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<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The domain was registered on March 8th , 2013 : Registration Service Provided By : SHANGHAI MEICHENG TECHNOLOGY INFORMATION DEVELOPMENT CO. , LTD. Domain Name : DLMDOCUMENTSEXCHANGE.COM Registration Date : 08-Mar-2013 Expiration Date : 08-Mar-2014 Status : LOCKED The domain registration data indicates the following owner : Registrant Contact Details : peng jia peng jia ( bdoufwke123010 @ gmail.com ) beijingshiahiidienquc.d beijingshi beijing,100000' [/INST] [The:O, domain:O, was:O, registered:O, on:O, March:O, 8th:O, ,:O, 2013:O, ::O, Registration:O, Service:O, Provided:O, By:O, ::O, SHANGHAI:B-Organization, MEICHENG:I-Organization, TECHNOLOGY:I-Organization, INFORMATION:I-Organization, DEVELOPMENT:I-Organization, CO.:I-Organization, ,:I-Organization, LTD.:I-Organization, Domain:O, Name:O, ::O, DLMDOCUMENTSEXCHANGE.COM:B-Indicator, Registration:O, Date:O, ::O, 08-Mar-2013:O, Expiration:O, Date:O, ::O, 08-Mar-2014:O, Status:O, ::O, LOCKED:O, The:O, domain:O, registration:O, data:O, indicates:O, the:O, following:O, owner:O, ::O, Registrant:O, Contact:O, Details:O, ::O, peng:O, jia:O, peng:O, jia:O, (:O, bdoufwke123010:B-Indicator, @:I-Indicator, gmail.com:I-Indicator, ):O, beijingshiahiidienquc.d:B-Indicator, beijingshi:O, beijing,100000:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'CN Tel .' [/INST] [CN:O, Tel:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: '+86.01078456689 Fax .' [/INST] [+86.01078456689:O, Fax:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: '+86.01078456689 The command-and-control server is hosting an index page which also serves an APK file : The referenced “ Document.apk ” is 333583 bytes in size , MD5 : c4c4077e9449147d754afd972e247efc .' [/INST] [+86.01078456689:O, The:O, command-and-control:O, server:O, is:O, hosting:O, an:O, index:O, page:O, which:O, also:O, serves:O, an:O, APK:O, file:O, ::O, The:O, referenced:O, “:O, Document.apk:B-Indicator, ”:O, is:O, 333583:O, bytes:O, in:O, size:O, ,:O, MD5:O, ::O, c4c4077e9449147d754afd972e247efc:B-Indicator, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It has the same functionality as the one described above but contains different text .' [/INST] [It:O, has:O, the:O, same:O, functionality:O, as:O, the:O, one:O, described:O, above:O, but:O, contains:O, different:O, text:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The new text ( in Chinese , about relations between China , Japan and the disputed “ Senkaku Islands / Diaoyudao Islands / Diaoyutai Islands ” ) is shown to the victims and reads as following : When opened in a browser , this is what the command-and-control index page looks like : The text on the top means “ Title Title Title ” in Chinese , while the other strings appear to be random characters typed from the keyboard .' [/INST] [The:O, new:O, text:O, (:O, in:O, Chinese:O, ,:O, about:O, relations:O, between:O, China:O, ,:O, Japan:O, and:O, the:O, disputed:O, “:O, Senkaku:O, Islands:O, /:O, Diaoyudao:O, Islands:O, /:O, Diaoyutai:O, Islands:O, ”:O, ):O, is:O, shown:O, to:O, the:O, victims:O, and:O, reads:O, as:O, following:O, ::O, When:O, opened:O, in:O, a:O, browser:O, ,:O, this:O, is:O, what:O, the:O, command-and-control:O, index:O, page:O, looks:O, like:O, ::O, The:O, text:O, on:O, the:O, top:O, means:O, “:O, Title:O, Title:O, Title:O, ”:O, in:O, Chinese:O, ,:O, while:O, the:O, other:O, strings:O, appear:O, to:O, be:O, random:O, characters:O, typed:O, from:O, the:O, keyboard:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Interestingly , the command and control server includes a publicly accessible interface to work with the victims : Some of the commands with rough translations : The command-and-control server is running Windows Server 2003 and has been configured for Chinese language : This , together with the logs , is a strong indicator that the attackers are Chinese-speaking .' [/INST] [Interestingly:O, ,:O, the:O, command:O, and:O, control:O, server:O, includes:O, a:O, publicly:O, accessible:O, interface:O, to:O, work:O, with:O, the:O, victims:O, ::O, Some:O, of:O, the:O, commands:O, with:O, rough:O, translations:O, ::O, The:O, command-and-control:O, server:O, is:O, running:O, Windows:B-System, Server:I-System, 2003:O, and:O, has:O, been:O, configured:O, for:O, Chinese:O, language:O, ::O, This:O, ,:O, together:O, with:O, the:O, logs:O, ,:O, is:O, a:O, strong:O, indicator:O, that:O, the:O, attackers:O, are:O, Chinese-speaking:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Conclusions Every day , there are hundreds if not thousands of targeted attacks against Tibetan and Uyghur supporters .' [/INST] [Conclusions:O, Every:O, day:O, ,:O, there:O, are:O, hundreds:O, if:O, not:O, thousands:O, of:O, targeted:O, attacks:O, against:O, Tibetan:O, and:O, Uyghur:O, supporters:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The vast majority of these target Windows machines through Word documents exploiting known vulnerabilities such as CVE-2012-0158 , CVE-2010-3333 and CVE-2009-3129 .' [/INST] [The:O, vast:O, majority:O, of:O, these:O, target:O, Windows:B-System, machines:O, through:O, Word:B-System, documents:O, exploiting:O, known:O, vulnerabilities:O, such:O, as:O, CVE-2012-0158:B-Vulnerability, ,:O, CVE-2010-3333:B-Vulnerability, and:O, CVE-2009-3129:B-Vulnerability, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this case , the attackers hacked a Tibetan activist ’ s account and used it to attack Uyghur activists .' [/INST] [In:O, this:O, case:O, ,:O, the:O, attackers:O, hacked:O, a:O, Tibetan:O, activist:O, ’:O, s:O, account:O, and:O, used:O, it:O, to:O, attack:O, Uyghur:O, activists:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It indicates perhaps an interesting trend which is exploiting the trust relationships between the two communities .' [/INST] [It:O, indicates:O, perhaps:O, an:O, interesting:O, trend:O, which:O, is:O, exploiting:O, the:O, trust:O, relationships:O, between:O, the:O, two:O, communities:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This technique reminds us of a combination between ages old war strategies “ Divide et impera ” and “ By way of deception ” .' [/INST] [This:O, technique:O, reminds:O, us:O, of:O, a:O, combination:O, between:O, ages:O, old:O, war:O, strategies:O, “:O, Divide:O, et:O, impera:O, ”:O, and:O, “:O, By:O, way:O, of:O, deception:O, ”:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Until now , we haven ’ t seen targeted attacks against mobile phones , although we ’ ve seen indications that these were in development .' [/INST] [Until:O, now:O, ,:O, we:O, haven:O, ’:O, t:O, seen:O, targeted:O, attacks:O, against:O, mobile:O, phones:O, ,:O, although:O, we:O, ’:O, ve:O, seen:O, indications:O, that:O, these:O, were:O, in:O, development:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The current attack took advantage of the compromise of a high-profile Tibetan activist .' [/INST] [The:O, current:O, attack:O, took:O, advantage:O, of:O, the:O, compromise:O, of:O, a:O, high-profile:O, Tibetan:O, activist:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It is perhaps the first in a new wave of targeted attacks aimed at Android users .' [/INST] [It:O, is:O, perhaps:O, the:O, first:O, in:O, a:O, new:O, wave:O, of:O, targeted:O, attacks:O, aimed:O, at:O, Android:B-System, users:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'So far , the attackers relied entirely on social engineering to infect the targets .' [/INST] [So:O, far:O, ,:O, the:O, attackers:O, relied:O, entirely:O, on:O, social:O, engineering:O, to:O, infect:O, the:O, targets:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'History has shown us that , in time , these attacks will use zero-day vulnerabilities , exploits or a combination of techniques .' [/INST] [History:O, has:O, shown:O, us:O, that:O, ,:O, in:O, time:O, ,:O, these:O, attacks:O, will:O, use:O, zero-day:B-Vulnerability, vulnerabilities:I-Vulnerability, ,:O, exploits:O, or:O, a:O, combination:O, of:O, techniques:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For now , the best protection is to avoid any APK attachments that arrive on mobile phones via e-mail .' [/INST] [For:O, now:O, ,:O, the:O, best:O, protection:O, is:O, to:O, avoid:O, any:O, APK:O, attachments:O, that:O, arrive:O, on:O, mobile:O, phones:O, via:O, e-mail:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We detect the malware used in this attack as “ Backdoor.AndroidOS.Chuli.a ” .' [/INST] [We:O, detect:O, the:O, malware:O, used:O, in:O, this:O, attack:O, as:O, “:O, Backdoor.AndroidOS.Chuli.a:B-Malware, ”:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'MD5s : c4c4077e9449147d754afd972e247efc Document.apk 0b8806b38b52bebfe39ff585639e2ea2 WUC ’ s Conference.apk Triada : organized crime on Android Triada is a modular mobile Trojan that actively uses root privileges to substitute system files and uses several clever methods to become almost invisible March 3 , 2016 You know how armies typically move : first come the scouts to make sure everything is ok. Then the heavy troops' [/INST] [MD5s:O, ::O, c4c4077e9449147d754afd972e247efc:B-Indicator, Document.apk:B-Indicator, 0b8806b38b52bebfe39ff585639e2ea2:B-Indicator, WUC:O, ’:O, s:O, Conference.apk:B-Indicator, Triada:B-Malware, ::O, organized:O, crime:O, on:O, Android:B-System, Triada:B-Malware, is:O, a:O, modular:O, mobile:O, Trojan:O, that:O, actively:O, uses:O, root:O, privileges:O, to:O, substitute:O, system:O, files:O, and:O, uses:O, several:O, clever:O, methods:O, to:O, become:O, almost:O, invisible:O, March:O, 3:O, ,:O, 2016:O, You:O, know:O, how:O, armies:O, typically:O, move:O, ::O, first:O, come:O, the:O, scouts:O, to:O, make:O, sure:O, everything:O, is:O, ok.:O, Then:O, the:O, heavy:O, troops:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'arrive ; at least that was how it used to be before the age of cyber wars .' [/INST] [arrive:O, ;:O, at:O, least:O, that:O, was:O, how:O, it:O, used:O, to:O, be:O, before:O, the:O, age:O, of:O, cyber:O, wars:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It turns out , that Trojans behave quite the same way .' [/INST] [It:O, turns:O, out:O, ,:O, that:O, Trojans:O, behave:O, quite:O, the:O, same:O, way:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'There are a lot of small Trojans for Android capable of leveraging access privileges , in other words — gaining root access .' [/INST] [There:O, are:O, a:O, lot:O, of:O, small:O, Trojans:O, for:O, Android:B-System, capable:O, of:O, leveraging:O, access:O, privileges:O, ,:O, in:O, other:O, words:O, —:O, gaining:O, root:O, access:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Our malware analysts Nikita Buchka and Mikhail Kuzin can easily name 11 families of such Trojans .' [/INST] [Our:O, malware:O, analysts:O, Nikita:O, Buchka:O, and:O, Mikhail:O, Kuzin:O, can:O, easily:O, name:O, 11:O, families:O, of:O, such:O, Trojans:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Most