A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection

<div>Examples of holes. h1<svg height="5.65839pt" id="M239" style="vertical-align:-0.04980993pt" version="1.1" viewbox="-0.0498162 -5.60858 7.75925 5.65839" width="7.75925pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M548 311L516 323C497 274 452 240 400 240C372 240 342 260 316 278C277 305 235 330 188 330C116 330 66 268 40 199L72 187C91 236 136 270 188 270C216 270 246 250 272 232C311 205 353 180 400 180C472 180 522 242 548 311Z"></path></g></svg>h12 are Type-1 holes, and H1<svg height="5.65839pt" id="M240" style="vertical-align:-0.04980993pt" version="1.1" viewbox="-0.0498162 -5.60858 7.75925 5.65839" width="7.75925pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M548 311L516 323C497 274 452 240 400 240C372 240 342 260 316 278C277 305 235 330 188 330C116 330 66 268 40 199L72 187C91 236 136 270 188 270C216 270 246 250 272 232C311 205 353 180 400 180C472 180 522 242 548 311Z"></path></g></svg>H3 are the Type-2 holes.</div>

International Journal of Intelligent Systems

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Figure 5

Figure 5: A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection 