摘要
近年来,数据平台与系统的规模飞速扩张,性能快速提升,安全性能也随之越发重要。现有的基于深度学习的恶意行为检测方案缺少与模型契合的优化算法,导致模型缺乏自优化能力。提出了一种基于改进萤火虫算法与改进长短期记忆网络的恶意行为检测方法iFA-LSTM,该方法可以有效地进行恶意行为的二分类检测。通过UNSW-NB15数据集来验证所提出的方法,方法在单攻击二分类实验中的平均识别准确率达到了99.56%,且在混合攻击二分类实验中平均识别准确率也达到了98.79%,同时也充分证明了iFA的有效性。所提出的方法可以快速有效地进行恶意行为检测,非常有希望应用于恶意行为的安全监控和识别。
In recent years,the scale and performance of data platforms and systems have expanded rapidly,making security performance increasingly critical.Existing malicious behavior detection schemes based on deep learning lack optimization algorithms tailored to the models,resulting in a lack of self-optimization capabilities.This paper proposes a malicious behavior detection method called iFA-LSTM(improved firefly algorithm and improved long short-term memory network),which leverages an improved firefly algorithm and an improved LSTM network to effectively perform binary classification detection of malicious behaviors.The proposed method is validated using the UNSW-NB15 dataset.In single-attack binary classification experiments,the method achieves an average recognition accuracy of 99.56%,while in mixed-attack binary classification experiments,the average recognition accuracy reaches 98.79%.Additionally,the iFA fully demonstrates its effectiveness.The proposed method can detect malicious behaviors quickly and effectively,holding great promise for application in security monitoring and recognition of malicious behaviors.
作者
沈凡凡
汤星译
张军
徐超
陈勇
何炎祥
SHEN Fan-fan;TANG Xing-yi;ZHANG Jun;XU Chao;CHEN Yong;HE Yan-xiang(School of Computer Science(School of Intelligence Audit),Nanjing Audit University,Nanjing 211815;School of Software,East China University of Technology,Nanchang 330013;School of Computer Science,Wuhan University,Wuhan 430072,China)
出处
《计算机工程与科学》
CSCD
北大核心
2024年第12期2158-2170,共13页
Computer Engineering & Science
基金
国家自然科学基金(61902189,6242227,71972102,62162002,61972293)
江苏省高等学校基础科学(自然科学)研究项目(22KJA520004)
江西省自然科学基金(20212BAB202002)
江苏省研究生科研与实践创新计划项目(SJCX23_1101)。
关键词
平台与系统安全
恶意行为检测
神经网络
算法优化
二分类
platform and system security
malicious behavior detection
neural network
algorithm optimization
binary classification