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thinhhoangphamthinhhoangpham
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changed fisheye effect
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Lines changed: 697 additions & 236 deletions

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‎README_timearcs.md‎

Whitespace-only changes.

‎attack_extract.py‎

Lines changed: 185 additions & 185 deletions
Original file line numberDiff line numberDiff line change
@@ -1,185 +1,185 @@
1-
import argparse
2-
import json
3-
import os
4-
import time
5-
6-
import pandas as pd
7-
8-
9-
def parse_args() -> argparse.Namespace:
10-
parser = argparse.ArgumentParser(description="Filter large CSV by DDOS IPs using chunked processing")
11-
parser.add_argument(
12-
"input",
13-
nargs='+',
14-
help="Path(s) to input CSV file(s) - can specify multiple files",
15-
)
16-
parser.add_argument(
17-
"--output",
18-
default="decoded_pcap_data_set1_full_ddos.csv",
19-
help="Path to output CSV file",
20-
)
21-
parser.add_argument(
22-
"--ip-map",
23-
default="full_ip_map.json",
24-
dest="ip_map_path",
25-
help="Path to JSON mapping of IP string to integer id",
26-
)
27-
parser.add_argument(
28-
"--chunksize",
29-
type=int,
30-
default=1000000,
31-
help="Number of rows per chunk when reading the input CSV",
32-
)
33-
return parser.parse_args()
34-
35-
36-
def main() -> None:
37-
args = parse_args()
38-
39-
# Load the mapping file
40-
with open(args.ip_map_path, "r") as f:
41-
ip_map = json.load(f)
42-
43-
# Default IPs extracted from the provided image; used if no IPs are passed
44-
extracted_ips = [
45-
"172.28.3.121",
46-
"172.28.213.189",
47-
"172.28.214.102",
48-
"172.28.23.163",
49-
"172.28.197.120",
50-
"77.91.104.22",
51-
"201.89.32.16",
52-
"68.91.226.37",
53-
"172.28.3.242",
54-
"172.28.16.39",
55-
"172.28.3.56",
56-
"172.28.13.198",
57-
"172.28.133.17",
58-
"172.28.220.39",
59-
"172.28.12.167",
60-
"172.28.13.29",
61-
"172.28.6.9",
62-
"172.28.27.162",
63-
"172.28.197.210",
64-
"172.28.196.167",
65-
"172.28.212.207",
66-
"172.28.218.87",
67-
"172.28.5.81",
68-
"172.28.27.31",
69-
"172.28.11.182",
70-
"172.28.218.214",
71-
"172.28.133.166",
72-
"172.28.209.155",
73-
"172.28.219.150",
74-
"172.28.12.45",
75-
"172.28.222.131",
76-
"172.28.132.105",
77-
"172.28.198.83",
78-
"172.28.6.47",
79-
"172.28.211.200",
80-
"172.28.14.161",
81-
"172.28.128.124",
82-
"172.28.212.131",
83-
"172.28.211.212",
84-
"172.28.11.150",
85-
"172.28.130.107",
86-
"172.28.131.186",
87-
"172.28.212.85",
88-
"172.28.2.148",
89-
"172.28.194.173",
90-
"172.28.214.23",
91-
"172.28.16.212",
92-
"172.28.15.104",
93-
"172.28.128.218",
94-
"70.98.1.1",
95-
"66.200.1.1",
96-
"24.145.1.1",
97-
"64.180.1.1",
98-
"172.28.3.248",
99-
"172.28.8.210",
100-
"172.28.132.25",
101-
"172.28.22.122",
102-
"172.28.198.147",
103-
"172.28.2.90",
104-
"172.28.3.39",
105-
"172.28.6.48",
106-
"172.28.130.83",
107-
"172.28.6.170",
108-
"44.29.203.5",
109-
"123.44.92.173",
110-
"64.222.102.58",
111-
]
112-
113-
# Convert IPs to integers using the mapping and drop missing
114-
ddos_ids = {ip_map[ip] for ip in extracted_ips if ip in ip_map}
115-
116-
# Ensure we start fresh
117-
if os.path.exists(args.output):
118-
os.remove(args.output)
119-
120-
print(f"Processing {len(args.input)} input file(s)...")
121-
print(f"DDoS IPs to filter: {len(ddos_ids)} unique IPs")
122-
123-
overall_start_time = time.time()
124-
overall_total_rows = 0
125-
overall_total_matched = 0
126-
127-
# Process each input file
128-
for file_index, input_file in enumerate(args.input, start=1):
129-
print(f"\n[{file_index}/{len(args.input)}] Reading '{input_file}' in chunks of {args.chunksize:,} rows...")
130-
131-
if not os.path.exists(input_file):
132-
print(f" WARNING: File not found, skipping: {input_file}")
133-
continue
134-
135-
file_start_time = time.time()
136-
file_total_rows = 0
137-
file_total_matched = 0
138-
chunk_index = 0
139-
140-
# Stream input CSV in chunks and write matching rows incrementally
141-
for chunk in pd.read_csv(input_file, chunksize=args.chunksize):
142-
chunk_index += 1
143-
file_total_rows += len(chunk)
144-
overall_total_rows += len(chunk)
145-
146-
if "src_ip" not in chunk.columns or "dst_ip" not in chunk.columns:
147-
# If required columns are missing, skip this chunk
148-
continue
149-
150-
# src_ip and dst_ip might be floats or strings; coerce to nullable Int64 before matching
151-
src_ids = pd.to_numeric(chunk["src_ip"], errors="coerce").astype("Int64")
152-
dst_ids = pd.to_numeric(chunk["dst_ip"], errors="coerce").astype("Int64")
153-
mask = src_ids.isin(ddos_ids) | dst_ids.isin(ddos_ids)
154-
filtered_chunk = chunk[mask]
155-
matched_count = len(filtered_chunk)
156-
file_total_matched += matched_count
157-
overall_total_matched += matched_count
158-
159-
if not filtered_chunk.empty:
160-
# Write header only on first write (file doesn't exist because we removed it)
161-
write_header = not os.path.exists(args.output)
162-
filtered_chunk.to_csv(args.output, index=False, mode="a", header=write_header)
163-
164-
print(
165-
f" Chunk {chunk_index}: processed {len(chunk):,} rows (file total {file_total_rows:,}); matched {matched_count:,} (file total {file_total_matched:,})"
166-
)
167-
168-
file_elapsed = time.time() - file_start_time
169-
print(f" File complete: {file_total_rows:,} rows processed, {file_total_matched:,} matched in {file_elapsed:.2f}s")
170-
171-
overall_elapsed = time.time() - overall_start_time
172-
print(f"\n{'='*80}")
173-
if os.path.exists(args.output):
174-
print(
175-
f"All files processed. Wrote {overall_total_matched:,} matched rows to '{args.output}' in {overall_elapsed:.2f}s."
176-
)
177-