of them are almost harmless — all they did until recently was injecting tons of ads and downloading others of their kind .' [/INST] [Most:O, of:O, them:O, are:O, almost:O, harmless:O, —:O, all:O, they:O, did:O, until:O, recently:O, was:O, injecting:O, tons:O, of:O, ads:O, and:O, downloading:O, others:O, of:O, their:O, kind:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If you want to know more about them — our researchers have an article about them on Securelist .' [/INST] [If:O, you:O, want:O, to:O, know:O, more:O, about:O, them:O, —:O, our:O, researchers:O, have:O, an:O, article:O, about:O, them:O, on:O, Securelist:B-Organization, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If you follow the military analogy — those are the scouts .' [/INST] [If:O, you:O, follow:O, the:O, military:O, analogy:O, —:O, those:O, are:O, the:O, scouts:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As you probably have noticed , gaining root access gives them the capability to download and install applications — that ’ s the reason why once one of them get into the system , in a few minutes there are all the others .' [/INST] [As:O, you:O, probably:O, have:O, noticed:O, ,:O, gaining:O, root:O, access:O, gives:O, them:O, the:O, capability:O, to:O, download:O, and:O, install:O, applications:O, —:O, that:O, ’:O, s:O, the:O, reason:O, why:O, once:O, one:O, of:O, them:O, get:O, into:O, the:O, system:O, ,:O, in:O, a:O, few:O, minutes:O, there:O, are:O, all:O, the:O, others:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'But our researchers have predicted that these small Trojans would certainly be used to download some really bad malware that can actually harm the owners of the infected devices .' [/INST] [But:O, our:O, researchers:O, have:O, predicted:O, that:O, these:O, small:O, Trojans:O, would:O, certainly:O, be:O, used:O, to:O, download:O, some:O, really:O, bad:O, malware:O, that:O, can:O, actually:O, harm:O, the:O, owners:O, of:O, the:O, infected:O, devices:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'And that ’ s exactly what has happened recently .' [/INST] [And:O, that:O, ’:O, s:O, exactly:O, what:O, has:O, happened:O, recently:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Small Trojans like Leech , Ztorg and Gopro now download one of the most advanced mobile Trojans our malware analysts have ever encountered — we call it Triada .' [/INST] [Small:O, Trojans:O, like:O, Leech:B-Malware, ,:O, Ztorg:B-Malware, and:O, Gopro:B-Malware, now:O, download:O, one:O, of:O, the:O, most:O, advanced:O, mobile:O, Trojans:O, our:O, malware:O, analysts:O, have:O, ever:O, encountered:O, —:O, we:O, call:O, it:O, Triada:B-Malware, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Triada is a modular mobile Trojan that actively uses root privileges to substitute system files and exists mostly in the device ’ s RAM , which makes it extremely hard to detect .' [/INST] [Triada:B-Malware, is:O, a:O, modular:O, mobile:O, Trojan:O, that:O, actively:O, uses:O, root:O, privileges:O, to:O, substitute:O, system:O, files:O, and:O, exists:O, mostly:O, in:O, the:O, device:O, ’:O, s:O, RAM:O, ,:O, which:O, makes:O, it:O, extremely:O, hard:O, to:O, detect:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The dark ways of the Triada Once downloaded and installed , the Triada Trojan first tries to collect some information about the system — like the device model , the OS version , the amount of the SD card space , the list of the installed applications and other things .' [/INST] [The:O, dark:O, ways:O, of:O, the:O, Triada:B-Malware, Once:O, downloaded:O, and:O, installed:O, ,:O, the:O, Triada:B-Malware, Trojan:O, first:O, tries:O, to:O, collect:O, some:O, information:O, about:O, the:O, system:O, —:O, like:O, the:O, device:O, model:O, ,:O, the:O, OS:O, version:O, ,:O, the:O, amount:O, of:O, the:O, SD:O, card:O, space:O, ,:O, the:O, list:O, of:O, the:O, installed:O, applications:O, and:O, other:O, things:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Then it sends all that information to the Command & Control server .' [/INST] [Then:O, it:O, sends:O, all:O, that:O, information:O, to:O, the:O, Command:O, &:O, Control:O, server:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We have detected a total of 17 C & C servers on 4 different domains , which probably means the bad guys are quite familiar with what redundancy is .' [/INST] [We:O, have:O, detected:O, a:O, total:O, of:O, 17:O, C:O, &:O, C:O, servers:O, on:O, 4:O, different:O, domains:O, ,:O, which:O, probably:O, means:O, the:O, bad:O, guys:O, are:O, quite:O, familiar:O, with:O, what:O, redundancy:O, is:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The C & C server then responds with a configuration file , containing the personal identification number for the device and some settings — the time interval between contacting the server , the list of modules to be installed and so on .' [/INST] [The:O, C:O, &:O, C:O, server:O, then:O, responds:O, with:O, a:O, configuration:O, file:O, ,:O, containing:O, the:O, personal:O, identification:O, number:O, for:O, the:O, device:O, and:O, some:O, settings:O, —:O, the:O, time:O, interval:O, between:O, contacting:O, the:O, server:O, ,:O, the:O, list:O, of:O, modules:O, to:O, be:O, installed:O, and:O, so:O, on:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'After the modules are installed they are deployed to the short term memory and deleted from the device storage , which makes the Trojan a lot harder to catch .' [/INST] [After:O, the:O, modules:O, are:O, installed:O, they:O, are:O, deployed:O, to:O, the:O, short:O, term:O, memory:O, and:O, deleted:O, from:O, the:O, device:O, storage:O, ,:O, which:O, makes:O, the:O, Trojan:O, a:O, lot:O, harder:O, to:O, catch:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'There are two more reasons why Triada is so hard to detect and why it had impressed our researchers so much .' [/INST] [There:O, are:O, two:O, more:O, reasons:O, why:O, Triada:B-Malware, is:O, so:O, hard:O, to:O, detect:O, and:O, why:O, it:O, had:O, impressed:O, our:O, researchers:O, so:O, much:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'First , it modifies the Zygote process .' [/INST] [First:O, ,:O, it:O, modifies:O, the:O, Zygote:B-System, process:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Zygote is the core process in the Android OS that is used as a template for every application , which means that once the Trojan gets into Zygote , it becomes a part of literally every app that is launched on the device .' [/INST] [Zygote:B-System, is:O, the:O, core:O, process:O, in:O, the:O, Android:B-System, OS:O, that:O, is:O, used:O, as:O, a:O, template:O, for:O, every:O, application:O, ,:O, which:O, means:O, that:O, once:O, the:O, Trojan:O, gets:O, into:O, Zygote:B-System, ,:O, it:O, becomes:O, a:O, part:O, of:O, literally:O, every:O, app:O, that:O, is:O, launched:O, on:O, the:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Triada : organized crime on Android Second , it substitutes the system functions and conceals its modules from the list of the running processes and installed apps .' [/INST] [Triada:B-Malware, ::O, organized:O, crime:O, on:O, Android:B-System, Second:O, ,:O, it:O, substitutes:O, the:O, system:O, functions:O, and:O, conceals:O, its:O, modules:O, from:O, the:O, list:O, of:O, the:O, running:O, processes:O, and:O, installed:O, apps:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'So the system doesn ’ t see any strange processes running and thus does not cry the alarm .' [/INST] [So:O, the:O, system:O, doesn:O, ’:O, t:O, see:O, any:O, strange:O, processes:O, running:O, and:O, thus:O, does:O, not:O, cry:O, the:O, alarm:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Those are not the only system functions Triada modifies .' [/INST] [Those:O, are:O, not:O, the:O, only:O, system:O, functions:O, Triada:B-Malware, modifies:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As our researchers discovered , it also lays its hands on the outgoing SMS and filters the incoming ones .' [/INST] [As:O, our:O, researchers:O, discovered:O, ,:O, it:O, also:O, lays:O, its:O, hands:O, on:O, the:O, outgoing:O, SMS:O, and:O, filters:O, the:O, incoming:O, ones:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'That is actually how the bad guys decided to monetize the Trojan .' [/INST] [That:O, is:O, actually:O, how:O, the:O, bad:O, guys:O, decided:O, to:O, monetize:O, the:O, Trojan:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Some applications rely on SMS when it comes to in-app purchases — the transaction data is transferred via a short text message .' [/INST] [Some:O, applications:O, rely:O, on:O, SMS:O, when:O, it:O, comes:O, to:O, in-app:O, purchases:O, —:O, the:O, transaction:O, data:O, is:O, transferred:O, via:O, a:O, short:O, text:O, message:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The main reason for developers to choose SMS over traditional payments via Internet is that in the case with SMS no Internet connection is required .' [/INST] [The:O, main:O, reason:O, for:O, developers:O, to:O, choose:O, SMS:O, over:O, traditional:O, payments:O, via:O, Internet:O, is:O, that:O, in:O, the:O, case:O, with:O, SMS:O, no:O, Internet:O, connection:O, is:O, required:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Users do not see those SMS because they are processed not by the SMS app , but by the app that has initiated the transaction — e.g a free-to-play game .' [/INST] [Users:O, do:O, not:O, see:O, those:O, SMS:O, because:O, they:O, are:O, processed:O, not:O, by:O, the:O, SMS:O, app:O, ,:O, but:O, by:O, the:O, app:O, that:O, has:O, initiated:O, the:O, transaction:O, —:O, e.g:O, a:O, free-to-play:O, game:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Triada ’ s functionality allows it to modify those messages , so the money is sent not to some app developer , but to the malware operators .' [/INST] [Triada:B-Malware, ’:O, s:O, functionality:O, allows:O, it:O, to:O, modify:O, those:O, messages:O, ,:O, so:O, the:O, money:O, is:O, sent:O, not:O, to:O, some:O, app:O, developer:O, ,:O, but:O, to:O, the:O, malware:O, operators:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Triada steals the money either from the users — if they haven ’ t succeeded in purchasing whatever they wanted , or from the app developers , in case the user has completed the purchase successfully .' [/INST] [Triada:B-Malware, steals:O, the:O, money:O, either:O, from:O, the:O, users:O, —:O, if:O, they:O, haven:O, ’:O, t:O, succeeded:O, in:O, purchasing:O, whatever:O, they:O, wanted:O, ,:O, or:O, from:O, the:O, app:O, developers:O, ,:O, in:O, case:O, the:O, user:O, has:O, completed:O, the:O, purchase:O, successfully:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For now , that is the only way how cybercriminals can profit from Triada , but don ’ t forget that it ’ s a modular Trojan , so it can be turned into