Datasets:

pritamdeka

/

dataset_dnrti_train

like 0

<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 example , we analyzed a DropIt sample ( SHA256 : cca268c13885ad5751eb70371bbc9ce8c8795654fedb90d9e3886cbcfe323671 ) that dropped two executables , one of which was saved to , %TEMP%\flash_update.exe , that was a legitimate Flash Player installer .' [/INST] [For:O, example:O, ,:O, we:O, analyzed:O, a:O, DropIt:B-Tool, sample:I-Tool, (:O, SHA256:O, ::O, cca268c13885ad5751eb70371bbc9ce8c8795654fedb90d9e3886cbcfe323671:O, ):O, that:O, dropped:B-Features, two:I-Features, executables:I-Features, ,:O, one:O, of:O, which:O, was:O, saved:O, to:O, ,:O, %TEMP%\flash_update.exe:B-SamFile, ,:O, that:O, was:O, a:O, legitimate:O, Flash:B-Tool, Player:I-Tool, installer:I-Tool, .: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 HTA files contained job descriptions and links to job postings on popular employment websites .' [/INST] [The:O, HTA:B-SamFile, files:I-SamFile, contained:B-Features, job:I-Features, descriptions:I-Features, and:I-Features, links:I-Features, to:O, job:O, postings:O, on:O, popular:O, employment:O, websites: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: 'ChopShop1 is a new framework developed by the MITRE Corporation for network-based protocol decoders that enable security professionals to understand actual commands issued by human operators controlling endpoints .' [/INST] [ChopShop1:B-SamFile, is:O, a:O, new:O, framework:O, developed:O, by:O, the:O, MITRE:B-Org, Corporation:I-Org, for:O, network-based:O, protocol:O, decoders:O, that:O, enable:B-Features, security:I-Features, professionals:I-Features, to:O, understand:O, actual:O, commands:O, issued:O, by:O, human:O, operators:O, controlling:O, endpoints: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 found new variants of the Powermud backdoor , a new backdoor ( Backdoor.Powemuddy ) , and custom tools for stealing passwords , creating reverse shells , privilege escalation , and the use of the native Windows cabinet creation tool , makecab.exe , probably for compressing stolen data to be uploaded .' [/INST] [We:O, found:O, new:O, variants:O, of:O, the:O, Powermud:B-Tool, backdoor:I-Tool, ,:O, a:O, new:O, backdoor:O, (:O, Backdoor.Powemuddy:B-SamFile, ):O, ,:O, and:O, custom:B-Tool, tools:I-Tool, for:O, stealing:B-Features, passwords:I-Features, ,:O, creating:B-Features, reverse:I-Features, shells:I-Features, ,:O, privilege:B-Features, escalation:I-Features, ,:O, and:O, the:O, use:B-Features, of:I-Features, the:I-Features, native:I-Features, Windows:I-Features, cabinet:I-Features, creation:I-Features, tool:I-Features, ,:O, makecab.exe:B-SamFile, ,:O, probably:O, for:O, compressing:O, stolen:O, data:O, to:O, be:O, uploaded: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: 'Like the previous campaigns , these samples again involve a Microsoft Word document embedded with a malicious macro that is capable of executing PowerShell ( PS ) scripts leading to a backdoor payload .' [/INST] [Like:O, the:O, previous:O, campaigns:B-OffAct, ,:O, these:O, samples:O, again:O, involve:O, a:O, Microsoft:B-SamFile, Word:I-SamFile, document:I-SamFile, embedded:O, with:O, a:O, malicious:O, macro:O, that:O, is:O, capable:O, of:O, executing:O, PowerShell:O, (:O, PS:O, ):O, scripts:O, leading:B-Features, to:I-Features, a:I-Features, backdoor:I-Features, payload:I-Features, .: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: 'Taking a step back , as discussed in the Appendix in our initial OilRig blog , Clayslide delivery documents initially open with a worksheet named , Incompatible , that displays content that instructs the user to , Enable Content , to see the contents of the document , which in fact runs the malicious macro and compromises the system .' [/INST] [Taking:O, a:O, step:O, back:O, ,:O, as:O, discussed:O, in:O, the:O, Appendix:O, in:O, our:O, initial:O, OilRig:B-HackOrg, blog:O, ,:O, Clayslide:B-SamFile, delivery:I-SamFile, documents:I-SamFile, initially:O, open:O, with:O, a:O, worksheet:O, named:O, ,:O, Incompatible:O, ,:O, that:O, displays:O, content:O, that:O, instructs:O, the:O, user:O, to:O, ,:O, Enable:O, Content:O, ,:O, to:O, see:O, the:O, contents:O, of:O, the:O, document:O, ,:O, which:O, in:O, fact:O, runs:B-Features, the:I-Features, malicious:I-Features, macro:I-Features, and:O, compromises:B-Features, the:I-Features, system:I-Features, .: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 vulnerability exists in the old Equation Editor ( EQNEDT32.EXE ) , a component of Microsoft Office that is used to insert and evaluate mathematical formulas .' [/INST] [The:O, vulnerability:O, exists:O, in:O, the:O, old:O, Equation:B-Tool, Editor:I-Tool, (:O, EQNEDT32.EXE:B-SamFile, ):O, ,:O, a:O, component:O, of:O, Microsoft:O, Office:O, that:O, is:O, used:O, to:O, insert:B-Features, and:I-Features, evaluate:I-Features, mathematical:I-Features, formulas:I-Features, .: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 attackers then began to perform reconnaissance activities on Computer A via cmd.exe , collecting system-related information , such as the OS version , hardware configuration , and network information .' [/INST] [The:O, attackers:O, then:O, began:O, to:O, perform:O, reconnaissance:O, activities:B-OffAct, on:O, Computer:O, A:O, via:O, cmd.exe:B-SamFile, ,:O, collecting:B-Features, system-related:I-Features, information:I-Features, ,:O, such:O, as:O, the:O, OS:O, version:O, ,:O, hardware:O, configuration:O, ,:O, and:O, network: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: 'Catchamas is a custom Trojan designed to steal information from an infected computer and contains additional features designed to avoid detection .' [/INST] [Catchamas:B-SamFile, is:O, a:O, custom:O, Trojan:O, designed:O, to:O, steal:B-Features, information:I-Features, from:O, an:O, infected:O, computer:O, and:O, contains:O, additional:O, features:O, designed:O, to:O, avoid:O, detection: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: 'MXI Player appears to be a version of the Bahamut agent , designed to record the phone calls and collect other information about the user ( com.mxi.videoplay ) .' [/INST] [MXI:B-SamFile, Player:I-SamFile, appears:O, to:O, be:O, a:O, version:O, of:O, the:O, Bahamut:O, agent:O, ,:O, designed:O, to:O, record:B-Features, the:I-Features, phone:I-Features, calls:I-Features, and:O, collect:B-Features, other:I-Features, information:I-Features, about:O, the:O, user:O, (:O, com.mxi.videoplay: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: 'Threat actors may use the date string hardcoded into each Bookworm sample as a build identifier .' [/INST] [Threat:O, actors:O, may:O, use:O, the:O, date:B-SamFile, string:I-SamFile, hardcoded:I-SamFile, into:O, each:O, Bookworm:B-Tool, sample:I-Tool, as:O, a:O, build:B-Features, identifier:I-Features, .: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: 'Research presented in this report shows that the PUTTER PANDA operators are likely members of the 12th Bureau , 3rd General Staff Department ( GSD ) of the People 's Liberation Army ( PLA ) , operating from the unit 's headquarters in Shanghai with MUCD 61486 .' [/INST] [Research:O, presented:O, in:O, this:O, report:O, shows:O, that:O, the:O, PUTTER:B-HackOrg, PANDA:I-HackOrg, operators:B-HackOrg, are:O, likely:O, members:O, of:O, the:O, 12th:O, Bureau:O, ,:O, 3rd:O, General:O, Staff:O, Department:O, (:O, GSD:O, ):O, of:O, the:O, People:B-Idus, 's:I-Idus, Liberation:I-Idus, Army:I-Idus, (:O, PLA:B-Idus, ):O, ,:O, operating:O, from:O, the:O, unit:O, 's:O, headquarters:O, in:O, Shanghai:B-Area, with:O, MUCD:B-HackOrg, 61486:I-HackOrg, .: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 this group is mostly targeting businesses is apparent from the processes they are looking for on a compromised system .' [/INST] [That:O, this:O, group:B-HackOrg, is:O, mostly:O, targeting:O, businesses:B-Idus, is:O, apparent:O, from:O, the:O, processes:O, they:O, are:O, looking:O, for:O, on:O, a:O, compromised: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: 'They are both targeting businesses using accounting software , are fingerprinting systems of interest similarly , are looking for smart card readers , and finally , they deploy an array of malicious tools to spy on their victims .' [/INST] [They:O, are:O, both:O, targeting:O, businesses:B-Idus, using:O, accounting:O, software:O, ,:O, are:O, fingerprinting:O, systems:O, of:O, interest:O, similarly:O, ,:O, are:O, looking:O, for:O, smart:O, card:O, readers:O, ,:O, and:O, finally:O, ,:O, they:O, deploy:O, an:O, array:O, of:O, malicious:O, tools:O, to:O, spy:O, on:O, their:O, victims: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 adversary has been identified leveraging custom-developed plugins for versions 2 and 3 of the commodity malware Black Energy to target entities associated with energy , industrial control systems and SCADA , government , and media for espionage and destructive purposes , since at least 2011 .' [/INST] [This:O, adversary:O, has:O, been:O, identified:O, leveraging:O, custom-developed:O, plugins:O, for:O, versions:O, 2:O, and:O, 3:O, of:O, the:O, commodity:O, malware:O, Black:B-Tool, Energy:I-Tool, to:O, target:O, entities:O, associated:O, with:O, energy:B-Idus, ,:O, industrial:O, control:O, systems:O, and:O, SCADA:O, ,:O, government:B-Idus, ,:O, and:O, media:B-Idus, for:O, espionage:B-HackOrg, and:O, destructive:O, purposes:O, ,:O, since:O, at:O, least:O, 2011:B-Time, .: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 adversary has been identified leveraging custom-developed plugins for versions 2 and 3 of the commodity malware Black Energy to target entities associated with energy , government , and media for espionage and destructive purposes , since at least 2011 .' [/INST] [This:O, adversary:O, has:O, been:O, identified:O, leveraging:O, custom-developed:O, plugins:O, for:O, versions:O, 2:O, and:O, 3:O, of:O, the:O, commodity:O, malware:O, Black:B-Tool, Energy:I-Tool, to:O, target:O, entities:O, associated:O, with:O, energy:B-Idus, ,:O, government:B-Idus, ,:O, and:O, media:B-Idus, for:O, espionage:B-HackOrg, and:O, destructive:O, purposes:O, ,:O, since:O, at:O, least:O, 2011:B-Time, .: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 haven't heard about it for some reason , I would recommend to read this detailed report by Group-IB , as this APT attacks not only Russian banks , but also banks in more than 25 countries .' [/INST] [If:O, you:O, haven't:O, heard:O, about:O, it:O, for:O, some:O, reason:O, ,:O, I:O, would:O, recommend:O, to:O, read:O, this:O, detailed:O, report:O, by:O, Group-IB:B-SecTeam, ,:O, as:O, this:O, APT:B-OffAct, attacks:I-OffAct, not:O, only:O, Russian:B-Area, banks:B-Idus, ,:O, but:O, also:O, banks:B-Idus, in:O, more:O, than:O, 25:O, countries: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 credentials they use to register their malware infrastructure are easily associated with their public social media accounts on Google® , Facebook® , MySpace® , Instagram® , and various dating and blogging sites .' [/INST] [The:O, credentials:O, they:O, use:O, to:O, register:O, their:O, malware:O, infrastructure:O, are:O, easily:O, associated:O, with:O, their:O, public:O, social:B-Idus, media:I-Idus, accounts:O, on:O, Google®:B-Org, ,:O, Facebook®:B-Org, ,:O, MySpace®:B-Org, ,:O, Instagram®:B-Org, ,:O, and:O, various:O, dating:B-Org, and:I-Org, blogging:I-Org, sites:I-Org, .: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 previously detected groups we suspect are affiliated with the North Korean government compromising electric utilities in South Korea , but these compromises did not lead to a disruption of the power supply .' [/INST] [We:O, have:O, previously:O, detected:O, groups:B-HackOrg, we:O, suspect:O, are:O, affiliated:O, with:O, the:O, North:B-Area, Korean:I-Area, government:B-Org, compromising:O, electric:B-Idus, utilities:O, in:O, South:B-Area, Korea:I-Area, ,:O, but:O, these:O, compromises:O, did:O, not:O, lead:O, to:O, a:O, disruption:O, of:O, the:O, power:O, supply: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: 'North Korea linked hackers are among the most prolific nation-state threats , targeting not only the U.S. and South Korea but the global financial system and nations worldwide .' [/INST] [North:B-Area, Korea:I-Area, linked:O, hackers:O, are:O, among:O, the:O, most:O, prolific:O, nation-state:O, threats:O, ,:O, targeting:O, not:O, only:O, the:B-Area, U.S.:I-Area, and:O, South:B-Area, Korea:I-Area, but:O, the:O, global:O, financial:B-Idus, system:O, and:O, nations:B-Idus, worldwide: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: 'CapabilitiesFormBook is a data stealer , but not a full-fledged banker .' [/INST] [CapabilitiesFormBook:B-Idus, is:O, a:O, data:O, stealer:O, ,:O, but:O, not:O, a:O, full-fledged:O, banker:B-Org, .: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: 'Furthermore , there are indications that APT32 actors are targeting peripheral network security and technology infrastructure corporations .' [/INST] [Furthermore:O, ,:O, there:O, are:O, indications:O, that:O, APT32:B-HackOrg, actors:O, are:O, targeting:O, peripheral:O, network:O, security:B-Idus, and:O, technology:B-Idus, infrastructure:O, corporations: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 targeting of private sector interests by APT32 is notable and FireEye believes the actor poses significant risk to companies doing business in , or preparing to invest in , the country .' [/INST] [The:O, targeting:O, of:O, private:O, sector:O, interests:O, by:O, APT32:B-HackOrg, is:O, notable:O, and:O, FireEye:B-SecTeam, believes:O, the:O, actor:O, poses:O, significant:O, risk:O, to:O, companies:O, doing:O, business:B-Idus, in:O, ,:O, or:O, preparing:O, to:O, invest:O, in:O, ,:O, the:O, country: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 the motivation for each APT32 private sector compromise varied – and in some cases was unknown – the unauthorized access could serve as a platform for law enforcement , intellectual property theft , or anticorruption measures that could ultimately erode the competitive advantage of targeted organizations .' [/INST] [While:O, the:O, motivation:O, for:O, each:O, APT32:B-HackOrg, private:O, sector:O, compromise:O, varied:O, –:O, and:O, in:O, some:O, cases:O, was:O, unknown:O, –:O, the:O, unauthorized:O, access:O, could:O, serve:O, as:O, a:O, platform:O, for:O, law:B-Idus, enforcement:I-Idus, ,:O, intellectual:O, property:O, theft:O, ,:O, or:O, anticorruption:O, measures:O, that:O, could:O, ultimately:O, erode:O, the:O, competitive:O, advantage:O, of:O, targeted:O, organizations: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 use of the CARBANAK malware in FIN7 operations also provides limited evidence that these campaigns are linked to previously observed CARBANAK operations leading to fraudulent banking transactions , ATM compromise , and other monetization schemes .' [/INST] [The:O, use:O, of:O, the:O, CARBANAK:B-Tool, malware:I-Tool, in:O, FIN7:B-HackOrg, operations:O, also:O, provides:O, limited:O, evidence:O, that:O, these:O, campaigns:O, are:O, linked:O, to:O, previously:O, observed:O, CARBANAK:B-OffAct, operations:I-OffAct, leading:O, to:O, fraudulent:O, banking:B-Idus, transactions:I-Idus, ,:O, ATM:O, compromise:O, ,:O, and:O, other:O, monetization:O, schemes: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 our M-Trends 2017 report , we took a look at the incidents we investigated last year and provided a global and regional (the Americas , APAC and EMEA) analysis focused on attack trends , and defensive and emerging trends .' [/INST] [For:O, our:O, M-Trends:B-SecTeam, 