print(f"Total rows processed: {overall_total_rows:,}")
178-
else:
179-
print(
180-
f"All files processed. No matches found after processing {overall_total_rows:,} rows in {overall_elapsed:.2f}s."
181-
)
182-
183-
184-
if __name__ == "__main__":
185-
main()
1+
import argparse
2+
import json
3+
import os
4+
import time
5+
6+
import pandas as pd
7+
8+
9+
def parse_args() -> argparse.Namespace:
10+
parser = argparse.ArgumentParser(description="Filter large CSV by DDOS IPs using chunked processing")
11+
parser.add_argument(
12+
"input",
13+
nargs='+',
14+
help="Path(s) to input CSV file(s) - can specify multiple files",
15+
)
16+
parser.add_argument(
17+
"--output",
18+
default="decoded_pcap_data_set1_full_ddos.csv",
19+
help="Path to output CSV file",
20+
)
21+
parser.add_argument(
22+
"--ip-map",
23+
default="full_ip_map.json",
24+
dest="ip_map_path",
25+
help="Path to JSON mapping of IP string to integer id",
26+
)
27+
parser.add_argument(
28+
"--chunksize",
29+
type=int,
30+
default=1000000,
31+
help="Number of rows per chunk when reading the input CSV",
32+
)
33+
return parser.parse_args()
34+
35+
36+
def main() -> None:
37+
args = parse_args()
38+
39+
# Load the mapping file
40+
with open(args.ip_map_path, "r") as f:
41+
ip_map = json.load(f)
42+
43+
# Default IPs extracted from the provided image; used if no IPs are passed
44+
extracted_ips = [
45+
"172.28.3.121",
46+
"172.28.213.189",
47+
"172.28.214.102",
48+
"172.28.23.163",
49+
"172.28.197.120",
50+
"77.91.104.22",
51+
"201.89.32.16",
52+
"68.91.226.37",
53+
"172.28.3.242",
54+
"172.28.16.39",
55+
"172.28.3.56",
56+
"172.28.13.198",
57+
"172.28.133.17",
58+
"172.28.220.39",
59+
"172.28.12.167",
60+
"172.28.13.29",
61+
"172.28.6.9",
62+
"172.28.27.162",
63+
"172.28.197.210",
64+
"172.28.196.167",
65+
"172.28.212.207",
66+
"172.28.218.87",
67+
"172.28.5.81",
68+
"172.28.27.31",
69+
"172.28.11.182",
70+
"172.28.218.214",
71+
"172.28.133.166",
72+
"172.28.209.155",
73+
"172.28.219.150",
74+
"172.28.12.45",
75+
"172.28.222.131",
76+
"172.28.132.105",
77+
"172.28.198.83",
78+
"172.28.6.47",
79+
"172.28.211.200",
80+
"172.28.14.161",
81+
"172.28.128.124",
82+
"172.28.212.131",
83+
"172.28.211.212",
84+
"172.28.11.150",
85+
"172.28.130.107",
86+
"172.28.131.186",
87+
"172.28.212.85",
88+
"172.28.2.148",
89+
"172.28.194.173",
90+
"172.28.214.23",
91+
"172.28.16.212",
92+
"172.28.15.104",
93+
"172.28.128.218",
94+
"70.98.1.1",
95+
"66.200.1.1",
96+
"24.145.1.1",
97+
"64.180.1.1",
98+
"172.28.3.248",
99+
"172.28.8.210",
100+
"172.28.132.25",
101+
"172.28.22.122",
102+
"172.28.198.147",
103+
"172.28.2.90",
104+
"172.28.3.39",
105+
"172.28.6.48",
106+
"172.28.130.83",
107+
"172.28.6.170",
108+
"44.29.203.5",
109+
"123.44.92.173",
110+
"64.222.102.58",
111+
]
112+
113+
# Convert IPs to integers using the mapping and drop missing
114+
ddos_ids = {ip_map[ip] for ip in extracted_ips if ip in ip_map}
115+
116+
# Ensure we start fresh
117+
if os.path.exists(args.output):
118+
os.remove(args.output)
119+
120+
print(f"Processing {len(args.input)} input file(s)...")
121+
print(f"DDoS IPs to filter: {len(ddos_ids)} unique IPs")
122+
123+
overall_start_time = time.time()
124+
overall_total_rows = 0
125+
overall_total_matched = 0
126+
127+
# Process each input file
128+
for file_index, input_file in enumerate(args.input, start=1):
129+
print(f"\n[{file_index}/{len(args.input)}] Reading '{input_file}' in chunks of {args.chunksize:,} rows...")
130+
131+
if not os.path.exists(input_file):
132+
print(f" WARNING: File not found, skipping: {input_file}")
133+
continue
134+
135+
file_start_time = time.time()
136+
file_total_rows = 0
137+
file_total_matched = 0
138+
chunk_index = 0
139+
140+
# Stream input CSV in chunks and write matching rows incrementally
141+
for chunk in pd.read_csv(input_file, chunksize=args.chunksize):
142+
chunk_index += 1
143+
file_total_rows += len(chunk)
144+
overall_total_rows += len(chunk)
145+
146+
if "src_ip" not in chunk.columns or "dst_ip" not in chunk.columns:
147+
# If required columns are missing, skip this chunk
148+
continue
149+
150+
# src_ip and dst_ip might be floats or strings; coerce to nullable Int64 before matching
151+
src_ids = pd.to_numeric(chunk["src_ip"], errors="coerce").astype("Int64")
152+
dst_ids = pd.to_numeric(chunk["dst_ip"], errors="coerce").astype("Int64")
153+
mask = src_ids.isin(ddos_ids) | dst_ids.isin(ddos_ids)
154+
filtered_chunk = chunk[mask]
155+
matched_count = len(filtered_chunk)
156+
file_total_matched += matched_count
157+
overall_total_matched += matched_count
158+
159+
if not filtered_chunk.empty:
160+
# Write header only on first write (file doesn't exist because we removed it)
161+
write_header = not os.path.exists(args.output)
162+
filtered_chunk.to_csv(args.output, index=False, mode="a", header=write_header)
163+
164+
print(
165+
f" Chunk {chunk_index}: processed {len(chunk):,} rows (file total {file_total_rows:,}); matched {matched_count:,} (file total {file_total_matched:,})"
166+
)
167+
168+
file_elapsed = time.time() - file_start_time
169+
print(f" File complete: {file_total_rows:,} rows processed, {file_total_matched:,} matched in {file_elapsed:.2f}s")
170+
171+
overall_elapsed = time.time() - overall_start_time
172+
print(f"\n{'='*80}")
173+
if os.path.exists(args.output):
174+
print(
175+
f"All files processed. Wrote {overall_total_matched:,} matched rows to '{args.output}' in {overall_elapsed:.2f}s."
176+
)
177+
print(f"Total rows processed: {overall_total_rows:,}")
178+
else:
179+
print(
180+
f"All files processed. No matches found after processing {overall_total_rows:,} rows in {overall_elapsed:.2f}s."
181+
)
182+
183+
184+
if __name__ == "__main__":
185+
main()

