Bridewell announces CHECK penetration testing accreditation from NCSC
B announced it has been accredited by the
ExtraHop open sources ML Dataset to help security teams detect malware and botnet operations faster
E and response , has announced the open-sourcing of its expansive 16-million-row Machine Learning Dataset . The dataset aims to defend against domains generated by algorithms ( DGAs ), strengthening defences against malware and botnet operations .
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Bridewell announces CHECK penetration testing accreditation from NCSC
ridewell , a leading UK cybersecurity firm , has
B announced it has been accredited by the
Government ’ s National Cyber Security Centre ( NCSC ) to provide CHECK penetration testing to government , public sector bodies and organisations under the UK ’ s critical national infrastructure ( CNI ).
CHECK-authorised penetration tests on CNI systems and networks are conducted by verified companies using NCSC-recognised methods and by staff who hold NCSCapproved qualifications . The current NCSC advice is for all HMG organisations to use CHECK-approved penetration testing services . any weaknesses in their systems ,” said James Smith , Head of Offensive Security , Bridewell . “ We ’ re pleased to have achieved this alongside our existing NCSC assurances for Cyber Security Consultancy and Cyber Incident Response .”
The company ’ s ongoing alignment with NCSC accreditations , which also include NCSC Assured Cyber Security Consultancy services in Audit and Review and Risk Management , and its holistic cybersecurity approach is helping to protect the globe ’ s most highly regulated , critical infrastructure organisations . Examples include Gwent Police , Manchester Airport Group and the UK Census 2021 .
With the hangover from The Electoral Commission ’ s cyberattack looming , assessing the security of sensitive government , public sector and CNI systems through rigorous and regular penetration testing is crucial to reassuring the general public and keeping sensitive data safe from malicious hackers .
“ We deemed it essential that we become a CHECK provider to ensure that our existing and new HMG customers receive a high-quality , industry-backed penetration service to identify
ExtraHop open sources ML Dataset to help security teams detect malware and botnet operations faster
xtraHop , a leader in cloud-native network detection
E and response , has announced the open-sourcing of its expansive 16-million-row Machine Learning Dataset . The dataset aims to defend against domains generated by algorithms ( DGAs ), strengthening defences against malware and botnet operations .
Amid a widening cybersecurity skills gap – up 26 % in the last year – and dwindling resources , the cyber landscape is rapidly evolving . Open-sourced research and datasets are becoming solutions to the challenges security teams face daily .
“ Collaboration among the cybersecurity community is invaluable : coming together to share our best work is the only way to remain on the offence and put attackers at a disadvantage ,” said Raja Mukerji , Chief Scientist and Co-founder , ExtraHop . “ Our research will be a game-changer for the community and we encourage other teams to open source their own insights that will similarly benefit the industry at large .”
In an effort to foster industry collaboration , ExtraHop is releasing its DGA detector dataset on GitHub . The dataset , comprising over 16 million rows of data , will assist security teams in identifying malicious activity in their environments before these activities escalate into business problems .
DGAs are used by cyberthreat actors to maintain control within an organisation ’ s environment after gaining access to a network . These tactics make cyberattacks difficult to detect and stop . Originally built for ExtraHop ’ s awardwinning NDR platform , Reveal ( x ), the research can now be utilised by any security researcher . By constructing their own Deep Learning classifier model , they can more quickly identify DGAs and intervene in attacks with greater speed and precision . Since its implementation in Reveal ( x ), ExtraHop ’ s DGA model has demonstrated more than a 98 % accuracy rate .
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