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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 names used for Android components are similar : Similarities with AnubisSimilarities with Anubis When analyzing these components , similarities were found in the code of both malware families : Similarities with Anubis Another major change that indicated that the actor copied code from the Anubis Trojan is the way of handling configuration values .' [/INST] [The:O, names:O, used:O, for:O, Android:B-System, components:O, are:O, similar:O, ::O, Similarities:O, with:O, AnubisSimilarities:O, with:O, Anubis:B-Malware, When:O, analyzing:O, these:O, components:O, ,:O, similarities:O, were:O, found:O, in:O, the:O, code:O, of:O, both:O, malware:O, families:O, ::O, Similarities:O, with:O, Anubis:B-System, Another:O, major:O, change:O, that:O, indicated:O, that:O, the:O, actor:O, copied:O, code:O, from:O, the:O, Anubis:B-Malware, Trojan:O, is:O, the:O, way:O, of:O, handling:O, configuration:O, values: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: 'Previous versions were storing config values within the variables of a class , while the latest version is using SharedPreferences with some of the keys being identical to those used by Anubis : isAccessibility time_work time_start_permission url_inj Conclusion Ginp is a simple but rather efficient banking Trojan providing the basic functionality to be able to trick victims into delivering personal information .' [/INST] [Previous:O, versions:O, were:O, storing:O, config:O, values:O, within:O, the:O, variables:O, of:O, a:O, class:O, ,:O, while:O, the:O, latest:O, version:O, is:O, using:O, SharedPreferences:O, with:O, some:O, of:O, the:O, keys:O, being:O, identical:O, to:O, those:O, used:O, by:O, Anubis:B-System, ::O, isAccessibility:O, time_work:O, time_start_permission:O, url_inj:O, Conclusion:O, Ginp:B-Malware, is:O, a:O, simple:O, but:O, rather:O, efficient:O, banking:O, Trojan:O, providing:O, the:O, basic:O, functionality:O, to:O, be:O, able:O, to:O, trick:O, victims:O, into:O, delivering:O, personal:O, information: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 a 5-month timespan , actor managed to create a Trojan from scratch which will presumably continue evolving offering new features such as keylogging , back-connect proxy or RAT capabilities .' [/INST] [In:O, a:O, 5-month:O, timespan:O, ,:O, actor:O, managed:O, to:O, create:O, a:O, Trojan:O, from:O, scratch:O, which:O, will:O, presumably:O, continue:O, evolving:O, offering:O, new:O, features:O, such:O, as:O, keylogging:O, ,:O, back-connect:O, proxy:O, or:O, RAT:O, capabilities: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: 'Ginp ’ s unusual target selection is not just about its focus on Spanish banks but also the wide selection of targeted apps per bank .' [/INST] [Ginp:B-Malware, ’:O, s:O, unusual:O, target:O, selection:O, is:O, not:O, just:O, about:O, its:O, focus:O, on:O, Spanish:O, banks:O, but:O, also:O, the:O, wide:O, selection:O, of:O, targeted:O, apps:O, per:O, bank: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 fact that the overlay screens are almost identical to the legitimate banking apps suggests that the actors might be very familiar with the Spanish banking applications and might even be accustomed to the language .' [/INST] [The:O, fact:O, that:O, the:O, overlay:O, screens:O, are:O, almost:O, identical:O, to:O, the:O, legitimate:O, banking:O, apps:O, suggests:O, that:O, the:O, actors:O, might:O, be:O, very:O, familiar:O, with:O, the:O, Spanish:O, banking:O, applications:O, and:O, might:O, even:O, be:O, accustomed:O, to:O, the:O, language: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: 