Journal of Ad-hoc Network and Mobile Computing (e-ISSN: 3048-9180)

Identity and Data Theft: Bypassing the Passwords using John the Ripper

Mayank Kumar, E. Prajwal, N. Narayana Reddy, P. Haneef, Abirami A — Mon, 01 Jun 2026 00:00:00 +0000

Despite having been around for decades, user-created passwords continue to be one of the most common methods of blocking unauthorized users from obtaining access to sensitive data or systems on the Internet. The rapid increase in the number of cyberattacks has revealed the serious shortcomings of using user-created passwords as an access control mechanism. Specifically, user-created passwords have security flaws when they are not strong enough, are too predictable, or are too easy to guess. To understand password security in a more systematic way, they analyze password cracking using John the Ripper (JtR), an open-source password auditing and recovery tool that is widely available online. The purpose of this research is to explore various password-cracking methods (i.e., dictionary, rule-based, hybrid, and brute-force). The study was conducted in a controlled laboratory environment using the Kali Linux operating system. The research tested groups of passwords for their hashed encrypted security using varying methods of attack in order to relate to how passwords are typically cracked in real life. The research used a well-known rockyou.txt wordlist to identify common user passwords for deciphering their hashed values through a dictionary-based attack. Overall, the results indicate that a large percentage of weak, frequently used passwords can be successfully cracked using dictionary attack techniques in a relatively short time. Although brute force attacks consumed a lot of processing power, they did yield success against all short passwords tried using this attack method. Ultimately, rule-based techniques greatly increased the efficiency of cracking by allowing for variations of commonly used password formats. As shown in the results of this research, it is essential to have robust password policies in place, to increase the complexity of passwords, and to implement multi-factor authentication in order to enhance security. The research also highlights the need to ethically use password cracking tools by limiting their use to authorized and educational environments, and for the purposes of security testing only. In conclusion, this research raises awareness about the vulnerability of passwords and encourages greater adoption of good cybersecurity practices.

CodeHero: A Gamified Open-source Mentorship Platform for Skill-based Peer Learning

Sumit Kumar, Manish Bhardwaj, Sourabh Prajapat, Vikash Singh, Vikas Sharma — Thu, 13 Aug 2026 00:00:00 +0000

In the last few years, online learning platforms have increased a lot and become very common among students. Still, with this growth, many platforms are not able to keep learners properly engaged. Interaction is often limited, and in many cases, mentors are not really verified based on their actual skills. Because of this, students sometimes spend a lot of time on courses that don’t give enough practical understanding or useful guidance. The learning ends up being more theoretical, and the overall experience is not always effective. To address these gaps, they present CodeHero: a gamified, open-source mentorship platform. CodeHero fosters skill-based peer learning while safeguarding mentor quality via a structured evaluation pipeline. The proposed platform follows a two-phase eligibility model in which students are assessed based on real and measurable contributions, including doubt-solving activity, learning consistency, peer ratings, technical blog writing, and community participation. Gamification is usually included to keep learners interested and active on the platform. Features such as rewards, leaderboards, and progress tracking can help students stay more consistent in their learning over time. Only students who successfully complete the first evaluation phase are allowed to transition into mentor roles and offer paid courses, ensuring that mentorship is provided only by capable and consistent contributors. CodeHero is implemented using the MERN stack, with a modular and scalable architecture that supports real-time interaction and efficient data handling. Experimental evaluation using simulated user activity demonstrates improved learner engagement, reduced dropout rates, and enhanced mentor quality when compared to traditional online learning platforms. The results indicate that the proposed approach provides an effective and practical solution for building quality-driven, technology-based online education systems.