literally everything on one command from the C & C server .' [/INST] [For:O, now:O, ,:O, that:O, is:O, the:O, only:O, way:O, how:O, cybercriminals:O, can:O, profit:O, from:O, Triada:B-Malware, ,:O, but:O, don:O, ’:O, t:O, forget:O, that:O, it:O, ’:O, s:O, a:O, modular:O, Trojan:O, ,:O, so:O, it:O, can:O, be:O, turned:O, into:O, literally:O, everything:O, on:O, one:O, command:O, from:O, the:O, C:O, &:O, C:O, server:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Fighting organized crime in your phone One of the main problems with Triada is that it can potentially hurt a LOT of people .' [/INST] [Fighting:O, organized:O, crime:O, in:O, your:O, phone:O, One:O, of:O, the:O, main:O, problems:O, with:O, Triada:B-Malware, is:O, that:O, it:O, can:O, potentially:O, hurt:O, a:O, LOT:O, of:O, people:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As we ’ ve mentioned earlier , Triada is downloaded by smaller Trojans that have leveraged the access privileges .' [/INST] [As:O, we:O, ’:O, ve:O, mentioned:O, earlier:O, ,:O, Triada:B-Malware, is:O, downloaded:O, by:O, smaller:O, Trojans:O, that:O, have:O, leveraged:O, the:O, access:O, privileges:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'And our researchers estimate that in every 10 Android users 1 was attacked by either one or several of those Trojans during the second half of 2015 , so there are millions of devices with a huge possibility of being infected with Triada .' [/INST] [And:O, our:O, researchers:O, estimate:O, that:O, in:O, every:O, 10:O, Android:B-System, users:O, 1:O, was:O, attacked:O, by:O, either:O, one:O, or:O, several:O, of:O, those:O, Trojans:O, during:O, the:O, second:O, half:O, of:O, 2015:O, ,:O, so:O, there:O, are:O, millions:O, of:O, devices:O, with:O, a:O, huge:O, possibility:O, of:O, being:O, infected:O, with:O, Triada:B-Malware, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'So , what can you do to protect yourself from this stealthy beast ?' [/INST] [So:O, ,:O, what:O, can:O, you:O, do:O, to:O, protect:O, yourself:O, from:O, this:O, stealthy:O, beast:O, ?:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: '1 .' [/INST] [1:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Never forget to update your system .' [/INST] [Never:O, forget:O, to:O, update:O, your:O, system:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It turns out that those smaller Trojans face serious problems trying to get root access on Android 4.4.4 and above , because a lot of vulnerabilities were patched in these versions .' [/INST] [It:O, turns:O, out:O, that:O, those:O, smaller:O, Trojans:O, face:O, serious:O, problems:O, trying:O, to:O, get:O, root:O, access:O, on:O, Android:B-System, 4.4.4:I-System, and:O, above:O, ,:O, because:O, a:O, lot:O, of:O, vulnerabilities:O, were:O, patched:O, in:O, these:O, versions:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'So if you have Android 4.4.4 or some more recent version of this OS on your device , your chances of getting infected with Triada are significantly lower .' [/INST] [So:O, if:O, you:O, have:O, Android:B-System, 4.4.4:I-System, or:O, some:O, more:O, recent:O, version:O, of:O, this:O, OS:O, on:O, your:O, device:O, ,:O, your:O, chances:O, of:O, getting:O, infected:O, with:O, Triada:B-Malware, are:O, significantly:O, lower:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Yet our statistics says that about 60 % of Android users are still sitting with Android 4.4.2 and below .' [/INST] [Yet:O, our:O, statistics:O, says:O, that:O, about:O, 60:O, %:O, of:O, Android:B-System, users:O, are:O, still:O, sitting:O, with:O, Android:B-System, 4.4.2:I-System, and:I-System, below:I-System, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Triada : organized crime on Android 2 .' [/INST] [Triada:B-Malware, ::O, organized:O, crime:O, on:O, Android:B-System, 2:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Better not to take any chances at all , no matter which version of the OS you use .' [/INST] [Better:O, not:O, to:O, take:O, any:O, chances:O, at:O, all:O, ,:O, no:O, matter:O, which:O, version:O, of:O, the:O, OS:O, you:O, use:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'So we recommend installing an anti-virus solution on your Android device .' [/INST] [So:O, we:O, recommend:O, installing:O, an:O, anti-virus:O, solution:O, on:O, your:O, Android:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Kaspersky Internet Security for Android detects all three of Triada ’ s modules , so it can save your money from cybercriminals that are behind Triada .' [/INST] [Kaspersky:B-System, Internet:I-System, Security:I-System, for:O, Android:B-System, detects:O, all:O, three:O, of:O, Triada:B-Malware, ’:O, s:O, modules:O, ,:O, so:O, it:O, can:O, save:O, your:O, money:O, from:O, cybercriminals:O, that:O, are:O, behind:O, Triada:B-Malware, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Just don ’ t forget that the scan does not run automatically in the free version .' [/INST] [Just:O, don:O, ’:O, t:O, forget:O, that:O, the:O, scan:O, does:O, not:O, run:O, automatically:O, in:O, the:O, free:O, version:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'But all in all Triada is yet another example of a really bad trend : malware developers are taking Android seriously , and the latest samples are almost as complex and hard to withstand , as their Windows-based kin .' [/INST] [But:O, all:O, in:O, all:O, Triada:B-Malware, is:O, yet:O, another:O, example:O, of:O, a:O, really:O, bad:O, trend:O, ::O, malware:O, developers:O, are:O, taking:O, Android:B-System, seriously:O, ,:O, and:O, the:O, latest:O, samples:O, are:O, almost:O, as:O, complex:O, and:O, hard:O, to:O, withstand:O, ,:O, as:O, their:O, Windows-based:B-System, kin:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The only good way to fight all these threats is to be proactive , and so a good security solution is a must .' [/INST] [The:O, only:O, good:O, way:O, to:O, fight:O, all:O, these:O, threats:O, is:O, to:O, be:O, proactive:O, ,:O, and:O, so:O, a:O, good:O, security:O, solution:O, is:O, a:O, must:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'TrickBot Pushing a 2FA Bypass App to Bank Customers in Germany March 24 , 2020 IBM X-Force researchers analyzed an Android malware app that ’ s likely being pushed to infected users by the TrickBot Trojan .' [/INST] [TrickBot:B-Malware, Pushing:O, a:O, 2FA:O, Bypass:O, App:O, to:O, Bank:O, Customers:O, in:O, Germany:O, March:O, 24:O, ,:O, 2020:O, IBM:B-Organization, X-Force:I-Organization, researchers:O, analyzed:O, an:O, Android:B-System, malware:O, app:O, that:O, ’:O, s:O, likely:O, being:O, pushed:O, to:O, infected:O, users:O, by:O, the:O, TrickBot:B-Malware, Trojan:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This app , dubbed “ TrickMo ” by our team , is designed to bypass second factor and strong authentication pushed to bank customers when they need to authorize a transaction .' [/INST] [This:O, app:O, ,:O, dubbed:O, “:O, TrickMo:B-Malware, ”:O, by:O, our:O, team:O, ,:O, is:O, designed:O, to:O, bypass:O, second:O, factor:O, and:O, strong:O, authentication:O, pushed:O, to:O, bank:O, customers:O, when:O, they:O, need:O, to:O, authorize:O, a:O, transaction:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While it ’ s not the first of its kind , this Android malware app is more sophisticated than similar apps and possesses interesting features that enable its operators to steal transaction authorization codes from victims who download the app .' [/INST] [While:O, it:O, ’:O, s:O, not:O, the:O, first:O, of:O, its:O, kind:O, ,:O, this:O, Android:B-System, malware:O, app:O, is:O, more:O, sophisticated:O, than:O, similar:O, apps:O, and:O, possesses:O, interesting:O, features:O, that:O, enable:O, its:O, operators:O, to:O, steal:O, transaction:O, authorization:O, codes:O, from:O, victims:O, who:O, download:O, the:O, app:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'According to our research , TrickMo is still under active development as we expect to see frequent changes and updates .' [/INST] [According:O, to:O, our:O, research:O, ,:O, TrickMo:B-Malware, is:O, still:O, under:O, active:O, development:O, as:O, we:O, expect:O, to:O, see:O, frequent:O, changes:O, and:O, updates:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While it can be used anywhere and target any bank or region , at this time , we are seeing it deployed specifically in Germany .' [/INST] [While:O, it:O, can:O, be:O, used:O, anywhere:O, and:O, target:O, any:O, bank:O, or:O, region:O, ,:O, at:O, this:O, time:O, ,:O, we:O, are:O, seeing:O, it:O, deployed:O, specifically:O, in:O, Germany:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Germany is one of the first attack turfs TrickBot spread to when it first emerged in 2016 .' [/INST] [Germany:O, is:O, one:O, of:O, the:O, first:O, attack:O, turfs:O, TrickBot:B-Malware, spread:O, to:O, when:O, it:O, first:O, emerged:O, in:O, 2016:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In 2020 , it appears that TrickBot ’ s vast bank fraud is an ongoing project that helps the gang monetize compromised accounts .' [/INST] [In:O, 2020:O, ,:O, it:O, appears:O, that:O, TrickBot:B-Malware, ’:O, s:O, vast:O, bank:O, fraud:O, is:O, an:O, ongoing:O, project:O, that:O, helps:O, the:O, gang:O, monetize:O, compromised:O, accounts:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'First Signs in September 2019 In September 2019 , a tweet by CERT-Bund caught the attention of the IBM Trusteer Mobile Security Research team .' [/INST] [First:O, Signs:O, in:O, September:O, 2019:O, In:O, September:O, 2019:O, ,:O, a:O, tweet:O, by:O, CERT-Bund:B-Organization, caught:O, the:O, attention:O, of:O, the:O, IBM:B-Organization, Trusteer:I-Organization, Mobile:I-Organization, Security:I-Organization, Research:I-Organization, team:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The tweet stated that TrickBot , a well-known banking Trojan owned by an organized cybercrime gang , uses man-in-the-browser ( MITB ) web injects in online banking sessions to ask infected users for their mobile phone number and device type .' [/INST] [The:O, tweet:O, stated:O, that:O, TrickBot:B-Malware, ,:O, a:O, well-known:O, banking:O, Trojan:O, owned:O, by:O, an:O, organized:O, cybercrime:O, gang:O, ,:O, uses:O, man-in-the-browser:O, (:O, MITB:O, ):O, web:O, injects:O, in:O, online:O, banking:O, sessions:O, to:O, ask:O, infected:O, users:O, for:O, their:O, mobile:O, phone:O, number:O, and:O, device:O, type:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Machine translation of this tweet reads : “ Watch out for online banking : Emotet reloads TrickBot .' [/INST] [Machine:O, translation:O, of:O, this:O, tweet:O, reads:O, ::O, “:O, Watch:O, out:O, for:O, online:O, banking:O, ::O, Emotet:B-Malware, reloads:O, TrickBot:B-Malware, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'On infected PCs , TrickBot displays a query for the mobile phone number and the device type used