The RoleBlack Lotus Labs has an opening for a Senior Lead Security Engineer that will leverage Lumen's unique visibility to hunt Advanced Persistent Threat actors (APTs) and scale discovery of evolving malicious threats.
Our global visibility of one of the worlds largest and most interconnected IP backbones as well as our computing cluster present exciting opportunities to integrate machine learning and graph analytic techniques as we find new ways to hunt for threats across the internet.
Black Lotus Labs has detected and disrupted key evolving threats at an internet scale for years.This position will work alongside advanced security researchers, data engineers, and malware reverse engineers, and help mentor analysts, engineers, and data scientists to tackle evolving threats accelerated by technologies like our Hadoop ecosystem (HBase, HDFS, Spark, Kafka, AirFlow), Elasticsearch and Redis clusters, Docker using Docker Swarm, malware environment, and a network of honeypots.This is a close-knit, experienced, amazingly smart team that you can be a part of and help build out.
This is a remote/work-from-home opening as well – and we are looking to expand the team globally to expand our ability to protect the world's most important networks.
The Main ResponsibilitiesResearch latest threat attacker tools, techniques and procedures (TTPs) with a goal of automating detection.Analyze attacks and use network, forensic and OSINT methods for investigation.Contribute to the development of tactical solutions to support triage and deep-dive analysis of malicious artifacts surfaced by internal and external partners.Conduct network analysis, forensic investigations and malware analysis to identify malicious activity and derive Indicators of Compromise (IOCs) and associated detection rules.Work with team to scale analysis of evolving threats and tracking threat actors leveraging support from data science tools sets developed by data scientists at the Lab such as machine learning and graph analytics.Set priority of what threats to analyze and how long to spend on them to maximize the team's impact.Build and maintain trust relationships with other intelligence teams, law enforcement, and other outside groups.Work as the team point-of-contact in a rotational cycle to triage incoming research-related events.Contribute to the creation and dissemination of finished cyber threat intelligence products and briefings.Overlap at least 10 hours per week with US working hours.
What We Look For in a CandidateDesired candidates will have a strong background exhibiting:In-depth technical knowledge of adversary capabilities, infrastructure, and techniques that can be applied to define, develop, and implement the techniques to discover and track the adversaries of today and identify the attacks of tomorrow.Experience using OSINT methods for investigation, including discovering novel threats in malware repositories.Scripting experience with Python and familiarity with distributed computing.Extensive experience hunting threat actors, and developing algorithms and techniques to identify new threats from large data sets.Deep knowledge of network-based threats and identifying behaviors without attack payloads.Strong analytical thinking and ability to quickly pick up new methods, tools and programming languages.User-level experience in a Unix-based environment.Familiarity with extracting data through SQL.Strong writing skills to assist in sharing our knowledge with the public.Demonstrable knowledge of several of the following areas: cybersecurity concepts, network protocols, firewalls, IDS/IPS systems, email security, endpoint security, network security, Windows/Linux/macOS systems, cyber threat hunting, malware analysis tools and techniques, cyber threat intelligence, common threat actor TTPs, application security concepts, cloud security fundamentals, Incident Response methodologies.
Well experienced candidates may also have the following skills:Experience with Spark and distributed computing teams, law enforcement, and other outside groupsExperience developing automation and analysis in Python-based environments.Understanding of static or dynamic analysis of malware.Ability to analyze large data sets and present conclusions drawn from them.Ability to work with others in providing direction and assisting in learning new topics.
Preferred:Functional knowledge of machine learning and how it can be applied to data sets.Public speaking experience and a willingness to share technical topics in public forum.
Compensation
