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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
New Hashing Algorithm for Use in TCP Reassembly Module of...
Sankalp Bagaria · 2015-01-09 · via cs.DS updates on arXiv.org

Since last decade, IDS/ IPS has gained popularity in protecting large networks. They can employ signature based techniques and/or flow-based techniques to prevent intrusion from outside/ inside the network they are trying to protect. Signature based IDS/ IPS can be stateless or stateful. Stateful IDS can store the state of the protocol and use it for better detection of malware. In the case of TCP/IP networks, an attacker can also launch an attack such that the malicious code is distributed over many packets. These packets pass through the traditional IDS/ IPS and reassemble inside the network. Once re-assembled inside the network by the TCP/IP layer, the malicious code launches an attack. The TCP state and a copy of last few packets for each active connection has to be maintained in IDS/IPS. In TCP re-assembly, packets are re-assembled at IDS/IPS and searched for signature matches. A connection table has to be maintained for active connections and their list of last few (atmost 11) packets that have already arrived. We need data structures for searching the connection that the latest incoming packet belongs to. Popular hashing algorithms like CRC, XOR, summing tuple, taking modulus are inefficient as hash keys are not evenly distributed in hash-key space. Thus we show how an algorithm based on cryptography concepts can be used for efficient hashing in network connection management. We also show how to use full four tuple for calculating hash key instead of simply summing the tuple and taking the modulus of the sum.