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Trojan Horse Dialer 10E

Antivirus Protection Dates Initial Rapid Release version January 15, 2001 Latest Rapid Release version August 8, 2016 revision 023 Initial Daily Certified version January 15, 2001 Latest Daily Certified version August These features include the categories of: applicable software characteristics of a particular type of malware; dynamic link library (DLL) and function name strings typically occurring in the body of the malware; A method as recited in claim 9 wherein said classification algorithm is based on the support vector machine (SVM) algorithm. 15. One technique for choosing the best parameters is simply trial and error. weblink

Worm Classification Example The following example describes feature selection, training parameters and results for a malware classifier designed to detect computer worms. Trained model 130 is the trained classification function in the form of a computer file that is output by a separate training application as described below. In order to check false positives, each classifier was run on several personal computers and directed to classify all files found in a portable executable format. The VC dimension value is 368.

Trojan:Win32/Lethic.H (Microsoft); Generic.ge (McAfee); Trojan.Gen (Symantec); Trojan.Win32.Buzus.ctfx (Kaspersky); Trojan.Win32... Preview post Submit post Cancel post You are reporting the following post: trojan dialer This post has been flagged and will be reviewed by our staff. A method as recited in claim 1 wherein said type of malware is a worm, spyware or a dialer. 4. One embodiment is a method for training a malware classifier.

Colume 264 lists the corresponding value for each function name that is considered a feature. Track this discussion and email me when there are updates If you're asking for technical help, please be sure to include all your system info, including operating system, model number, and The malware classifier apparatus as recited in claim 16 wherein said characteristics of said type of malware include header fields. 20. FIG. 6 illustrates a situation in which the training samples are not linearly separable.

All rights reserved. Prior art techniques able to detect known malware use a predefined pattern database that compares a known pattern with suspected malware. Smola (ed.), MIT Press, 1999. etaf replied Mar 7, 2017 at 11:36 PM Playing guitar ekim68 replied Mar 7, 2017 at 11:32 PM Loading...

A method as recited in claim 9 wherein said type of malware is a virus, a worm, a Trojan horse, a dropper, a wabbit, a fork bomb, spyware, adware, a backdoor, FIG. 12B is an example of a block diagram for computer system 900. BRIEF DESCRIPTION OF THE DRAWINGS The invention, together with further advantages thereof, may best be understood by reference to the following description taken in conjunction with the accompanying drawings in which: The... 8113 Total Search | Showing Results : 3781 - 3800 Previous Next

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The system returned: (22) Invalid argument The remote host or network may be down. In this particular embodiment, the feature definition file includes features specific to the detection of a computer worms. By providing these features and their values to the classifier, the classifier is better able to identify a particular type of malware. FIGS. 10B, 10C and 10C show features representing commonly used dynamic link libraries and function names.

Buy Home Office Online Store Renew Online Business Find a Partner Contact Us 1-877-218-7353 (M-F 8am - 5pm CST) Small Business Small Business Online Store Renew Online Find a Partner Contact have a peek at these guys Malware can be classified based upon how is executed, how it spreads or what it does. Normally, the host program continues to function after it is infected by the virus. Furthermore, method embodiments of the present invention may execute solely upon CPU 922 or may execute over a network such as the Internet in conjunction with a remote CPU that shares

  1. Please try the request again.
  2. Training of the classification algorithm uses a database of positive samples (benign computer software) and negative samples (computer software that is known malware).
  3. Also, malware often steals and uses the CD keys for some of the common computer games.
  4. It will be appreciated that the information retained within fixed disk 926, may, in appropriate cases, be incorporated in standard fashion as virtual memory in memory 924.
  5. For situations such as the one shown in FIG. 5 the training samples are linearly separable.
  6. The feature representation of the input computer software includes the relevant features and the values of each feature.

pacifier, Jul 9, 2004 Replies: 13 Views: 650 Flrman1 Jul 17, 2004 Locked Having a problem with spyware Corbu, Jul 12, 2004 Replies: 11 Views: 630 Flrman1 Jul 17, 2004 Locked Because the number of rules is often limited, this technique cannot achieve both a high detection rate and a low false-positive rate. Examples of these strings include auto-run registry keys such as CurrentVersion\Run CurrentVersion\Run Services HKLM\Windows\Software\Microsoft\CurrentVersion\Run and HKCR\exefile\shell\open\command. http://songstersoftware.com/trojan-horse/trojan-horse-dialer-8u.html A backdoor is a piece of software that allows access to the computer system by bypassing the normal authentication procedures.

