Applying Catastrophe Theory for Network Anomaly Detection in Cloud Computing Traffic

<p>A trade-off between <svg height="6.1673pt" id="M29" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 11.4781 6.1673" width="11.4781pt" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M515 96L502 119C471 88 431 62 423 62C416 62 411 70 417 101C440 223 469 341 497 448H486L412 422L380 277C330 188 210 57 152 57C137 57 126 69 139 124L195 366C210 431 205 448 182 448C155 448 89 413 23 350L36 326C73 354 103 376 112 376C118 376 118 365 113 340L61 118C54 90 52 68 52 51C52 0 75 -12 98 -12S151 -3 181 17C242 58 305 119 362 193H364L345 104C323 3 339 -12 359 -12C390 -12 464 35 515 96Z"></path></g></svg>&amp;<svg height="6.04987pt" id="M30" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 10.3428 6.04987" width="10.3428pt" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.013,0,0,-0.013,4.358,0)"><path d="M219 86C216 168 211 250 206 337C201 410 189 448 163 448C131 448 79 396 43 344L60 322C91 359 110 375 118 375S132 358 136 298C141 238 152 81 155 -12H182C242 62 331 177 390 258C435 321 451 360 451 391C450 424 432 448 408 448C390 448 372 435 366 419C362 410 362 401 366 394C373 383 376 367 376 350C376 283 262 138 221 86H219Z"></path></g></svg> control variables in week 5 Tuesday; the horizontal axis represents time.</p>

Security and Communication Networks

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Figure 2

Figure 2: Applying Catastrophe Theory for Network Anomaly Detection in Cloud Computing Traffic 