My colleague Lorenzo has a nice post doc position in malware detection. Also, the closing date for the Multilinear Maps postdoc here at Royal Holloway is tomorrow.
The recently-established Systems Security Research Lab (S2Lab), led by Dr Lorenzo Cavallaro within the Information Security Group (ISG) at Royal Holloway, University of London, is seeking to appoint 1 Post-Doctoral Research Assistant (PDRA) to work on the EPSRC-funded project “Mining the Network Behaviour of Bots”, part of the CEReS call (http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K033344/1).
The project aims at building on machine learning techniques to characterize the core network behaviors of malware, with a particular emphasis on bot-like threats. Most of the machine learning-based approaches applied to this context so far report high performance metrics although marginal, if any, effort has been put on providing quality metrics to assess the actual machine learning tasks and provide supporting evidence on its actual strengths (or weaknesses) once deployed in the wild. Within the project, we have been working towards addressing such shortcomings and are building detection models to classify and detect bot-like behaviors with (statistical) confidence, by analysing different network data sources, such as passive DNS traffic (to characterize DGA-based botnets), and malicious/benign network traces.
The PI research expertise is in systems security and malware analysis and detection; in addition, 4 co-investigators and 1 PDRA with expertise in machine learning, bioinformatics, and network analysis, make up the whole team.
The ideal candidate must have earned (or close to defend) a PhD in Computer Science or related discipline, with a particular emphasis on Computer Security. In addition, the candidate must have a strong research track record and a proven ability to find innovative solutions. The co-investigators and the machine learning PDRA are focused on devising novel machine learning technique, while the ideal candidate for this post must further be self-motivated, possess development skills, and be experienced in systems security and malware analysis / detection. Having explored machine learning in particular to tackle security aspects is highly desirable.
The main responsibilities of the post are:
- Developing novel analysis to mine and model network and host behaviors
- Developing novel techniques to detect malicious network behavior
- Developing research objectives and publishing research
- Planning own day to day research activity
- Attending project meetings, discussing research with collaborative partners, especially within the Systems Security Research Lab
- Limited supervision by the PI
- Attending conferences and presenting reearch papers
- Providing input to project web sites and other dissemination and engagement forums;
The Systems Security Research Lab is currently exploring a number of research projects, including Android security and techniques to automatically generate exploit for memory corruption vulnerabilities. Such projects inherently build on machine learning (and program analysis) and further collaboration between this project and S2Lab research activities at large is, of course, encouraged.
This is a full time post, available from Dec 14, 2015 or shortly thereafter, for a fixed term period of 12 months. This post is based in Egham, Surrey, where Royal Holloway, University of London is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from central London.
Royal Holloway University of London is an Academic Centre of Excellence in Cyber Security Research and Education only one of the two Higher Education institutions awarded with a Centre for Doctoral Training in Cyber Security.
For an informal discussion about the post, please contact the PI, Dr Lorenzo Cavallaro, at firstname.lastname@example.org or +44 (0)1784 414381.
Please apply online at https://goo.gl/RjYcZ2 — applications must include (i) a CV, (ii) a cover letter outlining how you fit into the project, and (iii) a personal research statement. Applications with missing documentation may not be fully considered.
To view further details of this post and to apply please visit https://jobs.royalholloway.ac.uk/. The RHUL Recruitment Team can be contacted with queries by email at: email@example.com or via telephone on: +44 (0)1784 41 4241.
Please quote the reference: 1115-336
Closing Date: Midnight (GMT), 30 November 2015
Interview Date: 7 December 2015