arXiv subjects

Victor Kebande

Publications and source records attributed to Victor Kebande.

11 recordsLinked to original sources

arXiv2026

Mitigating 51% Attacks in Blockchain Systems Through Early Detection and Checkpoint-Based Defense

The 51% attack remains one of the significant security concern in Proof-of-Work (PoW) blockchains, where increasing hash-power concentration can create a malicious majority-control risk and enable adversarial chain reorganization. This paper proposes a two-layer defense that combines early hash-power monitoring with checkpoint-based mitigation. The first layer monitors mining-power concentration and provides an early warning before the critical majority-control threshold is reached. The second layer uses checkpointing to restrict the depth of accepted chain reorganizations. Monte Carlo simulations are used to evaluate both mechanisms under different attack scenarios. Across 1,000 simulation runs, a 45% warning threshold provided a mean warning-to-critical lead time of 10.531 minutes before the modeled 50% critical threshold was reached. At the selected checkpoint depth of N = 6, approximately 63.68% of simulated reorganization attempts were rejected, while the mean reorganization-depth outcome decreased from 7.948 to 1.634 blocks, representing an approximately 79.4% reduction. The results demonstrate that early detection and checkpoint-based mitigation provide complementary mechanisms for reducing the potential impact of 51% attacks under the evaluated conditions.

cs.CR↗

arXiv2026

The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble

The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large language models (LLMs), large-scale compute infrastructures, and autonomous reasoning systems. However, the rapid acceleration of AI has increasingly shown technological, societal, economic, ethical and infrastructural challenges associated with peak data limitations, rising computational demands, synthetic data recursion, valuation inflation, and societal instability. The traditional scaling paradigms that have powered the modern AI systems are gradually encountering friction in sustaining continuous exponential growth. This paper views ``the end of AI exponentiation,'' thus exploring how it flutters inside and outside the bubble, where instability emerges within the AI ecosystem through compute and data-center races, speculative investments, and the rat-race toward superintelligence, and outside the ecosystem through labor disruption, governance concerns, public uncertainty, and geopolitical acceleration surrounding future intelligent systems and infrastructures globally.

cs.AI↗

arXiv2026

Agentic AI Cybersecurity Framework

The increasing scale, complexity, and dynamism of modern cyber threats have rendered traditional reactive cybersecurity mechanisms insufficient. This paper introduces an Agentic AI Cybersecurity Framework (AACF) designed to enable autonomous, goal-driven, and adaptive cyber defense operations. Unlike conventional systems that rely on predefined rules and human intervention, the proposed framework leverages agentic artificial intelligence to perceive environmental states, reason about potential threats, and execute context-aware responses with minimal supervision. The framework is structured into key functional layers, including perception, reasoning, decisionmaking, action execution, and feedback-driven learning, enabling continuous adaptation to evolving attack patterns. By integrating intelligent agents with real-time data analysis and automated response mechanisms, AACF supports proactive threat detection, dynamic risk assessment, and coordinated mitigation strategies across distributed environments. A conceptual architecture is presented, along with illustrative use cases demonstrating its applicability to intrusion detection, incident response, and autonomous security orchestration. The proposed framework contributes to the emerging paradigm of self-directed cybersecurity systems and provides a foundation for developing resilient, scalable, and intelligent defense infrastructures.

cs.CR↗

arXiv2026

AoNT Trap: Borromean-Entangled Mutable Chameleon Trapdoor Hash All-or-Nothing Stream Cipher

This work introduces the Borromean-Entangled Chameleon Trapdoor Hash All-or-Nothing (AoNT) Stream Cipher (BEC-Trap), a novel construction that merges Borromean interdependence, trapdoor-enabled mutability, and streaming encryption into a unified framework. The (BEC-Trap) cipher links key (K), initialization vector (V ), and internal state (St) in a Borromean structure, ensuring that breaking, guessing, or removing any one component collapses the entire keystream, providing a computational (AoNT) interdependence under standard cryptographic assumptions. A chameleon trapdoor hash is integrated to permit controlled collisions, enabling seamless rekeying, (V ) refresh, and state rotation without resynchronizing endpoints. This design provides confidentiality, forward secrecy, and adaptive key management with low computational overhead, making it suitable for high-throughput secure messaging, IoT communications, and privacy-preserving blockchain channels. Security analysis of the (BEC-Trap) shows that the construction is resistant to key-recovery attacks, state compromise, and desynchronization attempts, delivering a robust cryptographic primitive for next-generation secure communications.

cs.CR↗

arXiv2026

A 12-Step Process for Industrial Internet of Things (IIoT) Forensics

The increasing deployment of the Industrial Internet of Things (IIoT) in critical infrastructure sectors like manufacturing, healthcare, and transportation has shown new challenges for Digital Forensics (DF). Traditional DF methodologies are not well equipped to handle the complexity, scale, and heterogeneity of IIoT environments. This paper introduces a comprehensive Twelve-Step Process (TSP) tailored specifically for IIoT incidents, addressing the need for effective investigation and Potential Digital Evidence (PDE) handling in such dynamic environments in DF. We begin by exploring the importance of IIoT and its role in industrial ecosystems, followed by an examination of existing DF challenges. Each step of the process, from forensic readiness to investigation closure, is designed to ensure robust PDE collection, analysis, and legal compliance to increase chances of admissibility from a DF scenario

cs.CR↗

arXiv2026

Structural Analysis of Cryptographic Sequences using Stringology-Based Fingerprinting

Cryptographic primitives such as stream ciphers,Pseudorandom Number Generators (PRNGs), and block cipher modes produce sequences that are designed to be statistically indistinguishable from random data. As a result, the traditional evaluation techniques therefore rely primarily on statistical randomness tests to assess the quality of generated sequences. While these tests verify global statistical properties, they do not address whether structural characteristics of sequences can reveal information about the underlying generator. In this paper, we introduce a stringology-based fingerprinting, (SBF) framework for the structural analysis of cryptographic sequences. The proposed SBF framework interprets cryptographic outputs as symbolic strings and applies pattern-based feature extraction to capture structural statistics such as substring frequency distributions, recurrence patterns, and entropy characteristics. These structural features are aggregated into fingerprint vectors that characterize sequence generators. The experimental evaluation is conducted using datasets composed of Cipher-Generated Sequences (CGS) and Uniformly Random Sequences (URS). The results demonstrate that stringology-based pattern analysis can reveal measurable structural signatures across different sequence sources. Although these signals do not imply practical cryptographic weaknesses, they provide an additional analytical perspective for evaluating the structural behavior of cryptographic generators.

cs.CR↗

arXiv2026

Stringology Based Cryptology

The modern cryptographic primitives are known to generate large volumes of sequential data like keystreams, ciphertext blocks, and hash outputs. Traditional cryptgraphic evaluation methods rely primarily on statistical randomness tests and algebraic cryptanalysis techniques. This paper introduces the concept of Stringology-Based Cryptology (SBC), which applies classical string processing and pattern matching techniques to analyze structural properties of cryptographic outputs. By interpreting cryptographic outputs as symbolic sequences, stringology algorithms can be used to detect pattern recurrence, substring distributions, and structural correlations. In addition, the paper demonstrate how pattern frequency analysis and substring recurrence metrics can be applied to evaluate keystream outputs generated by stream ciphers. Experimental results illustrate that SBC analysis provides complementary insights into structural characteristics of cryptographic sequences and may support future research in structural cryptanalysis and cryptographic evaluation