'Although the current target list is limited to Spanish apps , it seems that the actor is taking into account that the bot should also be able to target other countries , seeing that the path used in the inject requests contains the country code of the targeted institution .' [/INST] [Although:O, the:O, current:O, target:O, list:O, is:O, limited:O, to:O, Spanish:O, apps:O, ,:O, it:O, seems:O, that:O, the:O, actor:O, is:O, taking:O, into:O, account:O, that:O, the:O, bot:O, should:O, also:O, be:O, able:O, to:O, target:O, other:O, countries:O, ,:O, seeing:O, that:O, the:O, path:O, used:O, in:O, the:O, inject:O, requests:O, contains:O, the:O, country:O, code:O, of:O, the:O, targeted:O, institution: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 could indicate that actor already has plans in expanding the targets to applications from different countries and regions .' [/INST] [This:O, could:O, indicate:O, that:O, actor:O, already:O, has:O, plans:O, in:O, expanding:O, the:O, targets:O, to:O, applications:O, from:O, different:O, countries:O, and:O, regions: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: 'Appendix Samples Some of the latest Ginp samples found in the wild : App name Package name SHA-256 hash Google Play Verificator sing.guide.false 0ee075219a2dfde018f17561467272633821d19420c08cba14322cc3b93bb5d5 Google Play Verificator park.rather.dance 087a3beea46f3d45649b7506073ef51c784036629ca78601a4593759b253d1b7 Adobe Flash Player ethics.unknown.during' [/INST] [Appendix:O, Samples:O, Some:O, of:O, the:O, latest:O, Ginp:B-Malware, samples:O, found:O, in:O, the:O, wild:O, ::O, App:O, name:O, Package:O, name:O, SHA-256:O, hash:O, Google:B-System, Play:I-System, Verificator:I-System, sing.guide.false:B-Indicator, 0ee075219a2dfde018f17561467272633821d19420c08cba14322cc3b93bb5d5:B-Indicator, Google:B-System, Play:I-System, Verificator:I-System, park.rather.dance:B-System, 087a3beea46f3d45649b7506073ef51c784036629ca78601a4593759b253d1b7:B-Indicator, Adobe:B-System, Flash:I-System, Player:I-System, ethics.unknown.during:B-Indicator] </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: '5ac6901b232c629bc246227b783867a0122f62f9e087ceb86d83d991e92dba2f Adobe Flash Player solution.rail.forward 7eb239cc86e80e6e1866e2b3a132b5af94a13d0d24f92068a6d2e66cfe5c2cea Adobe Flash Player com.pubhny.hekzhgjty 14a1b1dce69b742f7e258805594f07e0c5148b6963c12a8429d6e15ace3a503c' [/INST] [5ac6901b232c629bc246227b783867a0122f62f9e087ceb86d83d991e92dba2f:B-Indicator, Adobe:B-System, Flash:I-System, Player:I-System, solution.rail.forward:B-Indicator, 7eb239cc86e80e6e1866e2b3a132b5af94a13d0d24f92068a6d2e66cfe5c2cea:B-Indicator, Adobe:B-System, Flash:I-System, Player:I-System, com.pubhny.hekzhgjty:B-Indicator, 14a1b1dce69b742f7e258805594f07e0c5148b6963c12a8429d6e15ace3a503c:B-Indicator] </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: 'Adobe Flash Player sentence.fancy.humble 78557094dbabecdc17fb0edb4e3a94bae184e97b1b92801e4f8eb0f0626d6212 Target list The current list of apps observed to be targeted by Ginp contains a total of 24 unique applications as seen below .' [/INST] [Adobe:B-System, Flash:I-System, Player:I-System, sentence.fancy.humble:B-Indicator, 78557094dbabecdc17fb0edb4e3a94bae184e97b1b92801e4f8eb0f0626d6212:B-Indicator, Target:O, list:O, The:O, current:O, list:O, of:O, apps:O, observed:O, to:O, be:O, targeted:O, by:O, Ginp:B-Malware, contains:O, a:O, total:O, of:O, 24:O, unique:O, applications:O, as:O, seen:O, below:O, .:O] </s> |
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