for banking and then prompts users to install an alleged security app. ” When banking Trojans ask for this type of information , it usually means the next step will be an attempt to infect the victim ’ s mobile device .' [/INST] [On:O, infected:O, PCs:O, ,:O, TrickBot:B-Malware, displays:O, a:O, query:O, for:O, the:O, mobile:O, phone:O, number:O, and:O, the:O, device:O, type:O, used:O, for:O, banking:O, and:O, then:O, prompts:O, users:O, to:O, install:O, an:O, alleged:O, security:O, app.:O, ”:O, When:O, banking:O, Trojans:O, ask:O, for:O, this:O, type:O, of:O, information:O, ,:O, it:O, usually:O, means:O, the:O, next:O, step:O, will:O, be:O, an:O, attempt:O, to:O, infect:O, the:O, victim:O, ’:O, s:O, mobile:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Our team went ahead and hunted for samples of the app and analyzed it in our labs .' [/INST] [Our:O, team:O, went:O, ahead:O, and:O, hunted:O, for:O, samples:O, of:O, the:O, app:O, and:O, analyzed:O, it:O, in:O, our:O, labs:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this analysis , we get into the capabilities of the new variant and what we found to be a “ kill switch ” that can eliminate the malware remotely from an infected device .' [/INST] [In:O, this:O, analysis:O, ,:O, we:O, get:O, into:O, the:O, capabilities:O, of:O, the:O, new:O, variant:O, and:O, what:O, we:O, found:O, to:O, be:O, a:O, “:O, kill:O, switch:O, ”:O, that:O, can:O, eliminate:O, the:O, malware:O, remotely:O, from:O, an:O, infected:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Desktop Trojans and Their Mobile Component The process by which Trojans attempt to infect mobile devices is at least a decade old .' [/INST] [Desktop:O, Trojans:O, and:O, Their:O, Mobile:O, Component:O, The:O, process:O, by:O, which:O, Trojans:O, attempt:O, to:O, infect:O, mobile:O, devices:O, is:O, at:O, least:O, a:O, decade:O, old:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Usually , when users are already infected with malware like TrickBot on their desktop , they will see a web injection asking for their mobile device operating system ( OS ) type and phone number .' [/INST] [Usually:O, ,:O, when:O, users:O, are:O, already:O, infected:O, with:O, malware:O, like:O, TrickBot:B-Malware, on:O, their:O, desktop:O, ,:O, they:O, will:O, see:O, a:O, web:O, injection:O, asking:O, for:O, their:O, mobile:O, device:O, operating:O, system:O, (:O, OS:O, ):O, type:O, and:O, phone:O, number:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Next , if they indicate that they use an Android-based device , the Trojan , impersonating their bank with web injections , fools the victim into installing a fake security app .' [/INST] [Next:O, ,:O, if:O, they:O, indicate:O, that:O, they:O, use:O, an:O, Android-based:B-System, device:O, ,:O, the:O, Trojan:O, ,:O, impersonating:O, their:O, bank:O, with:O, web:O, injections:O, ,:O, fools:O, the:O, victim:O, into:O, installing:O, a:O, fake:O, security:O, app:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The supposed purpose of that app is to obtain and use a required “ security code ” to log in to their online banking site .' [/INST] [The:O, supposed:O, purpose:O, of:O, that:O, app:O, is:O, to:O, obtain:O, and:O, use:O, a:O, required:O, “:O, security:O, code:O, ”:O, to:O, log:O, in:O, to:O, their:O, online:O, banking:O, site:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Our research team analyzed the malicious Android application that is most likely being spread by TrickBot and dubbed it “ TrickMo. ” Targeting users in Germany at this time , TrickMo is the latest variation in the transaction authentication number ( TAN ) -stealing malware category .' [/INST] [Our:O, research:O, team:O, analyzed:O, the:O, malicious:O, Android:B-System, application:O, that:O, is:O, most:O, likely:O, being:O, spread:O, by:O, TrickBot:B-Malware, and:O, dubbed:O, it:O, “:O, TrickMo.:B-Malware, ”:O, Targeting:O, users:O, in:O, Germany:O, at:O, this:O, time:O, ,:O, TrickMo:B-Malware, is:O, the:O, latest:O, variation:O, in:O, the:O, transaction:O, authentication:O, number:O, (:O, TAN:O, ):O, -stealing:O, malware:O, category:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Its main capabilities include : Stealing personal device information Intercepting SMS messages Recording targeted applications for one-time password ( TAN ) Lockdown of the phone Stealing pictures from the device Self-destruction and removal As banks release more advanced security measures , banking malware evolves to keep up with the perpetual arms race .' [/INST] [Its:O, main:O, capabilities:O, include:O, ::O, Stealing:O, personal:O, device:O, information:O, Intercepting:O, SMS:O, messages:O, Recording:O, targeted:O, applications:O, for:O, one-time:O, password:O, (:O, TAN:O, ):O, Lockdown:O, of:O, the:O, phone:O, Stealing:O, pictures:O, from:O, the:O, device:O, Self-destruction:O, and:O, removal:O, As:O, banks:O, release:O, more:O, advanced:O, security:O, measures:O, ,:O, banking:O, malware:O, evolves:O, to:O, keep:O, up:O, with:O, the:O, perpetual:O, arms:O, race:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'From our analysis of the TrickMo mobile malware , it is apparent that TrickMo is designed to break the newest methods of OTP and , specifically , TAN codes often used in Germany .' [/INST] [From:O, our:O, analysis:O, of:O, the:O, TrickMo:B-Malware, mobile:O, malware:O, ,:O, it:O, is:O, apparent:O, that:O, TrickMo:B-Malware, is:O, designed:O, to:O, break:O, the:O, newest:O, methods:O, of:O, OTP:O, and:O, ,:O, specifically:O, ,:O, TAN:O, codes:O, often:O, used:O, in:O, Germany:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Among