TROJ_DROPPER.DWZ Alias:Backdoor.Win32.SmallHTTP-based.c (Kaspersky), New Malware.h !! (McAfee), Downloader (Symantec), DR/SmallHTTP-based.C (Avira), Mal/EncPk-AM (Sophos), WORM_RBOT.CXQ Alias:Backdoor.Win32.Rbot.bjp (Kaspersky), W32/Sdbot.worm.gen.h (McAfee), TR/Crypt.PCMM.Gen (Avira), JS_DLOADER.ZCM Alias:HEUR/Exploit.HTML (Avira), Mal/ObfJS-H (Sophos), WORM_RANDEX.BG Alias:Backdoor.Win32.VanBot.d (Kaspersky), W32/Sdbot.worm.gen.h (McAfee), W32.Randex.GEL Any suspect computer software is input to the malware classifier with the resulting output being a label that identifies the software as benign, normal software or as a particular type of For example, if it is decided to implement a malware classifier that will detect computer worms then a feature definition file is constructed having specific features relevant to computer worms.

A malware classifier uses a pattern classification algorithm to statistically analyze computer software in order to categorize it by giving it a classification label.

Because many malware programs are packed, leaving only the stub of the import table or perhaps even no import table, the malware classifier will search for the name of the dynamic Graph 470 shows the converted feature space in which the training samples (or rather, their converted values) are now linearly separable using line 475. These are strings identifying registry keys, passwords, games, e-mail commands, etc. Some Trojan horses can spread or activate other malware, such as viruses (a dropper).

Business  For Home  Alerts No new notifications at this time. Once it is determined for which type of malware to screen, in step 604 classification labels are determined. The below description provides an example of the use of the invention with malware written in the portable executable (PE) format. http://songstersoftware.com/trojan-horse/trojan-horse-dialer-7-b.html JS_PSYME.CBX Alias:Trojan-Clicker.JS.Agent.h (Kaspersky), HEUR/Exploit.HTML (Avira), Mal/ObfJS-C (Sophos), ANDROIDOS_SNDAPPS.SM ...sends these information to the following site: http://www.{BLOCKED}tudios.com/android_notifier/notifier.php?h={encrypted information} It waits for a reply from the server which contains the configuration about its

It is important to be able to detect such malware when it first becomes present in a computer system, or better yet, before it can be transferred to a user's computer. The feature representation of the input computer software includes the relevant features and the values of each feature. What version is it?Trojan horse Dialer.17.H Filename: A0048604.dllTrojan horse Dialer.17.E Filename: A0043340.dllTrojan horse Dialer.17.M Filename: A0048593.dllTrojan horse Dialer.17.H Filename: sporder.dllTrojan horse Dialer.17.M Filename: newdotnet6_38.dllTrojan horse Dialer.17.M Filename: newdotnet6_38.dllA quick fix (if For example, if the model is to be trained to detect computer worms, then the possible classification labels are either “worm” or “normal.” In step 608 appropriate features relevant to the

As shown, reference numerals 708-752 lists particular function names along with their associated dynamic link library name. The malware classifier apparatus as recited in claim 16 wherein said pattern classification algorithm is based on the support vector machine (SVM) algorithm. 22. Once the model is trained it is then used to classify known worms and known benign applications; if the results are not good (i.e. Burges and A.

FIG. 10A shows characteristics 704 found in the header of a portable executable format file. HTML_DLOADER.KGZ Alias:Trojan-Downloader.JS.Psyme.ea (Kaspersky), TR/Dldr.Psyme.EA.4 (Avira), Mal/ObfJS-H (Sophos), WORM_AUTORUN.RPC ...Microsoft); W32/Autorun.worm.dp (McAfee); W32.SillyFDC (Symantec); Worm.Win32.AutoRun.ehw, Worm.Win32.AutoRun.ehw (Kaspersky); Backdoor.Win32.FlyAgent.h (v) (Sunbelt); Trojan.Autorun.AON (FSecure) WORM_SPYBOT.RP Alias:Backdoor.Win32.VanBot.bh (Kaspersky), W32/Sdbot.worm.gen.h (McAfee), W32.Spybot.Worm (Symantec), TR/Agent.111104.1 (Avira), Troj/IRCBot-TC tca0bell, Jul 16, 2004 Replies: 9 Views: 538 Flrman1 Jul 17, 2004 Locked Hijack This Log, Please help uvenkat, Jul 17, 2004 Replies: 0 Views: 413 uvenkat Jul 17, 2004 Locked A Trojan horse can be deliberately attached to otherwise useful software by a programmer, or can be spread by tricking users into believing that it is useful.

Spyware usually works and spreads like Trojan horses.