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Birla Institute of Technology

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Research library11linked papers
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Selected work

Representative Papers

Hybrid Topological Data Analysis and LSTM Networks for Enhanced Network Intrusion Detection Using CIC-IDS2017 Dataset

Jun 30, 2026

This study addresses the challenge of detecting sophisticated cyber threats—such as DDoS attacks, brute-force attempts, and web-based intrusions—by proposing a novel intrusion detection approach that integrates topological data analysis (TDA) with long short-term memory (LSTM) networks. The method leverages persistent homology to extract topological features from network traffic, including Betti curves and persistence diagrams, and for the first time embeds these features into a deep learning framework where they are complementarily fused with LSTM-captured temporal dependencies. Evaluated on the CIC-IDS2017 dataset using five-fold cross-validation, the proposed model achieves perfect performance with both AUC and F1-score of 1.000, significantly outperforming baseline models such as TDA combined with random forest and isolation forest, thereby demonstrating the efficacy and superiority of jointly modeling topological and temporal characteristics for intrusion detection.

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Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

Jun 28, 2026

This study addresses the challenge of stock return prediction in non-stationary financial markets by proposing an adaptive Financial Transformer model. The architecture incorporates a market state encoder and an adaptive gating attention mechanism to dynamically integrate eleven semantically grouped financial indicators, thereby modulating temporal modeling according to prevailing market conditions. A finance-aware composite objective function is introduced to jointly optimize prediction error, directional accuracy, and the Sharpe ratio, while correcting sequence alignment bias commonly present in backtesting procedures. Empirical evaluations demonstrate that the proposed method achieves competitive predictive performance across multiple stocks, reduces model complexity by 15.2%, and significantly enhances both parameter efficiency and the interpretability of trading signals.

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Taming the Black Swan: A Momentum-Gated Hierarchical Optimisation Framework for Asymmetric Alpha Generation

Apr 10, 2026

This study addresses the vulnerability of traditional momentum strategies to the “winner’s curse,” which often leads to severe drawdowns during market reversals. To mitigate this issue, the authors propose the AEGIS framework, which innovatively decouples momentum signals from risk exposure. The approach employs a volatility-adjusted momentum filter to identify robust trends, integrates a minimax correlation algorithm for structured diversification, and dynamically optimizes the Sortino ratio via sequential least squares programming (SLSQP) to adapt to varying market regimes. Backtesting over a 20-year period demonstrates that the strategy matches the returns of the Nasdaq-100 while significantly reducing downside volatility and generating substantial alpha relative to the S&P 500, thereby achieving dual objectives: efficient participation in bull markets and effective drawdown control in bear markets.

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Recent publications

Latest Papers

Hybrid Topological Data Analysis and LSTM Networks for Enhanced Network Intrusion Detection Using CIC-IDS2017 Dataset

Jun 30, 2026

This study addresses the challenge of detecting sophisticated cyber threats—such as DDoS attacks, brute-force attempts, and web-based intrusions—by proposing a novel intrusion detection approach that integrates topological data analysis (TDA) with long short-term memory (LSTM) networks. The method leverages persistent homology to extract topological features from network traffic, including Betti curves and persistence diagrams, and for the first time embeds these features into a deep learning framework where they are complementarily fused with LSTM-captured temporal dependencies. Evaluated on the CIC-IDS2017 dataset using five-fold cross-validation, the proposed model achieves perfect performance with both AUC and F1-score of 1.000, significantly outperforming baseline models such as TDA combined with random forest and isolation forest, thereby demonstrating the efficacy and superiority of jointly modeling topological and temporal characteristics for intrusion detection.

0 citationsRead paper

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

Jun 28, 2026

This study addresses the challenge of stock return prediction in non-stationary financial markets by proposing an adaptive Financial Transformer model. The architecture incorporates a market state encoder and an adaptive gating attention mechanism to dynamically integrate eleven semantically grouped financial indicators, thereby modulating temporal modeling according to prevailing market conditions. A finance-aware composite objective function is introduced to jointly optimize prediction error, directional accuracy, and the Sharpe ratio, while correcting sequence alignment bias commonly present in backtesting procedures. Empirical evaluations demonstrate that the proposed method achieves competitive predictive performance across multiple stocks, reduces model complexity by 15.2%, and significantly enhances both parameter efficiency and the interpretability of trading signals.

0 citationsRead paper

Taming the Black Swan: A Momentum-Gated Hierarchical Optimisation Framework for Asymmetric Alpha Generation

Apr 10, 2026

This study addresses the vulnerability of traditional momentum strategies to the “winner’s curse,” which often leads to severe drawdowns during market reversals. To mitigate this issue, the authors propose the AEGIS framework, which innovatively decouples momentum signals from risk exposure. The approach employs a volatility-adjusted momentum filter to identify robust trends, integrates a minimax correlation algorithm for structured diversification, and dynamically optimizes the Sortino ratio via sequential least squares programming (SLSQP) to adapt to varying market regimes. Backtesting over a 20-year period demonstrates that the strategy matches the returns of the Nasdaq-100 while significantly reducing downside volatility and generating substantial alpha relative to the S&P 500, thereby achieving dual objectives: efficient participation in bull markets and effective drawdown control in bear markets.

0 citationsRead paper