
    Learning one’s first programming language includes challenges of syntax, surplus code and semantics. The learning can be easy or quite hard for a novice programmer depending on the programming language. Even the small “Hello World” program code contains semantic and syntactic complexity. This paper discusses the pros and cons of multiple tools that may be used for syntax independent implementation of solutions. Based on the shortcomings of existing tools, Flowgramming – a platform independent flowcharting software for the novice programmer / problem solver and their instructor, is also proposed in the paper. Flowcharts developed using Flowgramming can be executed by the built-in interpreter which helps the novice programmer focus on understanding the problem solving strategy in a visually appealing manner and also allows for a language independent learning of solution strategies.

  • Evolving malware variants as antigens for antivirus systems
    Ritwik Murali, Palanisamy Thangavel, and C. Shunmuga Velayutham
    Elsevier BV

  • ATLAS - A Co-evolutionary Framework for Automatic Tuning of Adversarial Neural Networks
    Saurav Shyju and Ritwik Murali
    ACM
    Generative Adversarial Networks (GANs) have gained popularity due to their ability to produce realistic examples from existing data without any supervision. However, they are dependent on their hyperparameters, the tuning of which is usually a manual task. Additionally, the computing resources required for such training are also extremely high. In this paper, ATLAS - a Cloud-based Co-evolutionary Framework for training such adversarial networks using Evolutionary Algorithms is proposed. ATLAS views the GAN components (generator and discriminator) as in a predator-prey relationship and involves co-evolution as a method to address the challenges of overfitting, exploding/vanishing gradients and tunes the hyperparameters of both the components of the GAN. The ATLAS framework is designed to be customizable, and resource flexible to allow for set-up and easy usage for training complex adversarial networks in both distributed and cloud environments. Experiments testing ATLAS capability for anomaly detection were performed and the results show that ATLAS can consistently evolve and produce high-performance GAN models.

  • Adapting novelty towards generating antigens for antivirus systems
    Ritwik Murali and C Shunmuga Velayutham
    ACM
    It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade detection by scanners. Therefore, there exists the need for a proactively generated malware variant dataset to aid detection of such diverse variants by automated antivirus scanners. This paper proposes and demonstrates a generic assembly source code based framework that facilitates any evolutionary algorithm to generate diverse and potential variants of an input malware, while retaining its maliciousness, yet capable of evading antivirus scanners. Generic code transformation functions and a novelty search supported quality metric have been proposed as components of the framework to be used respectively as variation operators and fitness function, for evolutionary algorithms. The results demonstrate the effectiveness of the framework in generating diverse variants and the generated variants have been shown to evade over 98% of popular antivirus scanners. The malware variants evolved by the framework can serve as antigens to assist malware analysis engines to improve their malware detection algorithms.

  • A Conceptual Direction on Automatically Evolving Computer Malware using Genetic and Evolutionary Algorithms
    Ritwik Murali and C Shunmuga Velayutham
    IEEE
    The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.

  • Enhancing Digital Well-being using Opinion Mining and Sentiment Classifiers
    Ritwik Murali, Akash Ravi, and Harshit Agarwal
    IEEE
    Opinion mining on various issues is a very popular trend in the micro-blogging research community. Advanced data mining techniques using sentiment analysis and machine learning algorithms on large datasets like microblogging websites are popular and trending in the data science community. But such analytics are limited to only certain aspects of interest. In this paper, we present a search engine that presents a user’s emotions over a timeline. This would serve as a novel approach to adding search capability over opinion mining procedures and exploring potential optimizations for a wide range of features and methods for training sentiment classifiers. Users should be able to chart down their emotional quotient using the proposed application. This would aid in promoting their digital well-being.

  • A Malware Variant Resistant to Traditional Analysis Techniques
    Ritwik Murali, Akash Ravi, and Harshit Agarwal
    IEEE
    In today’s world, the word malware is synonymous with mysterious programs that spread havoc and sow destruction upon the computing system it infects. These malware are analyzed and understood by malware analysts who reverse engineer the program in an effort to understand it and provide appropriate identifications or signatures that enable anti-malware programs to effectively combat and resolve threats. Malware authors develop ways to circumvent or prevent this analysis of their code thus rendering preventive measures ineffective. This paper discusses existing analysis subverting techniques and how they are overcome by modern analysis techniques. Further, this paper proposes a new method to resist traditional malware analysis techniques by creating a split-personality malware variant that uses a technique known as shadow attack. The proposal is validated by creating a malware dropper and testing this dropper in controlled laboratory conditions as a part of the concept of proactive defense.

