.png){kind=icon}
返回
On Feature Selection Algorithms for Effective Botnet Detection
.png){kind=icon}
OA
AI
DOI：10.1007/s10922-024-09817-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The threats of botnets are becoming a growing concern infecting more and more computers every day. Although botnets can be detected from their behavioral patterns, it is becoming more challenging to differentiate the behavior between the malicious traffic and the legitimate traffic as with the advancement of the technologies the malicious traffics are following the similar behavioral patterns of benign traffics. The detection of malicious traffic largely depends on the traffic features that are being used to feed in the detection process. Selecting the best features for effective botnet detection is the main contribution of this paper. At the very beginning, we show the impact of different features on botnet detection process. Then we propose several heuristics to select the best features from a handful of possible features. Some proposed heuristics are truly feature-based and some are group-based, thus generating different accuracy levels. We also analyze time complexity of each heuristic and provide a detailed performance analysis. As working with all combinations of a large number of features is not feasible, some heuristics work by grouping the features based on their similarity in patterns and checking all combinations within the groups of small number of features which improves the time complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows that some heuristics outperform state-of-the-art feature selection algorithms.
Keyword:
BOTNET
Decision tree
Machine learning
Feature
期刊
IF:
3.9
论文数:
1.0K
被引数:
1.3K
机构
引用论文
On Consistency between Lagrange and Hamilton Formalisms in Quantum Mechanics论量子力学中拉格朗日形式与哈密顿形式的一致性OAAI
OAAI分享收藏Construction of infrared finite observables inN=4super Yang-Mills theory在N=4超杨-米尔斯理论中红外有限可观测量的构造
OAAI
OAAI分享收藏Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review物联网应用、安全挑战、攻击、入侵检测和未来展望: 系统综述.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Can we measure the number of quark flavors without building an ultrahigh-energy accelerator?我们能否在不建造超高速加速器的情况下测量夸克味道的数量？
PREAI
分享收藏
查看更多内容