the various features we discuss in this post , we believe that TrickMo ’ s most significant novelty is an app recording feature , which gives it the ability to overcome the newer pushTAN app validations used by German banks .' [/INST] [Among:O, the:O, various:O, features:O, we:O, discuss:O, in:O, this:O, post:O, ,:O, we:O, believe:O, that:O, TrickMo:B-Malware, ’:O, s:O, most:O, significant:O, novelty:O, is:O, an:O, app:O, recording:O, feature:O, ,:O, which:O, gives:O, it:O, the:O, ability:O, to:O, overcome:O, the:O, newer:O, pushTAN:O, app:O, validations:O, used:O, by:O, German:O, banks:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the analysis that follows , we describe in detail the capabilities of this new variant and a “ kill switch ” that can remotely eliminate the malware from a mobile device .' [/INST] [In:O, the:O, analysis:O, that:O, follows:O, ,:O, we:O, describe:O, in:O, detail:O, the:O, capabilities:O, of:O, this:O, new:O, variant:O, and:O, a:O, “:O, kill:O, switch:O, ”:O, that:O, can:O, remotely:O, eliminate:O, the:O, malware:O, from:O, a:O, mobile:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Why Do Desktop Trojans Use a Mobile Component ?' [/INST] [Why:O, Do:O, Desktop:O, Trojans:O, Use:O, a:O, Mobile:O, Component:O, ?:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'About a decade ago , attackers wielding banking Trojans could simply use stolen credentials to access a victim ’ s online banking account and perform money transfers .' [/INST] [About:O, a:O, decade:O, ago:O, ,:O, attackers:O, wielding:O, banking:O, Trojans:O, could:O, simply:O, use:O, stolen:O, credentials:O, to:O, access:O, a:O, victim:O, ’:O, s:O, online:O, banking:O, account:O, and:O, perform:O, money:O, transfers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As a countermeasure , financial institutions introduced various second factor authentication ( 2FA ) methods .' [/INST] [As:O, a:O, countermeasure:O, ,:O, financial:O, institutions:O, introduced:O, various:O, second:O, factor:O, authentication:O, (:O, 2FA:O, ):O, methods:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'One method , which was popular in Germany , is known as mobile TAN ( mTAN ) .' [/INST] [One:O, method:O, ,:O, which:O, was:O, popular:O, in:O, Germany:O, ,:O, is:O, known:O, as:O, mobile:O, TAN:O, (:O, mTAN:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It was implemented by sending an SMS message containing a one-time password ( OTP ) to the client ’ s mobile device .' [/INST] [It:O, was:O, implemented:O, by:O, sending:O, an:O, SMS:O, message:O, containing:O, a:O, one-time:O, password:O, (:O, OTP:O, ):O, to:O, the:O, client:O, ’:O, s:O, mobile:O, device:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The transaction would only be authorized after the client enters the TAN into the online banking website in their browser .' [/INST] [The:O, transaction:O, would:O, only:O, be:O, authorized:O, after:O, the:O, client:O, enters:O, the:O, TAN:O, into:O, the:O, online:O, banking:O, website:O, in:O, their:O, browser:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Keep in mind that while this case is about TANs , it can be any OTP , depending on which bank is being targeted .' [/INST] [Keep:O, in:O, mind:O, that:O, while:O, this:O, case:O, is:O, about:O, TANs:O, ,:O, it:O, can:O, be:O, any:O, OTP:O, ,:O, depending:O, on:O, which:O, bank:O, is:O, being:O, targeted:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Meanwhile , desktop banking Trojans developed the ability to execute various social engineering schemes by using web injections , a method that alters the content presented to the infected victim in their browser .' [/INST] [Meanwhile:O, ,:O, desktop:O, banking:O, Trojans:O, developed:O, the:O, ability:O, to:O, execute:O, various:O, social:O, engineering:O, schemes:O, by:O, using:O, web:O, injections:O, ,:O, a:O, method:O, that:O, alters:O, the:O, content:O, presented:O, to:O, the:O, infected:O, victim:O, in:O, their:O, browser:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In some cases , sophisticated web injects were used to trick victims into entering their 2FA codes directly into the web forms controlled by the malware to eliminate the need for the mobile malware component .' [/INST] [In:O, some:O, cases:O, ,:O, sophisticated:O, web:O, injects:O, were:O, used:O, to:O, trick:O, victims:O, into:O, entering:O, their:O, 2FA:O, codes:O, directly:O, into:O, the:O, web:O, forms:O, controlled:O, by:O, the:O, malware:O, to:O, eliminate:O, the:O, need:O, for:O, the:O, mobile:O, malware:O, component:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'But attackers were still constantly looking for new methods to steal TANs .' [/INST] [But:O, attackers:O, were:O, still:O, constantly:O, looking:O, for:O, new:O, methods:O, to:O, steal:O, TANs:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Around 2011 , the infamous Zeus Trojan started using web injects that tricked users into downloading a mobile component called “ ZitMo ” ( Zeus in the Mobile ) .' [/INST] [Around:O, 2011:O, ,:O, the:O, infamous:O, Zeus:B-Malware, Trojan:I-Malware, started:O, using:O, web:O, injects:O, that:O, tricked:O, users:O, into:O, downloading:O, a:O, mobile:O, component:O, called:O, “:O, ZitMo:B-Malware, ”:O, (:O, Zeus:B-Malware, in:O, the:O, Mobile:O, ):O, .:O] </s> |