Paper Citation Record · LEDGER

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods

As of 13 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2411.14512.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.14512 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:34:25.154054Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ea487d3-e8c9-44a7-80f6-7eac0fe363ec · outbound

This paper cites Since it is a connectionless protocol, attackers use other comput ers or IOT devices as botnets through a command to use network workstations for the completion of the atta ck.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Since it is a connectionless protocol, attackers use other comput ers or IOT devices as botnets through a command to use network workstations for the completion of the atta ck

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.537537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e91e8760-8e1e-4822-97b0-0ff1f4fe1809 · outbound

This paper cites Smurf is provided through cracking IP addresses in a network for a large number of Internet Control Message Protocol (ICMP) traffic to the victim’s server.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Smurf is provided through cracking IP addresses in a network for a large number of Internet Control Message Protocol (ICMP) traffic to the victim’s server

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.509660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a493e8b-ea4a-4d3c-b111-cb587de7e7a5 · outbound

This paper cites T his attack is conducted through the insertion of an aff ected SQL statement as an array and passing this through the database of a website as an equation.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods T his attack is conducted through the insertion of an aff ected SQL statement as an array and passing this through the database of a website as an equation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.490374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c326a7ea-af95-4c06-9bea-e06a42fc4f1a · outbound

This paper cites Unlike SIDDOS attackers se nd valid messages instead of illegitimate messages but at a very slow rate to a web application container web s erver.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Unlike SIDDOS attackers se nd valid messages instead of illegitimate messages but at a very slow rate to a web application container web s erver

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.475922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89a30893-7604-40a3-aeda-9b6a42bc6eff · outbound

This paper cites Accur acy is the measurement of the rate of the correctly classi fied instances.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Accur acy is the measurement of the rate of the correctly classi fied instances

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.460178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22db4b87-de00-44f0-82f6-15ec97eb02a5 · outbound

This paper cites The constructed confusion matrix of the test dataset through the Logistic Regression model is presented in Table II.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods The constructed confusion matrix of the test dataset through the Logistic Regression model is presented in Table II

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.445367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aa6543aa-3fb7-48b7-a8fd-0e85ff21cca6 · outbound

This paper cites an unresolved cited work.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:34:25.431153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b3e2c1bd-ee0a-4114-80c0-4c63091a088b · outbound

This paper cites The classification report which contains the information about precision and recall rate is pres ented in Table III.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods The classification report which contains the information about precision and recall rate is pres ented in Table III

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.411335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 38461509-a8d2-400c-b0f7-393456e960dd · outbound

This paper cites It also implies that the i nput features and output class level have a continuous relationship.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods It also implies that the i nput features and output class level have a continuous relationship

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.396036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 457bacd5-16c4-4f47-96f3-b288d3cd1580 · outbound

This paper cites The confusion matrix of this model is presented in Table IV.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods The confusion matrix of this model is presented in Table IV

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.380022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22de9edc-7a07-4114-bee5-66e94c54cdbc · outbound

This paper cites TABLE IV.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods TABLE IV

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.365252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0165798-e3f7-4c5c-ae54-804440ff7d9e · outbound

This paper cites Only the SIDDOS class provides high precisi on but low recall.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Only the SIDDOS class provides high precisi on but low recall

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.352049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a44edc5-9e09-48f3-b62d-0a92e140ce32 · outbound

This paper cites Analysis of accounting models for the detection of duplicate requests in web services,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Analysis of accounting models for the detection of duplicate requests in web services,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.337589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f71d1659-c15e-47ac-8904-c682496092bd · outbound

This paper cites Botnet detection based on traffic behavior analysis and flow intervals,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Botnet detection based on traffic behavior analysis and flow intervals,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.321554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2995cad4-5df0-4bf5-9a07-8d1384c44f5f · outbound

This paper cites Protecting web 2.0 s ervices from botnet exploitations,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Protecting web 2.0 s ervices from botnet exploitations,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.306258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5cec1321-2f4b-4eda-bc8f-858e2046f344 · outbound

This paper cites Emerging Research in Computing, Informa tion, Communication, and Applications,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Emerging Research in Computing, Informa tion, Communication, and Applications,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.289770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 681908c8-39dd-4001-939d-abb943cd9a37 · outbound

This paper cites Detecting Distributed Denial of Service Attacks Us ing Data Mining Techniques,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Detecting Distributed Denial of Service Attacks Us ing Data Mining Techniques,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.273894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9c83a01d-df6b-4f68-8fc9-fde136e0ba48 · outbound

This paper cites an unresolved cited work.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:34:25.257065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2c341eb-8f52-48e7-aecf-162cb06ce0b8 · outbound

This paper cites DDoS attack detection using machine learning techniques in cloud computing environments,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods DDoS attack detection using machine learning techniques in cloud computing environments,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.239750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a29c048b-4d6d-4294-a595-dacc447e00a9 · outbound

This paper cites DDoS attack detection using unique source IP deviation,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods DDoS attack detection using unique source IP deviation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.222183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b32b17e-5c8a-424f-81b0-548094eb1f2d · outbound

This paper cites Detection of DD OS Attacks in Network Traffic Using Deep Learning,.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Detection of DD OS Attacks in Network Traffic Using Deep Learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:34:25.206297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55a74d78-5016-4d4a-9e2a-5f047ee7d222 · outbound

This paper cites an unresolved cited work.

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:34:25.190029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.