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887  <h1>Posts</h1>
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892</div>
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922    <div class="section-subheading article-title mb-0 mt-0">
923      <a href="/post/kringlecon-2022-walkthrough/" >Kringlecon 2022 Walkthrough</a>
924    </div>
925
926    
927    <a href="/post/kringlecon-2022-walkthrough/"  class="summary-link">
928      <div class="article-style">
929        Introduction Objective 1 - KringleCon Orientation Objective 2 - Recover the Tolkien Ring Wireshark Phishing - Sparkle Redberry Q1 - What type of objects can be exported from the PCAP?
930      </div>
931    </a>
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952      Pranshu Bajpai</span>
953  </div>
954  
955  
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958  <span class="article-date">
959    
960    
961      
962          Last updated on
963      
964    
965    Sep 17, 2023
966  </span>
967  
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972  <span class="middot-divider"></span>
973  <span class="article-reading-time">
974    21 min read
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1025    <div class="section-subheading article-title mb-0 mt-0">
1026      <a href="/post/kringlecon-2021-walkthrough/" >Kringlecon 2021 Walkthrough</a>
1027    </div>
1028
1029    
1030    <a href="/post/kringlecon-2021-walkthrough/"  class="summary-link">
1031      <div class="article-style">
1032        Introduction Objective 1 - KringleCon Orientation Document Analysis - Piney Sappington Objective 2 - Where in the World is Caramel Santaigo? Grepping for Gold - Greasy Gopherguts Objective 3 - Thaw Frost Tower&rsquo;s Entrance Logic Chompers - Noel McBoetie Objective 4 - Slot Machine Investigation IPv6 Sandbox - Jewel Loggins Objective 5 - Strange USB Device Holiday Hero - Chimney Scissorsticks Elevator Hack Objective 6 - Shellcode Primer - Ruby Cyster Objective 7 - Printer Exploitation HoHo &hellip; No - Eve Snowshoes Objective 8 - Kerberoasting on an Open Fire Objective 9 - Splunk IMDS Exploration - Noxious O.
1033      </div>
1034    </a>
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1055      Pranshu Bajpai</span>
1056  </div>
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1062    
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1065          Last updated on
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1068    Sep 17, 2023
1069  </span>
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1076  <span class="article-reading-time">
1077    30 min read
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1128    <div class="section-subheading article-title mb-0 mt-0">
1129      <a href="/post/docker-tutorial-cheatsheet/" >Docker Tutorial and/or Cheatsheet</a>
1130    </div>
1131
1132    
1133    <a href="/post/docker-tutorial-cheatsheet/"  class="summary-link">
1134      <div class="article-style">
1135        Frequently used Docker commands
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1158      Pranshu Bajpai</span>
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1168          Last updated on
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1171    Sep 17, 2023
1172  </span>
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1179  <span class="article-reading-time">
1180    2 min read
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1193    <i class="fas fa-folder mr-1"></i><a href="/category/devops/">DevOps</a>, <a href="/category/containers/">Containers</a>, <a href="/category/docker/">Docker</a></span>
1194  
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1196</div>
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1229  <div class="media-body">
1230
1231    <div class="section-subheading article-title mb-0 mt-0">
1232      <a href="/post/kringlecon-2020-walkthrough/" >Kringlecon 2020 Walkthrough</a>
1233    </div>
1234
1235    
1236    <a href="/post/kringlecon-2020-walkthrough/"  class="summary-link">
1237      <div class="article-style">
1238        Introduction Objective 1 - Uncover Santa&rsquo;s Gift List Kringle Kiosk - Shiny Upatree Objective 2 - Investigate S3 bucket Linux Primer - Sugarplum Mary Objective 3 - Point-of-Sale Password Recovery Unescape Tmux - Pepper Minstix Objective 4 - Operate the Santavator Speaker lights/door/vending machine - Bushy Evergreen Objective 5 - Open HID Lock Objective 6 - Splunk Challenge CAN-D bus investigation - Wunorse Openslae Objective 7 - Solve the Sleigh&rsquo;s CAN-D-BUS Problem Holly Evergreen - Redis Investigation Objective 8 - Broken Tag Generator Scapy - Alabaster Snowball Objective 9 - ARP Shenanigans Objective 10 - Defeat Fingerprint Sensor Snowball Game - Tangle Coalbox Objective 11a) - Naughty/Nice List with Blockchain Investigation Part 1 Setting up the environment Understanding the vulnerability Dumping the 32 bit PRNG integers Predicting the PRNG state Objective 11b) - Naughty/Nice List with Blockchain Investigation Part 2 Additional links Introduction Spoiler alert!