2017:B-Time, report:O, ,:O, we:O, took:O, a:O, look:O, at:O, the:O, incidents:O, we:O, investigated:O, last:B-Time, year:I-Time, and:O, provided:O, a:O, global:O, and:O, regional:O, (the:O, Americas:B-Area, ,:O, APAC:O, and:O, EMEA):O, analysis:O, focused:O, on:O, attack:O, trends:O, ,:O, and:O, defensive:B-Idus, and:O, emerging:B-Idus, trends: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 April 2015 , we uncovered the malicious efforts of APT30 , a suspected China-based threat group that has exploited the networks of governments and organizations across the region , targeting highly sensitive political , economic and military information .' [/INST] [In:O, April:B-Time, 2015:I-Time, ,:O, we:O, uncovered:O, the:O, malicious:O, efforts:O, of:O, APT30:B-HackOrg, ,:O, a:O, suspected:O, China-based:B-Area, threat:O, group:O, that:O, has:O, exploited:O, the:O, networks:O, of:O, governments:B-Org, and:O, organizations:B-Org, across:O, the:O, region:O, ,:O, targeting:O, highly:O, sensitive:B-Idus, political:I-Idus, ,:O, economic:B-Idus, and:O, military:B-Idus, 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: 'Yet the document cache published April 8 provides evidence that the NSA had once launched a series of successful computer-based intrusions against multiple high-profile foreign targets , including the Office of the President of Iran and the Russian Federal Nuclear Center .' [/INST] [Yet:O, the:O, document:O, cache:O, published:O, April:O, 8:O, provides:O, evidence:O, that:O, the:O, NSA:B-Org, had:O, once:O, launched:O, a:O, series:O, of:O, successful:O, computer-based:O, intrusions:O, against:O, multiple:O, high-profile:O, foreign:O, targets:O, ,:O, including:O, the:O, Office:O, of:O, the:O, President:O, of:O, Iran:O, and:O, the:O, Russian:B-Area, Federal:I-Idus, Nuclear:I-Idus, Center:I-Idus, .: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: 'Emotet activity in 2019 included several high-volume campaigns that collectively distributed tens of millions of messages primarily targeting the manufacturing and healthcare industries .' [/INST] [Emotet:B-OffAct, activity:I-OffAct, in:O, 2019:O, included:O, several:O, high-volume:O, campaigns:B-OffAct, that:O, collectively:O, distributed:O, tens:O, of:O, millions:O, of:O, messages:O, primarily:O, targeting:O, the:O, manufacturing:B-Idus, and:O, healthcare:B-Idus, industries:I-Idus, .: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: 'Originally targeting Western European banks , Emotet has since been developed into a robust global botnet that is comprised of several modules , each of which equips Emotet with different spamming , email logging , information stealing , bank fraud , downloading , and DDoS , among others .' [/INST] [Originally:O, targeting:O, Western:B-Area, European:I-Area, banks:B-Idus, ,:O, Emotet:B-Tool, has:O, since:O, been:O, developed:O, into:O, a:O, robust:O, global:O, botnet:O, that:O, is:O, comprised:O, of:O, several:O, modules:O, ,:O, each:O, of:O, which:O, equips:O, Emotet:B-Tool, with:O, different:O, spamming:O, ,:O, email:B-Features, logging:I-Features, ,:O, information:B-Features, stealing:I-Features, ,:O, bank:B-Features, fraud:I-Features, ,:O, downloading:B-Features, ,:O, and:O, DDoS:B-Features, ,:O, among: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: 'Originally targeting Western European banks , it has since been developed into a robust global botnet that is comprised of several modules , each of which equips Emotet with different spamming , email logging , information stealing , bank fraud , downloading , and DDoS , among others .' [/INST] [Originally:O, targeting:O, Western:B-Area, European:I-Area, banks:B-Idus, ,:O, it:O, has:O, since:O, been:O, developed:O, into:O, a:O, robust:O, global:O, botnet:O, that:O, is:O, comprised:O, of:O, several:O, modules:O, ,:O, each:O, of:O, which:O, equips:O, Emotet:B-Tool, with:O, different:O, spamming:O, ,:O, email:B-Features, logging:I-Features, ,:O, information:B-Features, stealing:I-Features, ,:O, bank:B-Features, fraud:I-Features, ,:O, downloading:B-Features, ,:O, and:O, DDoS:B-Features, ,:O, among: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: 'Transparent Tribe has been active for several years and conducting suspected intelligence collection operations against South Asian political and military targets .' [/INST] [Transparent:O, Tribe:O, has:O, been:O, active:O, for:O, several:O, years:O, and:O, conducting:O, suspected:O, intelligence:B-Purp, collection:I-Purp, operations:O, against:O, South:O, Asian:O, political:B-Idus, and:O, military:B-Idus, 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: 'In previous incidents involving this threat actor , we observed them using malicious documents hosted on websites about the Indian Army , instead of sending these documents directly as an email attachment .' [/INST] [In:O, previous:O, incidents:O, involving:O, this:O, threat:O, actor:O, ,:O, we:O, observed:O, them:O, using:O, malicious:O, documents:O, hosted:O, on:O, websites:O, about:O, the:O, Indian:B-Idus, Army:I-Idus, ,:O, instead:O, of:O, sending:O, these:O, documents:O, directly:O, as:O, an:O, email:B-Way, attachment:I-Way, .: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: 'To date , Whitefly has attacked organizations in the healthcare , media , telecommunications , and engineering sectors .' [/INST] [To:O, date:O, ,:O, Whitefly:B-HackOrg, has:O, attacked:O, organizations:O, in:O, the:O, healthcare:B-Idus, ,:O, media:B-Idus, ,:O, telecommunications:B-Idus, ,:O, and:O, engineering:B-Org, sectors:I-Org, .: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: 'Between May 2017 and December 2018 , a multi-purpose command tool that has been used by Whitefly was also used in attacks against defense , telecoms , and energy targets in Southeast Asia and Russia .' [/INST] [Between:O, May:B-Time, 2017:I-Time, and:O, December:B-Time, 2018:I-Time, ,:O, a:O, multi-purpose:O, command:O, tool:O, that:O, has:O, been:O, used:O, by:O, Whitefly:B-HackOrg, was:O, also:O, used:O, in:O, attacks:B-OffAct, against:O, defense:B-Idus, ,:O, telecoms:B-Idus, ,:O, and:O, energy:B-Idus, targets:O, in:O, Southeast:B-Area, Asia:I-Area, and:O, Russia:B-Area, .: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 malicious documents seen in recent activity refer to a number of topics , including recent military promotions within the Pakistan Army , information related to the Pakistan Atomic Energy Commission , as well as Pakistan 's Ministry of the Interior .' [/INST] [The:O, malicious:B-SamFile, documents:I-SamFile, seen:O, in:O, recent:O, activity:B-OffAct, refer:O, to:O, a:O, number:O, of:O, topics:O, ,:O, including:O, recent:O, military:O, promotions:O, within:O, the:O, Pakistan:B-Idus, Army:I-Idus, ,:O, information:O, related:O, to:O, the:O, Pakistan:B-Area, Atomic:O, Energy:O, Commission:O, ,:O, as:O, well:O, as:O, Pakistan:B-Area, 's:O, Ministry:O, of:O, the:O, Interior: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 believe that the Carbanak campaign is a clear indicator of a new era in cybercrime in which criminals use APT techniques directly against the financial industry instead of through its customers .' [/INST] [We:O, believe:O, that:O, the:O, Carbanak:B-Exp, campaign:B-OffAct, is:O, a:O, clear:O, indicator:O, of:O, a:O, new:O, era:O, in:O, cybercrime:O, in:O, which:O, criminals:B-HackOrg, use:O, APT:B-Way, techniques:I-Way, directly:O, against:O, the:O, financial:B-Idus, industry:I-Idus, instead:O, of:O, through:O, its:O, customers:B-Org, .: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 believe that the Carbanak campaign is a clear indicator of a new era in cybercrime in which criminals use APT techniques directly against the financial industry instead of through its customers .' [/INST] [We:O, believe:O, that:O, the:O, Carbanak:B-Exp, campaign:B-OffAct, is:O, a:O, clear:O, indicator:O, of:O, a:O, new:O, era:O, in:O, cybercrime:O, in:O, which:O, criminals:B-HackOrg, use:O, APT:B-Way, techniques:I-Way, directly:O, against:O, the:O, financial:B-Idus, industry:I-Idus, instead:O, of:O, through:O, its:O, customers:B-Org, .: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: 'APT41 has targeted payment services specializing in handling in-game transactions and real money transfer (RMT) purchases .' [/INST] [APT41:B-HackOrg, has:O, targeted:O, payment:B-Idus, services:I-Idus, specializing:O, in:O, handling:B-Purp, in-game:I-Purp, transactions:I-Purp, and:O, real:O, money:O, transfer:O, (RMT):O, purchases: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 group behind these attacks has stolen gigabytes of confidential documents , mostly from military organizations .' [/INST] [The:O, group:B-HackOrg, behind:O, these:O, attacks:O, has:O, stolen:B-Purp, gigabytes:I-Purp, of:O, confidential:O, documents:O, ,:O, mostly:O, from:O, military:B-Idus, organizations:B-Idus, .: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: 'They seem to have specialized knowledge about military operations , as they are focused on stealing specific files such as those that describe navigation routes .' [/INST] [They:B-HackOrg, seem:O, to:O, have:O, specialized:O, knowledge:O, about:O, military:B-Idus, operations:O, ,:O, as:O, they:O, are:O, focused:O, on:O, stealing:B-Purp, specific:I-Purp, files:I-Purp, such:O, as:O, those:O, that:O, describe:B-Purp, navigation:I-Purp, routes:I-Purp, .: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: 'Early in Q2 , Kaspersky identified an interesting Lazarus attack targeting a mobile gaming company in South Korea that we believe was aimed at stealing application source code .' [/INST] [Early:O, in:O, Q2:O, ,:O, Kaspersky:B-SecTeam, identified:O, an:O, interesting:O, Lazarus:B-HackOrg, attack:O, targeting:O, a:O, mobile:B-Idus, gaming:I-Idus, company:O, in:O, South:B-Area, Korea:I-Area, that:O, we:O, believe:O, was:O, aimed:O, at:O, stealing:B-Purp, application:B-Purp, source:O, code: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 believe that the Carbanak campaign is a clear indicator of a new era in cybercrime in which criminals use APT techniques directly against the financial industry instead of through its customers .' [/INST] [We:O, believe:O, that:O, the:O, Carbanak:B-Exp, campaign:B-OffAct, is:O, a:O, clear:O, indicator:O, of:O, a:O, new:O, era:O, in:O, cybercrime:O, in:O, which:O, criminals:B-HackOrg, use:O, APT:B-Way, techniques:I-Way, directly:O, against:O, the:O, financial:B-Idus, industry:I-Idus, instead:O, of:O, through:O, its:O, customers: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 believe that the Carbanak campaign is a clear indicator of a new era in cybercrime in which criminals use APT techniques directly against the financial industry instead of through its customers .' [/INST] [We:O, believe:O, that:O, the:O, Carbanak:B-Exp, campaign:B-OffAct, is:O, a:O, clear:O, indicator:O, of:O, a:O, new:O, era:O, in:O, cybercrime:O, in:O, which:O, criminals:B-HackOrg, use:O, APT:B-Way, techniques:I-Way, directly:O, against:O, the:O, financial:B-Idus, industry:I-Idus, instead:O, of:O, through:O, its:O, customers: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 believe that these industries have also been targeted as part of a larger supply-chain attack in order for Orangeworm to get access to their intended victims related to healthcare .' [/INST] [We:O, believe:O, that:O, these:O, industries:O, have:O, also:O, been:O, targeted:O, as:O, part:O, of:O, a:O, larger:O, supply-chain:B-OffAct, attack:I-OffAct, in:O, order:O, for:O, Orangeworm:O, to:O, get:O, access:O, to:O, their:O, intended:O, victims:O, related:O, to:O, healthcare:B-Idus, .: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: 'Orangeworm 's secondary targets include Manufacturing , Information Technology , Agriculture , and Logistics .' [/INST] [Orangeworm:O, 's:O, secondary:O, targets:O, include:O, Manufacturing:B-Idus, ,:O, Information:B-Idus, Technology:I-Idus, ,:O, Agriculture:B-Idus, ,:O, and:O, Logistics:B-Idus, .: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 these industries may appear to be unrelated , we found them to have multiple links to healthcare , such as large manufacturers that produce medical imaging devices sold directly into healthcare firms , IT organizations that provide support services to medical clinics , and logistical organizations that deliver healthcare products .' [/INST] [While:O, these:O, industries:O, may:O, appear:O, to:O, be:O, unrelated:O, ,:O, we:O, found:O, them:O, to:O, have:O, multiple:O, links:O, to:O, healthcare:B-Idus, ,:O, such:O, as:O, large:O, manufacturers:O, that:O, produce:O, medical:O, imaging:O, devices:O, sold:O, directly:O, into:O, healthcare:B-Org, firms:I-Org, ,:O, IT:B-Org, organizations:I-Org, that:O, provide:O, support:O, services:O, to:O, medical:B-Org, clinics:I-Org, ,:O, and:O, logistical:B-Org, organizations:I-Org, that:O, deliver:O, healthcare:B-Idus, products: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: 'Patchwork targets were chosen worldwide with a focus on personnel working on military and political assignments , and specifically those working on issues relating to Southeast Asia and the South China Sea .' [/INST] [Patchwork:B-HackOrg, targets:O, were:O, chosen:O, worldwide:O, with:O, a:O, focus:O, on:O, personnel:B-Org, working:O, on:O, military:B-Idus, and:O, political:B-Idus, assignments:O, ,:O, and:O, specifically:O, those:O, working:O, on:O, issues:O, relating:O, to:O, Southeast:B-Area, Asia:I-Area, and:O, the:O, South:B-Area, China:I-Area, Sea:I-Area, .: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: 'Patchwork ( also known as Dropping Elephant ) is a cyberespionage group whose targets included diplomatic and government agencies as well as businesses .' [/INST] [Patchwork:B-HackOrg, (:O, also:O, known:O, as:O, Dropping:B-HackOrg, Elephant:I-HackOrg, ):O, is:O, a:O, cyberespionage:B-HackOrg, group:I-HackOrg, whose:O, targets:O, included:O, diplomatic:B-Org, and:O, government:B-Org, agencies:I-Org, as:O, well:O, as:O, businesses:B-Idus, .: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: 'Dropping Elephant ( also known as , Chinastrats , and , Patchwork , ) is a relatively new threat actor that is targeting a variety of high profile diplomatic and economic targets using a custom set of attack tools .' [/INST] [Dropping:B-HackOrg, Elephant:I-HackOrg, (:O, also:O, known:O, as:O, ,:O, Chinastrats:B-HackOrg, ,:O, and:O, ,:O, Patchwork:B-HackOrg, ,:O, ):O, is:O, a:O, relatively:O, new:O, threat:B-HackOrg, actor:I-HackOrg, that:O, is:O, targeting:O, a:O, variety:O, of:O, high:O, profile:O, diplomatic:B-Idus, and:O, economic:B-Idus, targets:O, using:O, a:O, custom:O, set:O, of:O, attack:O, tools: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 case , a small group reusing exploit code , some powershell-based malware and mostly social engineering has been able to steal sensitive documents and data from victims since at least November 2015 .' [/INST] [In:O, this:O, case:O, ,:O, a:O, small:O, group:B-HackOrg, reusing:O, exploit:O, code:O, ,:O, some:O, powershell-based:B-Tool, malware:I-Tool, and:O, mostly:O, social:B-Idus, engineering:I-Idus, has:O, been:O, able:O, to:O, steal:B-Purp, sensitive:I-Purp, documents:I-Purp, and:I-Purp, data:I-Purp, from:O, victims:O, since:O, at:O, least:O, November:B-Time, 2015:I-Time, .: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 malicious documents seen in recent activity refer to a number of topics , including recent military promotions within the Pakistan Army , information related to the Pakistan Atomic Energy Commission , as well as Pakistan 's Ministry of the Interior .' [/INST] [The:O, malicious:B-SamFile, documents:I-SamFile, seen:O, in:O, recent:O, activity:B-OffAct, refer:O, to:O, a:O, number:O, of:O, topics:O, ,:O, including:O, recent:O, military:O, promotions:O, within:O, the:O, Pakistan:B-Idus, Army:I-Idus, ,:O, information:O, related:O, to:O, the:O, Pakistan:B-Area, Atomic:O, Energy:O, Commission:O, ,:O, as:O, well:O, as:O, Pakistan:B-Area, 's:O, Ministry:O, of:O, the:O, Interior: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: 'PittyTiger leverages social engineering to deliver spearphishing emails , in a variety of languages including English , French and Chinese , and email phishing pages to their targets .' [/INST] [PittyTiger:B-HackOrg, leverages:O, social:B-Idus, engineering:I-Idus, to:O, deliver:O, spearphishing:B-Way, emails:I-Way, ,:O, in:O, a:O, variety:O, of:O, languages:O, including:O, English:O, ,:O, French:O, and:O, Chinese:B-Area, ,:O, and:O, email:B-Way, phishing:I-Way, pages:I-Way, to:O, their: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: 'The previous two volumes of the Microsoft Security Intelligence Report explored the activities of two such groups , code-named STRONTIUM and PLATINUM , which used previously unknown vulnerabilities and aggressive , persistent techniques to target specific individuals and institutions — often including military installations , intelligence agencies , and other government bodies .' [/INST] [The:O, previous:O, two:O, volumes:O, of:O, the:O, Microsoft:O, Security:O, Intelligence:O, Report:O, explored:O, the:O, activities:B-OffAct, of:O, two:O, such:O, groups:B-HackOrg, ,:O, code-named:O, STRONTIUM:B-HackOrg, and:O, PLATINUM:B-HackOrg, ,:O, which:O, used:O, previously:O, unknown:O, vulnerabilities:O, and:O, aggressive:O, ,:O, persistent:O, techniques:O, to:O, target:O, specific:B-Org, individuals:I-Org, and:O, institutions:B-Org, —:O, often:O, including:O, military:B-Idus, installations:O, ,:O, intelligence:B-Org, agencies:I-Org, ,:O, and:O, other:O, government:B-Idus, bodies: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 particular unit is believed to hack into victim companies throughout the world in order to steal corporate trade secrets , primarily relating to the satellite , aerospace and communication industries .' [/INST] [This:O, particular:O, unit:O, is:O, believed:O, to:O, hack:O, into:O, victim:O, companies:O, throughout:O, the:O, world:O, in:O, order:O, to:O, steal:B-Purp, corporate:I-Purp, trade:I-Purp, secrets:I-Purp, ,:O, primarily:B-Purp, relating:I-Purp, to:I-Purp, the:I-Purp, satellite:I-Purp, ,:O, aerospace:B-Idus, and:O, communication:B-Idus, industries:I-Idus, .: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: 'PUTTER PANDA is a determined adversary group , conducting intelligence-gathering operations targeting the Government , Defense , Research , and Technology sectors in the United States , with specific targeting of the US Defense and European satellite and aerospace industries .' [/INST] [PUTTER:B-HackOrg, PANDA:I-HackOrg, is:O, a:O, determined:O, adversary:O, group:B-HackOrg, ,:O, conducting:O, intelligence-gathering:B-Purp, operations:O, targeting:O, the:O, Government:B-Idus, ,:O, Defense:B-Idus, ,:O, Research:B-Idus, ,:O, and:O, Technology:B-Org, sectors:I-Org, in:O, the:O, United:B-Area, States:I-Area, ,:O, with:O, specific:O, targeting:O, of:O, the:O, US:B-Idus, Defense:I-Idus, and:O, European:B-Area, satellite:B-Idus, and:O, aerospace:B-Idus, industries:I-Idus, .: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 2015 and 2016 , Dridex was one of the most prolific eCrime banking trojans on the market and , since 2014 , those efforts are thought to have netted INDRIK SPIDER millions of dollars in criminal profits .' [/INST] [In:O, 2015:B-Time, and:O, 2016:B-Time, ,:O, Dridex:B-Tool, was:O, one:O, of:O, the:O, most:O, prolific:O, eCrime:O, banking:B-Idus, trojans:O, on:O, the:O, market:O, and:O, ,:O, since:O, 2014:B-Time, ,:O, those:O, efforts:O, are:O, thought:O, to:O, have:O, netted:O, INDRIK:B-HackOrg, SPIDER:I-HackOrg, millions:O, of:O, dollars:O, in:O, criminal:O, profits: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 August 2017 , a new ransomware variant identified as BitPaymer was reported to have ransomed the U.K. 's National Health Service ( NHS ) , with a high ransom demand of 53 BTC ( approximately $200,000 USD ) .' [/INST] [In:O, August:B-Time, 2017:I-Time, ,:O, a:O, new:O, ransomware:O, variant:O, identified:O, as:O, BitPaymer:B-Tool, was:O, reported:O, to:O, have:O, ransomed:O, the:O, U.K.:B-Area, 's:O, National:B-Idus, Health:I-Idus, Service:I-Idus, (:O, NHS:B-Idus, ):O, ,:O, with:O, a:O, high:O, ransom:O, demand:O, of:O, 53:O, BTC:O, (:O, approximately:O, $200,000:O, USD: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: 'Known for hijacking prominent social media accounts , the self-styled white hat hacking group OurMine took over a number of verified Twitter and Facebook accounts belonging to the cable network .' [/INST] [Known:O, for:O, hijacking:O, prominent:O, social:B-Idus, media:I-Idus, accounts:O, ,:O, the:O, self-styled:O, white:O, hat:O, hacking:O, group:O, OurMine:O, took:O, over:O, a:O, number:O, of:O, verified:O, Twitter:B-Org, and:O, Facebook:B-Org, accounts:O, belonging:O, to:O, the:O, cable:O, network: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: 'Through research , 360 Helios Team has found that , since 2007 , the Poison Ivy Group has carried out 11 years of cyber espionage campaigns against Chinese key units and departments , such as national defense , government , science and technology , education and maritime agencies .' [/INST] [Through:O, research:O, ,:O, 360:B-SecTeam, Helios:I-SecTeam, Team:I-SecTeam, has:O, found:O, that:O, ,:O, since:O, 2007:B-Time, ,:O, the:O, Poison:B-HackOrg, Ivy:I-HackOrg, Group:I-HackOrg, has:O, carried:O, out:O, 11:O, years:O, of:O, cyber:O, espionage:O, campaigns:B-OffAct, against:O, Chinese:B-Area, key:O, units:O, and:O, departments:O, ,:O, such:O, as:O, national:B-Idus, defense:I-Idus, ,:O, government:B-Org, ,:O, science:B-Idus, and:O, technology:B-Idus, ,:O, education:B-Idus, and:O, maritime:B-Org, agencies:I-Org, .: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: 'Dragos has reported that XENOTIME , the APT group behind the TRISIS (aka TRITON and HatMan) attack on a Saudi Arabian petro-chemical facility in 2017 , has expanded its focus beyond the oil and gas industries .' [/INST] [Dragos:B-SecTeam, has:O, reported:O, that:O, XENOTIME:B-HackOrg, ,:O, the:O, APT:O, group:O, behind:O, the:O, TRISIS:B-HackOrg, (aka:O, TRITON:O, and:O, HatMan):O, attack:O, on:O, a:O, Saudi:B-Area, Arabian:I-Area, petro-chemical:O, facility:O, in:O, 2017:B-Time, ,:O, has:O, expanded:O, its:O, focus:O, beyond:O, the:O, oil:B-Idus, and:O, gas:B-Idus, industries:I-Idus, .: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: 'Known targets of this group have been involved in the maritime industry , as well as engineering-focused entities , and include research institutes , academic organizations , and private firms in the United States .' [/INST] [Known:O, targets:O, of:O, this:O, group:B-HackOrg, have:O, been:O, involved:O, in:O, the:O, maritime:B-Idus, industry:O, ,:O, as:O, well:O, as:O, engineering-focused:O, entities:O, ,:O, and:O, include:O, research:B-Idus, institutes:I-Idus, ,:O, academic:B-Idus, organizations:I-Idus, ,:O, and:O, private:B-Org, firms:I-Org, in:O, the:B-Area, United:I-Area, States:I-Area, .: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: 'Historically , the majority of their targeting has been focused on the South Korean government , military , and defense industrial base .' [/INST] [Historically:O, ,:O, the:O, majority:O, of:O, their:O, targeting:O, has:O, been:O, focused:O, on:O, the:O, South:B-Org, Korean:I-Org, government:I-Org, ,:O, military:B-Idus, ,:O, and:O, defense:B-Idus, industrial:O, base: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: 'Historically , the majority of