在数学,统计学和计算机科学中,尤其是在机器学习和逆问题中,正则化是添加信息以解决不适定问题或防止过度拟合的过程。 正则化适用于不适定的优化问题中的目标函数。

VIP内容

题目: CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data

摘要:

本文研究了利用光谱聚类方法对多尺度数据进行聚类的问题。传统的光谱聚类技术通过处理一个反映物体接近度的相似矩阵来发现聚类。对于多尺度数据,基于距离的相似度是无效的,因为稀疏聚类的对象可能相距很远,而密集聚类的对象必须足够近。可以通过将物体的“可达相似性”概念与给定的基于距离的相似性相结合,得到物体的系数矩阵,解决了多尺度数据的光谱聚类问题。本文提出了利用轨迹套索对系数矩阵进行正则化的算法CAST。证明了所得到的系数矩阵具有“分组效应”和“稀疏性”。我们表明,这两个特征意味着非常有效的光谱聚类。我们评估CAST和其它10种聚类方法在广泛的数据集w.r.t.各种应用。实验结果表明,该算法在多尺度数据的测试用例中具有良好的鲁棒性。

成为VIP会员查看完整内容
0
14

最新内容

The recent advancements in machine learning have led to a wave of interest in adopting online learning-based approaches for long-standing attack mitigation issues. In particular, DDoS attacks remain a significant threat to network service availability even after more than two decades. These attacks have been well studied under the assumption that malicious traffic originates from a single attack profile. Based on this premise, malicious traffic characteristics are assumed to be considerably different from legitimate traffic. Consequently, online filtering methods are designed to learn network traffic distributions adaptively and rank requests according to their attack likelihood. During an attack, requests rated as malicious are precipitously dropped by the filters. In this paper, we conduct the first systematic study on the effects of data poisoning attacks on online DDoS filtering; introduce one such attack method, and propose practical protective countermeasures for these attacks. We investigate an adverse scenario where the attacker is "crafty", switching profiles during attacks and generating erratic attack traffic that is ever-shifting. This elusive attacker generates malicious requests by manipulating and shifting traffic distribution to poison the training data and corrupt the filters. To this end, we present a generative model MimicShift, capable of controlling traffic generation while retaining the originating regular traffic's intrinsic properties. Comprehensive experiments show that online learning filters are highly susceptible to poisoning attacks, sometimes performing much worse than a random filtering strategy in this attack scenario. At the same time, our proposed protective countermeasure effectively minimizes the attack impact.

0
0
下载
预览

最新论文

The recent advancements in machine learning have led to a wave of interest in adopting online learning-based approaches for long-standing attack mitigation issues. In particular, DDoS attacks remain a significant threat to network service availability even after more than two decades. These attacks have been well studied under the assumption that malicious traffic originates from a single attack profile. Based on this premise, malicious traffic characteristics are assumed to be considerably different from legitimate traffic. Consequently, online filtering methods are designed to learn network traffic distributions adaptively and rank requests according to their attack likelihood. During an attack, requests rated as malicious are precipitously dropped by the filters. In this paper, we conduct the first systematic study on the effects of data poisoning attacks on online DDoS filtering; introduce one such attack method, and propose practical protective countermeasures for these attacks. We investigate an adverse scenario where the attacker is "crafty", switching profiles during attacks and generating erratic attack traffic that is ever-shifting. This elusive attacker generates malicious requests by manipulating and shifting traffic distribution to poison the training data and corrupt the filters. To this end, we present a generative model MimicShift, capable of controlling traffic generation while retaining the originating regular traffic's intrinsic properties. Comprehensive experiments show that online learning filters are highly susceptible to poisoning attacks, sometimes performing much worse than a random filtering strategy in this attack scenario. At the same time, our proposed protective countermeasure effectively minimizes the attack impact.

0
0
下载
预览
Top