1239      </div>
1240    </a>
1241    
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1243    <div class="stream-meta article-metadata">
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1258    
1259
1260  <span >
1261      Pranshu Bajpai</span>
1262  </div>
1263  
1264  
1265
1266  
1267  <span class="article-date">
1268    
1269    
1270      
1271          Last updated on
1272      
1273    
1274    Sep 17, 2023
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1276  
1277
1278  
1279
1280  
1281  <span class="middot-divider"></span>
1282  <span class="article-reading-time">
1283    29 min read
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1285  
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1294  <span class="middot-divider"></span>
1295  <span class="article-categories">
1296    <i class="fas fa-folder mr-1"></i><a href="/category/ctf/">CTF</a></span>
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1332  <div class="media-body">
1333
1334    <div class="section-subheading article-title mb-0 mt-0">
1335      <a href="/post/practical-dbscan-clustering-python/" >Practical DBSCAN Clustering with Python</a>
1336    </div>
1337
1338    
1339    <a href="/post/practical-dbscan-clustering-python/"  class="summary-link">
1340      <div class="article-style">
1341        Introduction Generating sample data Feature scaling Determining $\varepsilon$ and $minPts$ Model fitting Visualization Outlier detection Conclusion Additional links Introduction Density Based Spatial Clustering of Applications with Noise, DBSCAN for short, is a popular clustering algorithm that can be specially useful for outlier detection and clustering data of varying density.
1342      </div>
1343    </a>
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1364      Pranshu Bajpai</span>
1365  </div>
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1371    
1372    
1373      
1374          Last updated on
1375      
1376    
1377    Sep 17, 2023
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1379  
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1384  <span class="middot-divider"></span>
1385  <span class="article-reading-time">
1386    4 min read
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1398  <span class="article-categories">
1399    <i class="fas fa-folder mr-1"></i><a href="/category/data-science/">Data Science</a></span>
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1435  <div class="media-body">
1436
1437    <div class="section-subheading article-title mb-0 mt-0">
1438      <a href="/post/practical-kmeans-clustering-python/" >Practical KMeans Clustering with Python</a>
1439    </div>
1440
1441    
1442    <a href="/post/practical-kmeans-clustering-python/"  class="summary-link">
1443      <div class="article-style">
1444        Introduction Algorithm Generating sample data Feature scaling Determining $K$ Elbow method Silhouette method Model fitting Model accuracy Conclusion Additional links Introduction KMeans clustering is perhaps the most well-known technique of partitioning similar data into the same cluster
1444s.
1445      </div>
1446    </a>
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1464    
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1466  <span >
1467      Pranshu Bajpai</span>
1468  </div>
1469  
1470  
1471
1472  
1473  <span class="article-date">
1474    
1475    
1476      
1477          Last updated on
1478      
1479    
1480    Sep 17, 2023
1481  </span>
1482  
1483
1484  
1485
1486  
1487  <span class="middot-divider"></span>
1488  <span class="article-reading-time">
1489    5 min read
1490  </span>
1491  
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1495  
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1497
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1499  
1500  <span class="middot-divider"></span>
1501  <span class="article-categories">
1502    <i class="fas fa-folder mr-1"></i><a href="/category/data-science/">Data Science</a></span>
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1505</div>
1506
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1508    </div>
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1538  <div class="media-body">
1539
1540    <div class="section-subheading article-title mb-0 mt-0">
1541      <a href="/post/pcap-analysis-zeek/" >PCAP Analysis with Zeek | Digital Forensics and Incident Response</a>
1542    </div>
1543
1544    
1545    <a href="/post/pcap-analysis-zeek/"  class="summary-link">
1546      <div class="article-style">
1547        Introduction Zeek (previously called bro) is a useful tool that enables high-level PCAP analysis at the application layer. I have mostly been doing my packet capture analysis in Wireshark and while Wireshark is still my number one tool for PCAP analysis, Zeek was a great find for me.