cs.CR↗

arXiv2026

Neural Stringology Based Cryptanalysis of EChaCha20

Modern stream ciphers rely on strong diffusion and pseudorandom keystream generation (PKG) to resist cryptanalysis. While conventional evaluation methods such as statistical randomness tests and differential analysis provide important security assurances, they may fail to detect localized structural patterns embedded within cipher outputs. In this paper, a Neural Stringology Cryptanalysis (NSC) framework that combines classical string pattern analysis with machine learning techniques to investigate potential structural anomalies in stream cipher keystreams is introduced. The proposed approach first applies stringology-inspired feature extraction methods such as m-gram frequency analysis, substring recurrence detection, and positional pattern statistics aligned with the internal operations of Add-Rotate-XOR (ARX) based stream ciphers. These extracted features are then analyzed using a neural learning model to identify deviations from expected random behavior and to detect subtle structural patterns that may not be captured by traditional statistical tests. Experimental evaluation is conducted on keystream outputs generated by the EChaCha20 stream cipher under multiple configurations, including reduced round variants. The results demonstrate that the proposed NSC framework can identify distinguishable structural characteristics in the keystream data under controlled conditions, suggesting that integrating machine learning with stringology-based analysis provides a promising complementary methodology for evaluating the structural robustness of modern ARX-based stream cipher designs.

cs.CR↗

arXiv2026

Stringology-Based Cryptanalysis for EChaCha20 Stream Cipher

Stringology-Based Cryptanalysis (SBC) offers a suitable and a structurally aligned approach for uncovering structural patterns in stream ciphers that traditional statistical tests may often fail to detect. Despite \texttt{EChaCha20}'s design enhancements, no systematic investigation has been performed to determine whether its expanded 6$\times$6 state matrix and modified Quarter-Round Function (\texttt{QR-F}) introduce subtle keystream patterns, rotational biases, or partial collisions that could serve as statistical distinguishers. As such, addressing this gap is critical to ensure that the cipher's modifications do not unintentionally reduce its security margin. Therefore, this paper leverages Knuth-Morris-Pratt (\texttt{KMP}) and Boyer-Moore (\texttt{BM}) algorithms to analyze \texttt{EChaCha20}, which is a variant of ChaCha20 that features an expanded 6$\times$6 state matrix and an enhanced \texttt{QR-F}. The author has developed and optimized adaptations of the \texttt{KMP} and \texttt{BM} algorithms for 32-bit word level pattern analysis and employed them to investigate $m$-bit pattern frequency distributions to assess the \texttt{EChaCha20}'s resistance of rotational-differential attacks. Our experimental results on large-scale one million keystream datasets have confirmed that \texttt{EChaCha20} is able to maintain strong pseudorandomness at 16-bit and 32-bit levels with minor irregularities observed in the 8-bit domain. In addition to these, the differential tests have indicated a rapid diffusion, exhibiting an avalanche effect after two \texttt{QR-F} rounds and no statistically significant rotational collisions were observed within the evaluated bounds, consistent with expected ARX diffusion behavior beyond 3 rounds. This work puts forward SBC as a complementary tool for ARX cipher evaluation and provide new thoughts on the security properties of \texttt{EChaCha20}.

cs.CR↗

arXiv2024

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data privacy. These assertions have been supported by three experiments that have significantly leveraged three manually validated datasets aimed at detecting and examining different code smell scenarios. In experiment 1, which was concerned with a centralized training experiment, dataset two achieved the lowest accuracy (92.30%) with fewer smells, while datasets one and three achieved the highest accuracy with a slight difference (98.90% and 99.5%, respectively). This was followed by experiment 2, which was concerned with cross-evaluation, where each ML model was trained using one dataset, which was then evaluated over the other two datasets. Results from this experiment show a significant drop in the model's accuracy (lowest accuracy: 63.80\%) where fewer smells exist in the training dataset, which has a noticeable reflection (technical debt) on the model's performance. Finally, the last and third experiments evaluate our approach by splitting the dataset into 10 companies. The ML model was trained on the company's site, then all model-updated weights were transferred to the server. Ultimately, an accuracy of 98.34% was achieved by the global model that has been trained using 10 companies for 100 training rounds. The results reveal a slight difference in the global model's accuracy compared to the highest accuracy of the centralized model, which can be ignored in favour of the global model's comprehensive knowledge, lower training cost, preservation of data privacy, and avoidance of the technical debt problem.

cs.SE↗

arXiv2023

Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study

This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

cs.LG↗