33%
14.11.2013
_ID
------ -------- ---------- ---------- -------------------------------------------------------------
2 PDB$SEED 4062623230 4062623230 E0C9D94CE3B6497BE04380B0A8C06105 NORMAL 1720734 1
3 PDB001 1700339437 1700339437 E0D0BE79135B75B0E04380B0A8C00F14 NORMAL 1956354 1
33%
25.05.2012
. Something along the lines of:
TID PRIO USER DISK READ DISK WRITE IO> COMMAND
18981 be/4 www-data 0.00 B/s 3.92 K/s apache2 -k start
Or, if you were trying to see which system user
33%
22.12.2017
-table count
Mac Entries for Vlan 1:
---------------------------
Dynamic Address Count : 3
Static Address Count : 0
Total Mac Addresses : 3
Total Mac Address Space Available: 6021
In a MAC flooding attack
33%
11.02.2016
DestinationSizeChange 41943106 (40.0 MB)
Another view of the file statistics lists which file effected the change:
# gunzip -c /mnt/backup/rdiff-backup-data/file_statistics.\
2015-03-15T10\:44\:06+01\:00.data.gz | awk '$2
33%
22.08.2011
,000 commonly used passwords:
$ john -wordlist:password.lst passfile.txt
Loaded 2 passwords with 2 different salts (FreeBSD MD5 [32/64])
admin (root)
t-bone (khess)
guesses: 2 time: 0:00:00:00 100
33%
14.08.2017
:31 FS_scan.csv
$ gzip -9 FS_scan.csv
$ ls -lsah FS_scan.csv.gz
268K -rw-r--r-- 1 laytonjb laytonjb 261K 2014-06-09 20:31 FS_scan.csv.gz
The original file is 3.2MB, but after using gzip with the -9
33%
04.11.2011
-o pe_start
# vgcreate RaidVolGroup00 /dev/sdx
# lvcreate --extents 100%VG --name RaidLogVol00 RaidVolGroup00
# mkfs -t ext3 -E stride=32 -m 0 -O dir_index,filetype,has_journal,sparse_super /dev
33%
14.06.2017
-rw-r--r-- 1 laytonjb laytonjb 261K 2014-06-09 20:31 FS_scan.csv.gz
The original file is 3.2MB, but after using gzip
with the -9
option (i.e., maximum compression), the resulting file is 268KB. The .gz
33%
17.04.2017
. The disadvantage of hardware appliances is that they are less flexible than software, which you can adapt to suit your own landscape.
Cuckoo [3] was launched in August 2010, and the release candidate for version 2.0
33%
02.02.2021
.sin(periods * 2 * np.pi * t)
12 return max(value, 0.0)
13 else:
14 value = np.sin(periods * 2 * np.pi * t)
15 return max(value, 0.0)
16
17 # building the data vector
18 my_data = []
19 i = 0
20 while