their targeting has been focused on the South Korean government , military , and defense industrial base .' [/INST] [Historically:O, ,:O, the:O, majority:O, of:O, their:O, targeting:O, has:O, been:O, focused:O, on:O, the:O, South:B-Org, Korean:I-Org, government:I-Org, ,:O, military:B-Idus, ,:O, and:O, defense:B-Idus, industrial:O, base: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: 'TEMP.Periscope BackgroundActive since at least 2013 , TEMP.Periscope has primarily focused on maritime-related targets across multiple verticals , including engineering firms , shipping and transportation , manufacturing , defense , government offices , and research universities .' [/INST] [TEMP.Periscope:B-HackOrg, BackgroundActive:O, since:O, at:O, least:O, 2013:B-Time, ,:O, TEMP.Periscope:B-HackOrg, has:O, primarily:O, focused:O, on:O, maritime-related:B-Idus, targets:O, across:O, multiple:O, verticals:O, ,:O, including:O, engineering:B-Org, firms:I-Org, ,:O, shipping:B-Idus, and:O, transportation:B-Idus, ,:O, manufacturing:B-Idus, ,:O, defense:B-Idus, ,:O, government:B-Org, offices:O, ,:O, and:O, research:B-Org, universities:I-Org, .: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: 'TEMP.Periscope BackgroundActive since at least 2013 , TEMP.Periscope has primarily focused on maritime-related targets across multiple verticals , including engineering firms , shipping and transportation , manufacturing , defense , government offices , and research universities .' [/INST] [TEMP.Periscope:B-HackOrg, BackgroundActive:O, since:O, at:O, least:O, 2013:B-Time, ,:O, TEMP.Periscope:B-HackOrg, has:O, primarily:O, focused:O, on:O, maritime-related:B-Idus, targets:O, across:O, multiple:O, verticals:O, ,:O, including:O, engineering:B-Org, firms:I-Org, ,:O, shipping:B-Idus, and:O, transportation:B-Idus, ,:O, manufacturing:B-Idus, ,:O, defense:B-Idus, ,:O, government:B-Org, offices:O, ,:O, and:O, research:B-Org, universities:I-Org, .: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: 'These malware families have a rich history of being used in many targeted attacks against government and private organizations .' [/INST] [These:O, malware:B-SamFile, families:O, have:O, a:O, rich:O, history:O, of:O, being:O, used:O, in:O, many:O, targeted:O, attacks:O, against:O, government:B-Idus, and:O, private:B-Idus, organizations:B-Idus, .: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 same time frame , APT10 also targeted a U.S. law firm and an international apparel company , likely to gather information for commercial advantage .' [/INST] [In:O, this:O, same:O, time:O, frame:O, ,:O, APT10:B-HackOrg, also:O, targeted:O, a:O, U.S.:B-Idus, law:I-Idus, firm:I-Idus, and:O, an:O, international:O, apparel:B-Idus, company:I-Idus, ,:O, likely:O, to:O, gather:B-Purp, information:I-Purp, for:O, commercial:O, advantage: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 admin@338 has largely targeted organizations involved in financial , economic and trade policy , typically using publicly available RATs such as Poison Ivy , as well some non-public backdoors .' [/INST] [The:O, admin@338:B-HackOrg, has:O, largely:O, targeted:O, organizations:O, involved:O, in:O, financial:B-Idus, ,:O, economic:B-Idus, and:O, trade:B-Idus, policy:I-Idus, ,:O, typically:O, using:O, publicly:B-Tool, available:I-Tool, RATs:I-Tool, such:O, as:O, Poison:B-Tool, Ivy:I-Tool, ,:O, as:O, well:O, some:O, non-public:B-Tool, backdoors:I-Tool, .: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 admin@338 started targeting Hong Kong media companies , probably in response to political and economic challenges in Hong Kong and China .' [/INST] [The:O, admin@338:B-HackOrg, started:O, targeting:O, Hong:B-Area, Kong:I-Area, media:B-Org, companies:I-Org, ,:O, probably:O, in:O, response:O, to:O, political:B-Idus, and:O, economic:B-Idus, challenges:O, in:O, Hong:B-Area, Kong:I-Area, and:O, China:B-Area, .: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 admin@338 linked to China and alleged to be responsible for targeted attacks against foreign governments and ministries , has now pointed its focus inward at China autonomous territory Hong Kong .' [/INST] [The:O, admin@338:B-HackOrg, linked:O, to:O, China:B-Area, and:O, alleged:O, to:O, be:O, responsible:O, for:O, targeted:B-OffAct, attacks:I-OffAct, against:O, foreign:O, governments:B-Idus, and:O, ministries:I-Idus, ,:O, has:O, now:O, pointed:O, its:O, focus:O, inward:O, at:O, China:B-Area, autonomous:O, territory:O, Hong:B-Area, Kong:I-Area, .: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: 'An APT gang linked to China and alleged to be responsible for targeted attacks against foreign governments and ministries , has now pointed its focus inward at China autonomous territory Hong Kong .' [/INST] [An:O, APT:B-HackOrg, gang:B-HackOrg, linked:O, to:O, China:B-Area, and:O, alleged:O, to:O, be:O, responsible:O, for:O, targeted:B-OffAct, attacks:I-OffAct, against:O, foreign:O, governments:B-Idus, and:O, ministries:I-Idus, ,:O, has:O, now:O, pointed:O, its:O, focus:O, inward:O, at:O, China:B-Area, autonomous:O, territory:O, Hong:B-Area, Kong:I-Area, .: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 group targeting Hong Kong media outlets is called admin@338 and is known to researchers for using publicly available remote access Trojans such as Poison Ivy to attack government and financial firms specializing in global economic policy .' [/INST] [The:O, group:B-HackOrg, targeting:O, Hong:B-Area, Kong:I-Area, media:B-Idus, outlets:O, is:O, called:O, admin@338:B-HackOrg, and:O, is:O, known:O, to:O, researchers:O, for:O, using:O, publicly:O, available:O, remote:B-Tool, access:I-Tool, Trojans:I-Tool, such:O, as:O, Poison:B-Tool, Ivy:I-Tool, to:O, attack:O, government:B-Idus, and:O, financial:B-Org, firms:I-Org, specializing:O, in:O, global:B-Idus, economic:I-Idus, policy: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 agroup targeting Hong Kong media outlets is called admin@338 and is known to researchers for using publicly available remote access Trojans such as Poison Ivy to attack government and financial firms specializing in global economic policy .' [/INST] [The:O, agroup:B-HackOrg, targeting:O, Hong:B-Area, Kong:I-Area, media:B-Idus, outlets:O, is:O, called:O, admin@338:B-HackOrg, and:O, is:O, known:O, to:O, researchers:O, for:O, using:O, publicly:O, available:O, remote:B-Tool, access:I-Tool, Trojans:I-Tool, such:O, as:O, Poison:B-Tool, Ivy:I-Tool, to:O, attack:O, government:B-Idus, and:O, financial:B-Org, firms:I-Org, specializing:O, in:O, global:B-Idus, economic:I-Idus, policy: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 admin@338 , active since 2008 , has been seen targeting organizations in the financial services , telecoms , government , and defense sectors .' [/INST] [The:O, admin@338:B-HackOrg, ,:O, active:O, since:O, 2008:B-Time, ,:O, has:O, been:O, seen:O, targeting:O, organizations:O, in:O, the:O, financial:B-Idus, services:I-Idus, ,:O, telecoms:B-Idus, ,:O, government:B-Idus, ,:O, and:O, defense:B-Org, sectors:I-Org, .: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 APT actor , active since 2008 , has been seen targeting organizations in the financial services , telecoms , government , and defense sectors .' [/INST] [The:O, APT:B-HackOrg, actor:I-HackOrg, ,:O, active:O, since:O, 2008:B-Time, ,:O, has:O, been:O, seen:O, targeting:O, organizations:O, in:O, the:O, financial:B-Idus, services:I-Idus, ,:O, telecoms:B-Idus, ,:O, government:B-Idus, ,:O, and:O, defense:B-Org, sectors:I-Org, .: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: 'FireEye said it has tracked admin@338 's activity since 2013 and the group has largely targeted organizations involved in financial , economic , and trade policy .' [/INST] [FireEye:B-SecTeam, said:O, it:O, has:O, tracked:O, admin@338:B-HackOrg, 's:O, activity:B-OffAct, since:O, 