1548      </div>
1549    </a>
1550    
1551
1552    <div class="stream-meta article-metadata">
1553
1554      
1555
1556      
1557        
1558
1559
1560<div class="article-metadata">
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1562  
1563  
1564  
1565  
1566  <div>
1567    
1568
1569  <span >
1570      Pranshu Bajpai</span>
1571  </div>
1572  
1573  
1574
1575  
1576  <span class="article-date">
1577    
1578    
1579      
1580          Last updated on
1581      
1582    
1583    Sep 17, 2023
1584  </span>
1585  
1586
1587  
1588
1589  
1590  <span class="middot-divider"></span>
1591  <span class="article-reading-time">
1592    3 min read
1593  </span>
1594  
1595
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1597  
1598  
1599  
1600
1601  
1602  
1603  <span class="middot-divider"></span>
1604  <span class="article-categories">
1605    <i class="fas fa-folder mr-1"></i><a href="/category/dfir/">DFIR</a></span>
1606  
1607
1608</div>
1609
1610      
1611    </div>
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1640<div class="media stream-item view-compact">
1641  <div class="media-body">
1642
1643    <div class="section-subheading article-title mb-0 mt-0">
1644      <a href="/post/pcap-analysis-with-wireshark-tshark/" >PCAP Analysis with Wireshark and Tshark | Digital Forensics and Incident Response</a>
1645    </div>
1646
1647    
1648    <a href="/post/pcap-analysis-with-wireshark-tshark/"  class="summary-link">
1649      <div class="article-style">
1650        Introduction PCAPs can greatly aid an investigation after an incident has occurred. However, PCAPs contain massive amounts of data that is difficult to parse and time is valuable, especially during live investigations.
1651      </div>
1652    </a>
1653    
1654
1655    <div class="stream-meta article-metadata">
1656
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1660        
1661
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1663<div class="article-metadata">
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1666  
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1669  <div>
1670    
1671
1672  <span >
1673      Pranshu Bajpai</span>
1674  </div>
1675  
1676  
1677
1678  
1679  <span class="article-date">
1680    
1681    
1682      
1683          Last updated on
1684      
1685    
1686    Sep 17, 2023
1687  </span>
1688  
1689
1690  
1691
1692  
1693  <span class="middot-divider"></span>
1694  <span class="article-reading-time">
1695    6 min read
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1706  <span class="middot-divider"></span>
1707  <span class="article-categories">
1708    <i class="fas fa-folder mr-1"></i><a href="/category/dfir/">DFIR</a></span>
1709  
1710
1711</div>
1712
1713      
1714    </div>
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1716    
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1719  <div class="ml-3">
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1743<div class="media stream-item view-compact">
1744  <div class="media-body">
1745
1746    <div class="section-subheading article-title mb-0 mt-0">
1747      <a href="/post/parsing-palo-alto-syslogs-with-logstash/" >Parsing Palo Alto syslogs with Logstash</a>
1748    </div>
1749
1750    
1751    <a href="/post/parsing-palo-alto-syslogs-with-logstash/"  class="summary-link">
1752      <div class="article-style">
1753        Introduction Palo Alto Firewalls are capable of forwarding syslogs to a remote location. However, parsing is necessary before these logs can be properly ingested at data ingestion and storage endpoint such as Elasticsearch.
1754      </div>
1755    </a>
1756    
1757
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1773    
1774
1775  <span >
1776      Pranshu Bajpai</span>
1777  </div>
1778  
1779  
1780
1781  
1782  <span class="article-date">
1783    
1784    
1785      
1786          Last updated on
1787      
1788    
1789    Sep 17, 2023
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1791  
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1796  <span class="middot-divider"></span>
1797  <span class="article-reading-time">
1798    5 min read
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1848
1849    <div class="section-subheading article-title mb-0 mt-0">
1850      <a href="/post/how-to-disable-annoying-windows-10/" >How to disable annoying Windows 10 notification sounds!</a>
1851    </div>
1852
1853    
1854    <a href="/post/how-to-disable-annoying-windows-10/"  class="summary-link">
1855      <div class="article-style">
1856        There are some Windows 10 notification (system) sounds that are quite annoying to say the least. I am not sure who decided that it was a good idea to choose these sounds for alert notifications in Windows 10.
1857      </div>
1858    </a>
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1879      Pranshu Bajpai</span>
1880  </div>
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1888      
1889          Last updated on
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1892    Sep 17, 2023
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1900  <span class="article-reading-time">
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