惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
博客园 - 【当耐特】
V
Visual Studio Blog
GbyAI
GbyAI
V
V2EX
P
Proofpoint News Feed
Microsoft Azure Blog
Microsoft Azure Blog
Microsoft Security Blog
Microsoft Security Blog
D
DataBreaches.Net
Hugging Face - Blog
Hugging Face - Blog
A
About on SuperTechFans
The Cloudflare Blog
阮一峰的网络日志
阮一峰的网络日志
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
N
Netflix TechBlog - Medium
aimingoo的专栏
aimingoo的专栏
B
Blog RSS Feed
量子位
MongoDB | Blog
MongoDB | Blog
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
Stack Overflow Blog
Stack Overflow Blog
小众软件
小众软件

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
On DDoS Attack Related Minimum Cut Problems
Qi Duan, Haadi Jafarian, Ehab Al-Shaer, Jinhui Xu · 2014-12-11 · via cs.DS updates on arXiv.org

In this paper, we study two important extensions of the classical minimum cut problem, called {\em Connectivity Preserving Minimum Cut (CPMC)} problem and {\em Threshold Minimum Cut (TMC)} problem, which have important applications in large-scale DDoS attacks. In CPMC problem, a minimum cut is sought to separate a of source from a destination node and meanwhile preserve the connectivity between the source and its partner node(s). The CPMC problem also has important applications in many other areas such as emergency responding, image processing, pattern recognition, and medical sciences. In TMC problem, a minimum cut is sought to isolate a target node from a threshold number of partner nodes. TMC problem is an important special case of network inhibition problem and has important applications in network security. We show that the general CPMC problem cannot be approximated within $logn$ unless $NP=P$ has quasi-polynomial algorithms. We also show that a special case of two group CPMC problem in planar graphs can be solved in polynomial time. The corollary of this result is that the network diversion problem in planar graphs is in $P$, a previously open problem. We show that the threshold minimum node cut (TMNC) problem can be approximated within ratio $O(\sqrt{n})$ and the threshold minimum edge cut problem (TMEC) can be approximated within ratio $O(\log^2{n})$. \emph{We also answer another long standing open problem: the hardness of the network inhibition problem and network interdiction problem. We show that both of them cannot be approximated within any constant ratio. unless $NP \nsubseteq \cap_{δ>0} BPTIME(2^{n^δ})$.