P2P Botnet检测框架的研究与分析

botnet;
bot;
centralized;
decentralized;
P2P;
4星 · 超过85%的资源 需积分: 3 2 下载量 184 浏览量 更新于2024-10-01 收藏 304KB PDF 举报
"A Proposed Framework for P2P Botnet Detection" 在网络安全领域，Botnet（僵尸网络）是最普遍且危害极大的攻击形式之一，对网络资产和组织财产构成严重威胁。Botnet是由被黑客控制的计算机（Bots）集合，这些计算机在BotMaster的统一指挥与控制（C&C）架构下远程运行。Botnet被用来执行各种恶意活动，包括分布式拒绝服务（DDoS）攻击、垃圾邮件发送以及网络钓鱼等。 现有的大多数Botnet检测方法主要关注特定的C&C协议（如IRC、HTTP）和集中式结构。然而，这种专注于单一协议和结构的方法存在局限性，因为Botnet会不断演变其结构和C&C技术以逃避检测。针对这一问题，论文提出了一种新的检测框架，专注于点对点（P2P）Botnet的识别。 这个提议的框架建立在我们对Botnet的定义之上。我们认为，Botnet是一个由一组行为相似、通信模式和恶意活动模式一致的Bot组成的网络。在提出的框架中，我们强调了识别Bot之间共享的通信模式和活动模式，这有助于检测P2P Botnet，因为P2P结构允许Bot直接相互交互，而不是通过中心服务器进行控制。 为了实现这一框架，首先需要深入理解P2P网络的工作原理，包括其节点之间的连接方式、数据交换的机制以及可能的匿名性策略。接着，可以采用机器学习算法来分析网络流量，识别异常的通信模式，比如异常的频繁连接、大量数据传输或特定时间的行为模式。此外，监测和分析网络中的异常行为，例如大量相同的请求或响应，也是识别Botnet的关键。 为了提高检测效率和准确性，我们可以利用网络流量分析、协议解析、行为建模等多种技术结合的方式。同时，考虑到P2P Botnet的动态性，该框架需要具备一定的自适应性，能够随着Botnet行为的变化而更新检测策略。 这个P2P Botnet检测框架旨在提供一个全面的解决方案，以应对不断演化的Botnet威胁。通过研究P2P网络特性，结合先进的数据分析和行为分析技术，能够更好地检测和防范这类难以捉摸的网络攻击，保护网络安全。

A Deep Learning Approach to Network Intrusion Detection

2019-12-09 上传
This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature ...

A Shared-Subspace Learning Framework for Multi-Label Classification

2016-03-01 上传
In this paper, we consider a general framework for ex- tracting shared structures in multi-label classification. In this framework, a common subspace is assumed to be shared among multiple labels. We...

Undefined control sequence. ...aper proposed a novel framework, as \cref

2023-08-11 上传
根据提供的部分语句："...aper proposed a novel framework, as \cref"，猜测可能出现错误的部分是"\cref"。如果这是一个自定义命令或控制序列，你需要确保它已正确定义。此外，也要确保使用了正确的宏包来支持该...

Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection

2023-04-23 上传
Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection is a research paper that proposes a new approach to detect anomalies in spatiotemporal data using graph convolutional ...

Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles

2023-05-23 上传
Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles is a research paper that proposes a framework for offloading video surveillance tasks in an ...

Fine-Grained Feature Enhancement for Object Detection in Remote Sensing Images

2023-04-04 上传
Object detection in remote sensing images is a challenging task due to the complex backgrounds, diverse object shapes and sizes, and varying imaging conditions. To address these challenges, fine-...

Abstract—In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented GaleShapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations.中文

2023-07-22 上传
在异构网络（HetNets）中，用户关联方法应该能够实现基站（BS）之间的负载平衡。本文研究了在受限于回程链路的HetNets中，用户关联和资源分配的联合优化问题，以增强系统容量。我们考虑了HetNets中的两个主要限制：...

一篇关于SSM的外文文献原文

2023-05-23 上传
In this paper, we proposed a design and implementation of an online shopping system based on the SSM framework. The system adopts a three-tier architecture, which includes presentation layer, service ...

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

2023-05-24 上传
4 实验 本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。然后，在第4.3节中，将IFCS-MOEA/D-DE与19个测试问题上的五种最先进的MOEA进行比较...

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

2023-02-27 上传
本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及...
sharptime
  • 粉丝: 2
  • 资源: 9
上传资源 快速赚钱

最新资源