  • Optimal Feature Selection for Non-Network Malware Classification
    KannanMani S. ManiArasuSekar, Paveethran Swaminathan, Ritwik Murali, Govind K. Ratan, and Surya V. Siva
    IEEE
    In this digital age, almost every system and service has moved from a localized to a digital environment. Consequently the number of attacks targeting both personal as well as commercial digital devices has also increased exponentially. In most cases specific malware attacks have caused widespread damage and emotional anguish. Though there are automated techniques to analyse and thwart such attacks, they are still far from perfect. This paper identifies optimal features, which improves the accuracy and efficiency of the classification process, required for malware classification in an attempt to assist automated anti-malware systems identify and block malware families in an attempt to secure the end user and reduce the damage caused by these malicious software.


  • A Simple and Robust End-to-End Encryption Architecture for Anonymous and Secure Whistleblowing
    Hariharan Jayakrishnan and Ritwik Murali
    IEEE
    Much has been mentioned about the importance of whistleblowing. While all organizations are recommended to have a whistleblowing mechanism, there are very few completely software-based platforms that assist in performing this task securely. The primary concerns are to ensure that the whistleblower remains anonymous and the disclosures are securely delivered to the competent authority, usually media organizations. In this paper, we have analyzed the security of the state of the art software-based whistleblowing platforms and related research, identified security issues and proposed a new architecture that satisfactorily ensures the requirements for anonymous and secure whistleblowing. We have verified the strength of our solution against the existing platforms and related research with the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. Our approach is practical, backed by cryptographic security and, because of its modularity, can be easily included in the current infrastructure of many whistleblowing platforms. The results show that our architecture is simple, robust and implements a complete end-to-end encryption strategy thus enabling secure and anonymous whistleblowing.

  • Localizing Assets in an Indoor Environment Using Sensor Fusion
    Ritwik Murali, Dhivya Nachimuthu, Dhansri Varsha SenthilKumar, Malarvizhi Shanmuga Pandian, and Dhareni Krishnen
    IEEE
    Object localization for indoor environments is still a challenge due to chaotic indoor environments. The most popular of techniques make use of Radio Frequency Identification (RFID) equipment or overhead cameras. We propose a novel method by combining these two popular methods, namely the RFID system and a single camera placed at an angle for increased range. The RFID system containing two RFID antennas estimates the approximate position of each object by identifying the range or area in which the object is found and then the camera recognizes the object by comparing its extracted features. The localization is achieved by the information obtained from the RFID sensors used. Experimental verification is performed to test the accuracy and robustness of this proposed method.

  • Identifying third-party-influenced vulnerabilities in massively multi-player online role-playing games


  • A novel advertisement recommendation system for online video portals


RECENT SCHOLAR PUBLICATIONS

  • Can Technical Festivals Help in Attaining Undergraduate Engineering Programme Outcomes?
    DM Dhanalakshmy, R Murali
    2024 IEEE International Conference on Teaching, Assessment and Learning for 2024

  • Unveiling the invisible struggles: Exploring student perspectives on mental health in universities
    R Murali, V Avudaiappan
    IEEE Potentials 2024

  • Exploring Evolution for Aesthetic & Abstract 3D Art
    R Murali, V Avudaiappan
    Proceedings of the Genetic and Evolutionary Computation Conference Companion 2024

  • Exploring the use of fitness landscape analysis for understanding malware evolution
    K Babaagba, R Murali, S Thomson
    Proceedings of the Genetic and Evolutionary Computation Conference Companion 2024

  • Towards assessing the credibility of chatbot responses for technical assessments in higher education
    R Murali, DM Dhanalakshmy, V Avudaiappan, G Sivakumar
    2024 IEEE Global Engineering Education Conference (EDUCON), 1-9 2024

  • Augmenting Virtual Labs with Artificial Intelligence for Hybrid Learning
    R Murali, N Ravi, A Surendran
    2024 IEEE Global Engineering Education Conference (EDUCON), 1-10 2024

  • Student perception of online judges in learning problem solving through introductory programming
    R Murali, A Ravi, K Nagendran, V Avudaiappan
    Proceedings of the 16th Annual ACM India Compute Conference, 43-48 2023

  • Empowering novice programmers with visual problem solving tools
    R Murali, R Sukumar, M Sanjana Gali, V Avudaiappan
    Proceedings of the 16th Annual ACM India Compute Conference, 100-103 2023

  • Evolving malware variants as antigens for antivirus systems
    R Murali, P Thangavel, CS Velayutham
    Expert Systems with Applications 226, 120092 2023

  • ATLAS-A co-evolutionary framework for automatic tuning of adversarial neural networks
    S Shyju, R Murali
    Proceedings of the Companion Conference on Genetic and Evolutionary 2023

  • Adapting novelty towards generating antigens for antivirus systems
    R Murali, CS Velayutham
    Proceedings of the Genetic and Evolutionary Computation Conference, 1254-1262 2022

