Autore Topic: Splunk and Tensorflow for Security: Catching the Fraudster with Behavior Biometr  (Letto 373 volte)

0 Utenti e 1 Visitatore stanno visualizzando questo topic.

Offline Flavio58

Advertisement
User account takeovers, credentials theft, and online payment method takeovers have been, and continue to be the primary ways fraudsters cause big dollar losses and reputation damages to businesses throughout the years. More than 50% of successful damaging attacks are initiated with valid user credentials.
Geo location? Threat intelligence feeds? Device recognition? IP address correlation? Attackers are well aware of these detection techniques.

Fraudsters are becoming increasingly more sophisticated. Within minutes they can leverage worldwide clouds of cheap virtual machines to conceal their true physical locations and utilize specialized device identity spoofing tools in attempts to deceive the most sophisticated fraud detection systems as well as human experts.

Identifying malicious actor or suspicious transaction by IP address, computer device identity or browser user agent anomalies is becoming more challenging.

To build more sophisticated defenses we need to consider enriching traditional machine data sources with information that reflects unique and complex behavior patterns of a person behind the screen. That’s when we’ll start finding something that fraudsters and cybercriminals cannot steal or fake.

Humans are using computer systems in a ways that are unique and consistent with their own biology and physiology. The way humans click and type, the way humans use mouse and other input devices are pretty consistent with that person’s own behavior, habits, education level, and familiarity with a service or system.

Habits and behaviors are very difficult to change and if we can identify legitimate users by their typical behavior patterns - we can detect anomalies on a totally new level. Same goes with fraudsters - ability to identify and quantify behavior patterns of cyber criminal will allow us to uncover and neutralize threats that may be undetectable by other means.

The question is: can we recognize the user - or a class of users by some unique ways they use their input devices, such as mouse or keyboard? Enter Behavioral Biometrics: the field of study related to measure of uniquely identifying and measurable patterns in human activities.

This post will show one of the ways you can implement advanced user behavior biometrics solution for security based on Splunk and one of the Deep Learning frameworks.

Attempts to discover and classify behavior biometrics patterns were attempted by a number of industry players. Let’s consider the task of matching user to mouse activity. Traditional detection system executes complicated actions of feature extraction, data measurements and normalization. For each mouse movement event, the system would apply artificial trajectory smoothing, measure multiple points of velocity, acceleration, curvature, relative distances, inflection points, etc...

After such heavy pre processing traditional machine learning techniques are applied to extracted features. Model is trained and subsequent predictions are made.

The limitation of such approach is that complexity of task is artificially reduced to a subset of calculated features and the rest of data is ignored.
This almost guarantees that more complex, subtle, and yet very personal behavior patterns that are naturally present in the raw data will be missed.

To detect, extract and recognize infinitely complex behavior patterns that might be present in a treasure trove of user behavior data we need to look at complete, unfiltered datasets through data science that must go beyond traditional algorithms based on a subset of engineered features.

But how do we get access to detailed datasets representing user activity?
Just like with any other data source - it’s easy to get precise, fine-grained end user input device activity directly into Splunk.

Thanks to our talented senior software engineer Oleg Izmerly - here is the complete source code that demonstrates how to do exactly that.

Knowing that each mouse movement generates X and Y coordinates of mouse pointer and also a timestamp - we can collect this data and send it to Splunk to enrich traditional data sources, like clickstream, web and application logs. This way user’s session activity data will also contain information about potentially unique behavior patterns.

https://www.splunk.com/blog/2017/04/18/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.html


Consulente in Informatica dal 1984

Software automazione, progettazione elettronica, computer vision, intelligenza artificiale, IoT, sicurezza informatica, tecnologie di sicurezza militare, SIGINT. 

Facebook:https://www.facebook.com/flaviobernardotti58
Twitter : https://www.twitter.com/Flavio58

Cell:  +39 366 3416556

f.bernardotti@deeplearningitalia.eu

#deeplearning #computervision #embeddedboard #iot #ai

 

Related Topics

  Oggetto / Aperto da Risposte Ultimo post
0 Risposte
63 Visite
Ultimo post Settembre 10, 2018, 04:02:21 am
da Flavio58
0 Risposte
61 Visite
Ultimo post Settembre 28, 2018, 04:04:25 pm
da Flavio58
0 Risposte
99 Visite
Ultimo post Ottobre 12, 2018, 10:08:37 pm
da Flavio58
0 Risposte
69 Visite
Ultimo post Ottobre 22, 2018, 06:02:30 am
da Flavio58
0 Risposte
51 Visite
Ultimo post Febbraio 20, 2019, 02:03:03 pm
da Ruggero Respigo

Sitemap 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326