‎attack_timearcs.html‎

Lines changed: 4 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -103,9 +103,12 @@
103103
<span>🔍 Lensing</span>
104104
<span style="font-size:10px; opacity:0.7; margin-left:4px;">(⇧L)</span>
105105
</button>
106+
<button id="resetFisheyeBtn" style="padding:6px 12px; border:1px solid #dee2e6; border-radius:4px; background:#fff; cursor:pointer; font-size:12px; font-weight:600; transition: all 0.2s;" title="Reset fisheye effect to original positions">
107+
<span>↺ Reset</span>
108+
</button>
106109
<div style="display:flex; align-items:center; gap:8px; padding:4px 8px; border:1px solid #dee2e6; border-radius:4px;">
107110
<label for="lensingMulSlider" style="font-size:12px; font-weight:600; white-space:nowrap;">Zoom:</label>
108-
<input type="range" id="lensingMulSlider" min="2" max="200" step="1" value="5" style="width:100px; cursor:pointer;" />
111+
<input type="range" id="lensingMulSlider" min="2" max="100" step="1" value="5" style="width:100px; cursor:pointer;" />
109112
<span id="lensingMulValue" style="font-size:12px; min-width:24px; text-align:right;">5x</span>
110113
</div>
111114
<div class="hint" style="margin-left: auto;">

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