  • Enhancing digital well-being using opinion mining and sentiment classifiers
    R Murali, A Ravi, H Agarwal
    2020 International Conference on Inventive Computation Technologies (ICICT 2020

  • 5th International Conference on Inventive Computation Technologies (ICICT-2020) 26-28 February 2020
    MG Kambalimath, MS Kakkasageri, BA Patel, A Parikh, Z Wu, S Qi, ...
     2020

  • Optimal feature selection for non-network malware classification
    KMS ManiArasuSekar, P Swaminathan, R Murali, GK Ratan, SV Siva
    2020 International Conference on Inventive Computation Technologies (ICICT 2020

  • A conceptual direction on automatically evolving computer malware using genetic and evolutionary algorithms
    R Murali, CS Velayutham
    2020 International Conference on Inventive Computation Technologies (ICICT 2020

  • A malware variant resistant to traditional analysis techniques
    R Murali, A Ravi, H Agarwal
    2020 international conference on emerging trends in information technology 2020

  • 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

  • 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

  • Localizing Assets in an Indoor Environment Using Sensor Fusion
    R Murali, D Nachimuthu, DV SenthilKumar, MS Pandian, D Krishnen
    International Conference on Advances in Computing, Communications and 2018

  • A Novel Advertisement Recommendation System For Online Video Portals
    S Soundappan, R ShajiKumar, M Ritwik, S Chithra, TM Sakthie Vishanth
    International Journal of Applied Engineering Research (IJAER) 10 (11), 28903 2015

MOST CITED SCHOLAR PUBLICATIONS

  • A comprehensive but not complicated survey on quantum computing
    PS Menon, M Ritwik
    IERI Procedia 10, 144-152 2014
    Citations: 34

  • A malware variant resistant to traditional analysis techniques
    R Murali, A Ravi, H Agarwal
    2020 international conference on emerging trends in information technology 2020
    Citations: 16

  • 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
    Citations: 11

  • Evolving malware variants as antigens for antivirus systems
    R Murali, P Thangavel, CS Velayutham
    Expert Systems with Applications 226, 120092 2023
    Citations: 10

  • Optimal feature selection for non-network malware classification
    KMS ManiArasuSekar, P Swaminathan, R Murali, GK Ratan, SV Siva
    2020 International Conference on Inventive Computation Technologies (ICICT 2020
    Citations: 8

  • A conceptual direction on automatically evolving computer malware using genetic and evolutionary algorithms
    R Murali, CS Velayutham
    2020 International Conference on Inventive Computation Technologies (ICICT 2020
    Citations: 7

  • Empowering novice programmers with visual problem solving tools
    R Murali, R Sukumar, M Sanjana Gali, V Avudaiappan
    Proceedings of the 16th Annual ACM India Compute Conference, 100-103 2023
    Citations: 5

  • Adapting novelty towards generating antigens for antivirus systems
    R Murali, CS Velayutham
    Proceedings of the Genetic and Evolutionary Computation Conference, 1254-1262 2022
    Citations: 5

  • Enhancing digital well-being using opinion mining and sentiment classifiers
    R Murali, A Ravi, H Agarwal
    2020 International Conference on Inventive Computation Technologies (ICICT 2020
    Citations: 5

  • 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
    Citations: 5

  • Localizing Assets in an Indoor Environment Using Sensor Fusion
    R Murali, D Nachimuthu, DV SenthilKumar, MS Pandian, D Krishnen
    International Conference on Advances in Computing, Communications and 2018
    Citations: 4

  • A Novel Advertisement Recommendation System For Online Video Portals
    S Soundappan, R ShajiKumar, M Ritwik, S Chithra, TM Sakthie Vishanth
    International Journal of Applied Engineering Research (IJAER) 10 (11), 28903 2015
    Citations: 4

  • Student perception of online judges in learning problem solving through introductory programming
    R Murali, A Ravi, K Nagendran, V Avudaiappan
    Proceedings of the 16th Annual ACM India Compute Conference, 43-48 2023
    Citations: 3

  • ATLAS-A co-evolutionary framework for automatic tuning of adversarial neural networks
    S Shyju, R Murali
    Proceedings of the Companion Conference on Genetic and Evolutionary 2023
    Citations: 3

  • Augmenting Virtual Labs with Artificial Intelligence for Hybrid Learning
    R Murali, N Ravi, A Surendran
    2024 IEEE Global Engineering Education Conference (EDUCON), 1-10 2024
    Citations: 2

  • Analyzing the Makier Virus
    R Murali, K Praveen
    International Journal of Computer Science Issues 10 (2), 530-533 2013
    Citations: 2

  • Towards assessing the credibility of chatbot responses for technical assessments in higher education
    R Murali, DM Dhanalakshmy, V Avudaiappan, G Sivakumar
    2024 IEEE Global Engineering Education Conference (EDUCON), 1-9 2024
    Citations: 1