2013:B-Time, and:O, the:O, group:B-HackOrg, has:O, largely:O, targeted:O, organizations:O, involved:O, in:O, financial:B-Idus, ,:O, economic:B-Idus, ,:O, and:O, trade:B-Idus, policy:I-Idus, .: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: 'They have largely targeted organizations involved in financial , economic and trade policy , typically using publicly available RATs such as Poison Ivy , as well some non-public backdoors .' [/INST] [They:O, have:O, largely:O, targeted:O, organizations:O, involved:O, in:O, financial:B-Idus, ,:O, economic:B-Idus, and:O, trade:B-Idus, policy:I-Idus, ,:O, typically:O, using:O, publicly:B-Tool, available:I-Tool, RATs:I-Tool, such:O, as:O, Poison:B-Tool, Ivy:I-Tool, ,:O, as:O, well:O, some:O, non-public:B-Tool, backdoors:I-Tool, .: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: 'Between November 26 , 2015 , and December 1 , 2015 , known and suspected China-based APT16 launched several spear phishing attacks targeting Japan and Taiwan in the high-tech , government services , media and financial services industries .' [/INST] [Between:O, November:B-Time, 26:I-Time, ,:I-Time, 2015:I-Time, ,:O, and:O, December:B-Time, 1:I-Time, ,:I-Time, 2015:I-Time, ,:O, known:O, and:O, suspected:O, China-based:B-Area, APT16:B-HackOrg, launched:O, several:O, spear:B-OffAct, phishing:I-OffAct, attacks:I-OffAct, targeting:O, Japan:B-Area, and:O, Taiwan:B-Area, in:O, the:O, high-tech:B-Idus, ,:O, government:B-Idus, services:I-Idus, ,:O, media:B-Idus, and:O, financial:B-Idus, services:I-Idus, industries:I-Idus, .: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: 'Between November 26 , 2015 , and December 1 , 2015 , known and suspected China-based APT groups launched several spear phishing attacks targeting Japanese and Taiwanese organizations in the high-tech , government services , media and financial services industries .' [/INST] [Between:O, November:B-Time, 26:I-Time, ,:I-Time, 2015:I-Time, ,:O, and:O, December:B-Time, 1:I-Time, ,:I-Time, 2015:I-Time, ,:O, known:O, and:O, suspected:O, China-based:B-Area, APT:B-HackOrg, groups:I-HackOrg, launched:O, several:O, spear:B-OffAct, phishing:I-OffAct, attacks:I-OffAct, targeting:O, Japanese:B-Area, and:O, Taiwanese:B-Area, organizations:O, in:O, the:O, high-tech:B-Idus, ,:O, government:B-Idus, services:I-Idus, ,:O, media:B-Idus, and:O, financial:B-Idus, services:I-Idus, industries:I-Idus, .: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: 'TG-0416 is a stealthy and extremely successful Advanced Persistent Threat ( APT ) group known to target a broad range of verticals since at least 2009 , including technology , industrial , manufacturing , human rights groups , government , pharmaceutical , and medical technology .' [/INST] [TG-0416:B-HackOrg, is:O, a:O, stealthy:O, and:O, extremely:O, successful:O, Advanced:B-HackOrg, Persistent:I-HackOrg, Threat:I-HackOrg, (:O, APT:B-HackOrg, ):O, group:I-HackOrg, known:O, to:O, target:O, a:O, broad:O, range:O, of:O, verticals:O, since:O, at:O, least:O, 2009:B-Time, ,:O, including:O, technology:B-Idus, ,:O, industrial:B-Idus, ,:O, manufacturing:B-Idus, ,:O, human:B-Org, rights:I-Org, groups:I-Org, ,:O, government:B-Idus, ,:O, pharmaceutical:B-Idus, ,:O, and:O, medical:B-Idus, technology:I-Idus, .: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: 'APT19 seemed to be going after defense sector firms , Chinese dissident groups and political , financial , pharmaceutical and energy sectors that could benefit the Chinese economy .' [/INST] [APT19:B-HackOrg, seemed:O, to:O, be:O, going:O, after:O, defense:B-Org, sector:I-Org, firms:I-Org, ,:O, Chinese:B-Area, dissident:O, groups:O, and:O, political:B-Idus, ,:O, financial:B-Idus, ,:O, pharmaceutical:B-Idus, and:O, energy:B-Org, sectors:I-Org, that:O, could:O, benefit:O, the:O, Chinese:B-Area, economy: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: 'APT19 seemed to be going after defense sector firms , Chinese dissident groups and other political target , as well as certain financial targets and other commercial targets in pharmaceutical and energy sectors that could benefit the Chinese economy .' [/INST] [APT19:B-HackOrg, seemed:O, to:O, be:O, going:O, after:O, defense:B-Org, sector:I-Org, firms:I-Org, ,:O, Chinese:B-Area, dissident:O, groups:O, and:O, other:O, political:B-Idus, target:O, ,:O, as:O, well:O, as:O, certain:O, financial:B-Idus, targets:O, and:O, other:O, commercial:B-Idus, targets:O, in:O, pharmaceutical:B-Idus, and:O, energy:B-Org, sectors:I-Org, that:O, could:O, benefit:O, the:O, Chinese:B-Area, economy: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: 'FANCY BEAR ( also known as Sofacy or APT 28 ) is a separate Russian-based threat actor , which has been active since mid 2000s , and has been responsible for targeted intrusion campaigns against the Aerospace , Defense , Energy , Government and Media sectors .' [/INST] [FANCY:B-HackOrg, BEAR:I-HackOrg, (:O, also:O, known:O, as:O, Sofacy:B-HackOrg, or:O, APT:B-HackOrg, 28:I-HackOrg, ):O, is:O, a:O, separate:O, Russian-based:B-Area, threat:B-HackOrg, actor:I-HackOrg, ,:O, which:O, has:O, been:O, active:O, since:O, mid:B-Time, 2000s:I-Time, ,:O, and:O, has:O, been:O, responsible:O, for:O, targeted:O, intrusion:B-OffAct, campaigns:I-OffAct, against:O, the:O, Aerospace:B-Idus, ,:O, Defense:B-Idus, ,:O, Energy:B-Idus, ,:O, Government:B-Idus, and:O, Media:B-Org, sectors:I-Org, .: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: 'APT28 espionage activity has primarily targeted entities in the U.S. , Europe , and the countries of the former Soviet Union , including governments , militaries , defense attaches , media entities , and dissidents and figures opposed to the current Russian Government .' [/INST] [APT28:B-OffAct, espionage:I-OffAct, activity:I-OffAct, has:O, primarily:O, targeted:O, entities:O, in:O, the:O, U.S.:B-Area, ,:O, Europe:B-Area, ,:O, and:O, the:O, countries:O, of:O, the:O, former:B-Area, Soviet:I-Area, Union:I-Area, ,:O, including:O, governments:B-Idus, ,:O, militaries:B-Idus, ,:O, defense:B-Idus, attaches:I-Idus, ,:O, media:B-Org, entities:I-Org, ,:O, and:O, dissidents:B-Org, and:O, figures:B-Org, opposed:O, to:O, the:O, current:O, Russian:B-Area, Government: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: 'APT28 espionage activity has primarily targeted entities in the U.S. , Europe , and the countries of the former Soviet Union , including governments and militaries , defense attaches , media entities , and dissidents and figures opposed to the current Russian Government .' [/INST] [APT28:B-OffAct, espionage:I-OffAct, activity:I-OffAct, has:O, primarily:O, targeted:O, entities:O, in:O, the:O, U.S.:B-Area, ,:O, Europe:B-Area, ,:O, and:O, the:O, countries:O, of:O, the:O, former:B-Area, Soviet:I-Area, Union:I-Area, ,:O, including:O, governments:B-Idus, and:O, militaries:B-Idus, ,:O, defense:B-Idus, attaches:I-Idus, ,:O, media:B-Org, entities:I-Org, ,:O, and:O, dissidents:B-Org, and:O, figures:B-Org, opposed:O, to:O, the:O, current:O, Russian:B-Area, Government: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: 'Since at least 2014 , FireEye has observed APT32 targeting foreign corporations with a vested interest in Vietnam 's manufacturing , consumer products , and hospitality sectors .' [/INST] [Since:O, at:O, least:O, 2014:B-Time, ,:O, FireEye:B-SecTeam, has:O, observed:O, APT32:B-HackOrg, targeting:O, foreign:B-Idus, corporations:I-Idus, with:O, a:O, vested:O, interest:O, in:O, Vietnam:B-Area, 's:O, manufacturing:B-Idus, ,:O, consumer:B-Idus, products:I-Idus, ,:O, and:O, hospitality:B-Org, sectors:I-Org, .: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: 'APT33 has targeted organizations – spanning multiple industries – headquartered in the United States , Saudi Arabia and South Korea .' [/INST] [APT33:B-HackOrg, has:O, targeted:O, organizations:O, –:O, spanning:B-Idus, multiple:I-Idus, industries:I-Idus, –:O, headquartered:O, in:O, the:O, United:B-Area, States:I-Area, ,:O, Saudi:B-Area, Arabia:I-Area, and:O, South:B-Area, Korea:I-Area, .: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: 'During the same time period , APT33 also targeted companies in South Korea involved in oil refining and petrochemicals .' [/INST] [During:O, the:O, same:O, time:O, period:O, ,:O, APT33:B-HackOrg, also:O, targeted:O, companies:O, in:O, South:B-Area, Korea:I-Area, involved:O, in:O, oil:B-Idus, refining:I-Idus, and:O, petrochemicals:B-Idus, .: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 generalized targeting of organizations involved in energy and petrochemicals mirrors previously observed targeting by other suspected Iranian threat groups , indicating a common interest in the sectors across Iranian actors .' [/INST] [The:O, generalized:O, targeting:O, of:O, organizations:O, involved:O, in:O, energy:B-Idus, and:O, petrochemicals:B-Idus, mirrors:O, previously:O, observed:O, targeting:O, by:O, other:O, suspected:O, Iranian:B-Area, threat:B-HackOrg, groups:I-HackOrg, ,:O, indicating:O, a:O, common:O, interest:O, in:O, the:O, sectors:O, across:O, Iranian:B-Area, actors:B-HackOrg, .: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: 'APT33 's targeting of organizations involved in aerospace and energy most closely aligns with nation-state interests , implying that the threat actor is most likely government sponsored .' [/INST] [APT33:B-HackOrg, 's:O, targeting:O, of:O, organizations:O, involved:O, in:O, aerospace:B-Idus, and:O, energy:B-Idus, most:O, closely:O, aligns:O, with:O, nation-state:O, interests:O, ,:O, implying:O, that:O, the:O, threat:B-HackOrg, actor:I-HackOrg, is:O, most:O, likely:O, government:O, sponsored: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: 'APT33 's focus on aviation may indicate the group 's desire to gain insight into regional military capabilities to enhance Iran 's aviation capabilities or to support Iran 's military and strategic decision making .' [/INST] [APT33:B-HackOrg, 's:O, focus:O, on:O, aviation:B-Idus, may:O, indicate:O, the:O, group:O, 's:O, desire:O, to:O, gain:O, insight:O, into:O, regional:O, military:B-Idus, capabilities:O, to:O, enhance:O, Iran:B-Area, 's:O, aviation:B-Purp, capabilities:I-Purp, or:O, to:O, support:O, Iran:B-Area, 's:O, military:B-Idus, and:O, strategic:B-Purp, decision:I-Purp, making:I-Purp, .: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: 'Specifically , the targeting of organizations in the aerospace and energy sectors indicates that the APT33 is likely in search of strategic intelligence capable of benefitting a government or military sponsor .' [/INST] [Specifically:O, ,:O, the:O, targeting:O, of:O, organizations:O, in:O, the:O, aerospace:B-Idus, and:O, energy:B-Org, sectors:I-Org, indicates:O, that:O, the:O, APT33:B-HackOrg, is:O, likely:O, in:O, search:O, of:O, strategic:O, intelligence:O, capable:O, of:O, benefitting:O, a:O, government:B-Idus, or:O, military:B-Idus, sponsor: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: 'APT33 's focus on aviation may indicate the group 's desire to gain insight into regional military aviation capabilities to enhance Iran 's aviation capabilities or to support Iran 's military and strategic decision making .' [/INST] [APT33:B-HackOrg, 's:O, focus:O, on:O, aviation:B-Idus, may:O, indicate:O, the:O, group:O, 's:O, desire:O, to:O, gain:O, insight:O, into:O, regional:O, military:B-Purp, aviation:I-Purp, capabilities:I-Purp, to:O, enhance:O, Iran:B-Area, 's:O, aviation:B-Idus, capabilities:O, or:O, to:O, support:O, Iran:B-Area, 's:O, military:B-Idus, and:O, strategic:O, decision:O, making: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 2017 , APT37 expanded its targeting beyond the Korean peninsula to include Japan , Vietnam and the Middle East , and to a wider range of industry verticals , including chemicals , electronics , manufacturing , aerospace , automotive and healthcare entities .' [/INST] [In:O, 2017:B-Time, ,:O, APT37:B-HackOrg, expanded:O, its:O, targeting:O, beyond:O, the:O, Korean:B-Area, peninsula:I-Area, to:O, include:O, Japan:B-Area, ,:O, Vietnam:B-Area, and:O, the:O, Middle:B-Area, East:I-Area, ,:O, and:O, to:O, a:O, wider:O, range:O, of:O, industry:O, verticals:O, ,:O, including:O, chemicals:B-Idus, ,:O, electronics:B-Idus, ,:O, manufacturing:B-Idus, ,:O, aerospace:B-Idus, ,:O, automotive:B-Idus, and:O, healthcare:B-Org, entities:I-Org, .: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 surmise that the targeting of banks , media , and government agencies is conducted in support of APT38 's primary mission .' [/INST] [We:O, surmise:O, that:O, the:O, targeting:O, of:O, banks:B-Idus, ,:O, media:B-Idus, ,:O, and:O, government:B-Org, agencies:I-Org, is:O, conducted:O, in:O, support:O, of:O, APT38:B-HackOrg, 's:O, primary:O, mission: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 APT38 targeted news outlets known for their business and financial sector reporting , probably in support of efforts to identify and compromise additional financial institutions .' [/INST] [The:O, APT38:B-HackOrg, targeted:O, news:B-Idus, outlets:I-Idus, known:O, for:O, their:O, business:O, and:O, financial:B-Org, sector:I-Org, reporting:O, ,:O, probably:O, in:O, support:O, of:O, efforts:O, to:O, identify:O, and:O, compromise:O, additional:O, financial:B-Org, institutions:I-Org, .: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: 'APT39 has prioritized the telecommunications sector , with additional targeting of the travel industry and IT firms that support it and the high-tech industry .' [/INST] [APT39:B-HackOrg, has:O, prioritized:O, the:O, telecommunications:B-Org, sector:I-Org, ,:O, with:O, additional:O, targeting:O, of:O, the:O, travel:B-Idus, industry:I-Idus, and:O, IT:B-Org, firms:I-Org, that:O, support:O, it:O, and:O, the:O, high-tech:B-Idus, industry:I-Idus, .: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: 'APT39 's focus on the telecommunications and travel industries suggests intent to perform monitoring , tracking , or surveillance operations against specific individuals , collect proprietary or customer data for commercial or operational purposes that serve strategic requirements related to national priorities , or create additional accesses and vectors to facilitate future campaigns .' [/INST] [APT39:B-HackOrg, 's:O, focus:O, on:O, the:O, telecommunications:B-Idus, and:I-Idus, travel:I-Idus, industries:I-Idus, suggests:O, intent:O, to:O, perform:O, monitoring:O, ,:O, tracking:O, ,:O, or:O, surveillance:O, operations:O, against:O, specific:B-Org, individuals:I-Org, ,:O, collect:B-Purp, proprietary:I-Purp, or:I-Purp, customer:I-Purp, data:I-Purp, for:O, commercial:B-Purp, or:I-Purp, operational:I-Purp, purposes:I-Purp, that:O, serve:O, strategic:O, requirements:O, related:O, to:O, national:O, priorities:O, ,:O, or:O, create:B-Purp, additional:I-Purp, accesses:I-Purp, and:I-Purp, vectors:I-Purp, to:O, facilitate:O, future:O, campaigns:B-OffAct, .: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: 'REDBALDKNIGHT , also known as BRONZE BUTLER and Tick , is a cyberespionage group known to target Japanese organizations such as government agencies ( including defense ) as well as those in biotechnology , electronics manufacturing , and industrial chemistry .' [/INST] [REDBALDKNIGHT:B-HackOrg, ,:O, also:O, known:O, as:O, BRONZE:B-HackOrg, BUTLER:I-HackOrg, and:O, Tick:B-HackOrg, ,:O, is:O, a:O, cyberespionage:B-HackOrg, group:I-HackOrg, known:O, to:O, target:O, Japanese:B-Area, organizations:O, such:O, as:O, government:B-Org, agencies:I-Org, (:O, including:O, defense:B-Idus, ):O, as:O, well:O, as:O, those:O, in:O, biotechnology:B-Idus, ,:O, electronics:B-Idus, manufacturing:I-Idus, ,:O, and:O, industrial:B-Idus, chemistry:I-Idus, .:O] </s>