Kunal Telangi | Searcharxiv

arXiv2026

Black Box Cryptanalysis of AES128

This paper presents computational results of cryptanalysis of AES using the Local Inversion by Black Box computations of the forward encryption and utilizes these results to develop a practically feasible approach for the key recovery of the full scale AES128 under Known Plaintext Attack (KPA). It is shown that complete recovery of unknown key bits is possible upto $80$ bits in a practically feasible time and memory in random KPA situation by sequential computation when remaining $48$ bits are known. The results of key recovery in $64$, $72$ and $80$ bit unknown cases are extrapolated to predict the period of the iterative sequence generated in the local inversion approach for the full $128$ bit unknown key case and a strategy is proposed to search the actual period by brute force parallel search of the sequence period with $10$ free bits defining the search space. Then it is shown that the actual key can be verified in polynomial time by fast powering of the forward encryption map. Hence this strategy shows that the key recovery problem for AES128 under KPA has a chance of success in practically feasible time for a majority of the plaintexts. Local inversion approach to cryptanalysis using black box computations is a universal method applicable to a vast variety of key recovery and map inversion problems. Hence the results presented in this paper are representative of estimates of cryptanalysis of other ciphers which can be considered almost as strong as AES128 as encryption functions.

cs.CR↗

arXiv2025

Fractional Policy Gradients: Reinforcement Learning with Long-Term Memory

We propose Fractional Policy Gradients (FPG), a reinforcement learning framework incorporating fractional calculus for long-term temporal modeling in policy optimization. Standard policy gradient approaches face limitations from Markovian assumptions, exhibiting high variance and inefficient sampling. By reformulating gradients using Caputo fractional derivatives, FPG establishes power-law temporal correlations between state transitions. We develop an efficient recursive computation technique for fractional temporal-difference errors with constant time and memory requirements. Theoretical analysis shows FPG achieves asymptotic variance reduction of order O(t^(-alpha)) versus standard policy gradients while preserving convergence. Empirical validation demonstrates 35-68% sample efficiency gains and 24-52% variance reduction versus state-of-the-art baselines. This framework provides a mathematically grounded approach for leveraging long-range dependencies without computational overhead.

cs.LG↗