基于改进的非洲秃鹰优化算法的DDoS攻击检测研究：Sin-Cos-bIAVOA方法及其性能比较分析,分布式拒绝服务攻击（DDOS）lunwen复现实验复现 Matlab代码Sin-Cos-bIAV
资源内容介绍
基于改进的非洲秃鹰优化算法的DDoS攻击检测研究：Sin-Cos-bIAVOA方法及其性能比较分析,分布式拒绝服务攻击（DDOS）lunwen复现实验复现 Matlab代码Sin-Cos-bIAVOA: A new feature selection method based on improved African vulture optimization algorithm and a novel transfer function to DDoS attack detection一种基于改进的非洲秃鹰优化算法的一种新的特征选择方法和一种新的DDoS攻击检测传递函数提出了一种二元改进的非洲秃鹰优化算法（Sin-Cos-bIAVOA）来选择DDoS攻击的有效特征。该方法采用一种新的化合物传递函数（Sin-Cos）来增加探索量。该方法采用引力固定半径最近邻（GFRNN）作为分类器，以选择特征的最优子集。并在数据集上对DDoS攻击检测的性能进行了比较。实验结果表明，与CIC-DDOS2019数据集的竞争对手相比，平均准确率（99.9979%）、精度（999.9979%）、查 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90341908/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90341908/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">针对提供的主题<span class="ff2">，</span>下面是一篇围绕分布式拒绝服务攻击<span class="ff2">（<span class="ff3">DDoS</span>）</span>和<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>特征选择方法</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">的文章<span class="ff2">：</span></div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">分布式拒绝服务攻击<span class="ff2">（<span class="ff3">DDoS</span>）</span>及其基于<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>的特征选择方法复现实验</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>引言</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">分布式拒绝服务攻击<span class="ff2">（<span class="ff3">DDoS</span>）</span>是一种常见的网络攻击手段<span class="ff2">，</span>其目的是通过大量合法的请求拥塞目标服</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">务器<span class="ff2">，</span>使其无法正常处理合法的请求<span class="ff2">，</span>从而使得服务不可用<span class="ff4">。</span>针对<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击的检测和防御已经成为</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">网络安全领域的重要研究方向<span class="ff4">。</span>本文将介绍一种基于改进的非洲秃鹰优化算法<span class="ff2">（<span class="ff3">Sin-Cos-bIAVOA</span>）</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">的特征选择方法<span class="ff2">，</span>以及其在新一代<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击检测中的应用<span class="ff4">。</span></div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、<span class="ff3">DDoS<span class="_ _1"> </span></span></span>攻击及检测</div><div class="t m0 x1 h2 ya ff3 fs0 fc0 sc0 ls0 ws0">DDoS<span class="_ _1"> </span><span class="ff1">攻击通过大量伪造或合法的请求<span class="ff2">，</span>使目标服务器过载<span class="ff2">，</span>从而无法响应正常的请求<span class="ff4">。</span>为了有效检</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">测<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击<span class="ff2">，</span>需要从网络流量中提取出有效的特征<span class="ff4">。</span>这些特征可以帮助我们区分正常的网络流量和</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">恶意流量<span class="ff4">。</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、<span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span></span>特征选择方法</div><div class="t m0 x1 h2 ye ff3 fs0 fc0 sc0 ls0 ws0">Sin-Cos-bIAVOA<span class="_ _1"> </span><span class="ff1">是一种基于改进的非洲秃鹰优化算法的特征选择方法<span class="ff4">。</span>该方法通过改进算法的搜</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">索策略和传递函数<span class="ff2">，</span>以更有效地选择<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击的有效特征<span class="ff4">。</span>具体来说<span class="ff2">，</span>该方法采用一种新的二元改</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">进的非洲秃鹰优化算法<span class="ff2">（<span class="ff3">Sin-Cos-bIAVOA</span>），</span>以寻找最优的特征子集<span class="ff4">。</span>此外<span class="ff2">，</span>该方法还采用了一种</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">新的化合物传递函数<span class="ff2">（<span class="ff3">Sin-Cos</span>），</span>以增加算法的探索量<span class="ff4">。</span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff4">、</span>实验复现与<span class="_ _0"> </span><span class="ff3">Matlab<span class="_ _1"> </span></span>代码实现</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">为了验证<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>特征选择方法的有效性<span class="ff2">，</span>我们进行了复现实验<span class="ff4">。</span>我们使用<span class="_ _0"> </span><span class="ff3">CIC-</span></div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">DDOS2019<span class="_ _1"> </span><span class="ff1">数据集<span class="ff2">，</span>该数据集包含了大量的<span class="_ _0"> </span></span>DDoS<span class="_ _1"> </span><span class="ff1">攻击和正常网络流量的数据<span class="ff4">。</span>我们使用<span class="_ _0"> </span></span>Matlab<span class="_ _1"> </span><span class="ff1">编</span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">写了相应的代码<span class="ff2">，</span>实现了<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>算法<span class="ff2">，</span>并使用了<span class="_ _0"> </span><span class="ff3">GFRNN<span class="ff2">（</span></span>引力固定半径最近邻<span class="ff2">）</span>作为分</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">类器<span class="ff2">，</span>以选择特征的最优子集<span class="ff4">。</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">五<span class="ff4">、</span>实验结果与分析</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">通过实验<span class="ff2">，</span>我们发现<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>算法能够有效地从网络流量中提取出<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击的有效特征</div><div class="t m0 x1 h2 y19 ff4 fs0 fc0 sc0 ls0 ws0">。<span class="ff1">与传统的特征选择方法相比<span class="ff2">，<span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span></span>算法能够更好地平衡探索和开发<span class="ff2">，</span>从而找到更优</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">的特征子集<span class="ff4">。</span>此外<span class="ff2">，</span>我们还比较了不同特征选择方法在<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击检测上的性能<span class="ff4">。</span>实验结果表明<span class="ff2">，</span>基</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">于<span class="_ _0"> </span><span class="ff3">Sin-Cos-bIAVOA<span class="_ _1"> </span></span>的特征选择方法在<span class="_ _0"> </span><span class="ff3">DDoS<span class="_ _1"> </span></span>攻击检测上具有较高的准确性和较低的误报率<span class="ff4">。</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>