Optimizing MNIST Digit Recognition for Edge Devices: A Multi-dimensional Evaluation Approach

Rajneesh Yadav, Aakash Parmar — Thu, 13 Aug 2026 00:00:00 +0000

Handwritten digit recognition on the MNIST dataset routinely achieves near-perfect accuracy. However, deploying these models on edge platforms introduces new challenges where predictive accuracy is no longer the only metric that matters. For constrained systems, memory footprint, computational overhead, and inference latency are equally critical. In this work, they propose a multi-dimensional evaluation framework for deployment-oriented assessment. The authors introduce the Edge Suitability Score (ESS), a composite metric that combines normalized accuracy, model size, and inference speed into a single value, weighted at 0.40, 0.35, and 0.25, respectively, to reflect their relative importance for microcontroller deployment. By comparing two lightweight architectures, a scaled-down CNN (L-CNN) and a depthwise-separable L-MobileNet, against a deeper Baseline CNN, the results show that compact networks can maintain near-99% accuracy while drastically reducing storage and computation requirements: L-MobileNet achieves 99.10% accuracy with only 12,186 parameters and roughly 48 KB of weight memory, compared with 99.45% accuracy and over 1 MB for the baseline. This framework offers a practical methodology for selecting neural networks in real-world edge environments, bridging the gap between theoretical performance and actual deployability on resource-constrained hardware such as the STM32 and ESP32.

TRUST-SDN: A Machine Learning-driven Trust Scoring Framework for Dependability-aware Routing in Mobile Ad-hoc Networks

Gade Sri Siri, Manas Kumar Yogi — Fri, 14 Aug 2026 00:00:00 +0000

Software-Defined Networking (SDN) decouples the control and data planes to enable programmable, centrally managed network behaviour, yet this same centralization concentrates dependability and compliance risk within a single control point. Existing dependability mechanisms for SDN deployments largely rely on static thresholds or binary intrusion-detection outputs that fail to capture the graded, time-machine learning-driven trust scoring framework that fuses behavioural telemetry, control-plane responsiveness, and compliance-adherence signals into a continuous, exponentially weighted trust score that drives dependability-aware and compliance-sensitive routing decisions. The framework comprises four cooperating modules: a Behavioural Telemetry Engine, a Composite Trust Aggregator, a Compliance-Aware Routing Engine, and a Dependability Regression Monitor. A formal boundedness theorem establishes that the proposed trust update rule remains stable within a closed interval under adversarial perturbation. Simulation on Mininet-based fat-tree topologies under varying adversarial loads demonstrates that TRUST-SDN improves detection accuracy, reduces mean time to recovery, and sustains markedly higher compliance-violation detection rates than static-threshold and Bayesian trust baselines, confirming its suitability for hyperconnected, compliance-sensitive future internet deployments.

Future of Artificial Intelligence and Cloud Computing Integration: Trends, Applications, Challenges, and Future Directions

Vaibhavi Sardar Patil, Siddhi Sunil Patil, Prathamesh Dinkar Patil — Fri, 14 Aug 2026 00:00:00 +0000

Artificial Intelligence (AI) and Cloud Computing have emerged as two of the most transformative technologies driving digital innovation across industries. Artificial Intelligence enables machines to perform intelligent tasks such as learning, reasoning, prediction, decision-making, and automation, while Cloud Computing provides scalable, on-demand computing resources through the Internet. The integration of these technologies enables organizations to develop intelligent applications without investing in expensive infrastructure and hardware. Cloud platforms provide storage, networking, virtual machines, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and AI development environments required for building and deploying machine learning models efficiently. Simultaneously, AI enhances cloud services by enabling intelligent automation, predictive analytics, cybersecurity monitoring, workload optimization, and real-time data processing. This integration has transformed various sectors, including healthcare, banking, finance, education, manufacturing, agriculture, transportation, retail, and smart cities. Despite offering numerous advantages such as scalability, cost efficiency, high performance, and business automation, AI and cloud integration also faces several challenges, including data privacy, cybersecurity threats, ethical concerns, regulatory compliance, infrastructure costs, and vendor dependency. Emerging technologies such as Explainable AI, Edge Computing, AI-as-a-Service, Green Cloud Computing, and Quantum Computing are expected to address these challenges and further accelerate digital transformation. This review article presents a comprehensive study of AI and Cloud Computing integration, including its architecture, applications, benefits, challenges, future trends, and research directions.