- Zhao, X., Salako, K., Strigini, L., Robu, V. and Flynn, D. (2020). Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles. Information and Software Technology pp. 1–1. doi:10.1016/j.infsof.2020.106393.
- Littlewood, B., Salako, K., Strigini, L. and Zhao, X. (2020). On reliability assessment when a software-based system is replaced by a thought-to-be-better one. Reliability Engineering & System Safety, 197, pp. 106752–106752. doi:10.1016/j.ress.2019.106752.
- Bloomfield, R.E., Popov, P., Salako, K., Stankovic, V. and Wright, D. (2017). Preliminary interdependency analysis: An approach to support critical-infrastructure risk-assessment. Reliability Engineering & System Safety, 167, pp. 198–217. doi:10.1016/j.ress.2017.05.030.
- Netkachov, O., Popov, P. and Salako, K. (2016). Model-Based Evaluation of the Resilience of Critical Infrastructures Under Cyber Attacks. pp. 231–243. doi:10.1007/978-3-319-31664-2_24.
- Netkachov, O., Popov, P. and Salako, K. (2014). Quantification of the Impact of Cyber Attack in Critical Infrastructures. pp. 316–327. doi:10.1007/978-3-319-10557-4_35.
- Salako, K. and Strigini, L. (2013). When does "Diversity" in Development Reduce Common Failures? Insights from Probabilistic Modelling. IEEE Transactions on Dependable and Secure Computing, 99(preprints). doi:10.1109/TDSC.2013.32.
London EC1V 0HB
Dr Kizito Salako is an applied mathematician and software developer at the Centre for Software Reliability (CSR), City University London. He holds a first-class double honours degree in Mathematics and Statistics from the University of Lagos; a Master of Advanced Study in Mathematics degree from the University of Cambridge (where he was both a Shell Centenary Scholar and a Commonwealth Scholar); and a PhD in Computer Science from City, University of London.
Kizito is passionate about applications of probability theory, Bayesian statistics, geometry and machine-learning techniques, when trying to simulate, assess and forecast the (failure) behaviour of software-based systems. His doctoral thesis clarifies and significantly extends the applicability of a family of probabilistic models used to describe the failure behaviour of multi-version software. He also developed a novel geometric approach to extremising the expected system reliability of a class of fault-tolerant software-based systems; so-called 1-out-of-N systems. Currently, he is particularly interested in the assessment/forecasting challenges that arise when these systems rely on evolving machine-learning solutions.
Since joining CSR, Kizito has contributed to several projects (e.g. DISPO, DIRC, IRRIIS, PIA-FARA, AFTER, DIDERO-PC, DISIEM) both as a mathematician and as a software developer.
He is experienced in building simulations of complex systems using C++, and was instrumental in developing the PIA-FARA simulation engine -- creating modular software that implements and analyses the probabilistic, functional and process relationships that may exist between the components of large-scale interdependent complex critical infrastructure.
Quantitative assessment of the dependability of software-based systems (in particular, systems that are complex, safety/security critical, and that may rely on evolving machine learning solutions):
The development, validation and application of advanced statistical approaches for dependability assessment;
Conservative Bayesian assessment methods, that take into account various forms of dependability evidence when assessing system dependability;
Monte-Carlo methods for simulating complex systems (where these systems are modelled as Generalised Semi-Markov and Markov Regenerative Processes);
The combined use of probabilistic conditional independence relations and physics models in modelling interdependencies between complex systems
The efficacy (in terms of improved reliability) of using diverse-redundant software in fault-tolerant configurations
Statistical forecasting/analyses of system risk, dependability and cyber-security issues:
Investigating the impact of software development strategies on system reliability;
Studying the efficacy of mitigation strategies for a system under cyber-attack;
Studying the efficacy of diverse redundant system architectures in mitigating cyber-attacks, and the implications of such strategies for the likelihood of confidentiality and integrity breaches;
Kizito has collaborated on several national and international research projects. Details of these projects can be found here. The projects include:
DISIEM (funded by the EU horizon-2020 program) Diversity Enhancements for Security Information and Events Management Systems, 2016–2019
CEDRICS (funded by the UK’s EPSRC) Communicating and Evaluating Cyber–Risks and Dependencies, 2014–2017
AFTER (EU funded FP7 project) A Framework for sysTems vulnerability identification, dEfence and Restoration, 2011–2014
DIDERO-PC (funded by the UK’s EPSRC) DIverse DatabasE ReplicatiOn Performance Comparison, 2013–2014
PIA-FARA (funded by the UK’s Technology Strategy Board) Probabilistic Interdependency Analysis: Framework/data-Analysis/Risk–Assessment, 2009–2010
CETIFS (funded by the UK’s Technology Strategy Board) A Critical Infrastructure Interdependency Modelling Feasibility Study, 2008
IRRIIS (EU funded FP6 Project) Integrated Risk–Reduction of Information-based Infrastructure Systems, 2006–2009
DIRC (funded by the UK’s EPSRC) Dependability Integrated Research Collaboration, 2000–2006
Probability Theory and Mathematical Statistics:
measure theoretic probability – probability spaces, integration, conditional expectation, characteristic functions, Radon-Nikodym derivatives and change-of-measure;
stochastic models and processes – filtered probability spaces; stationary/non-stationary; time-series analyses; point processes; order-statistics models; Gaussian processes; Markov processes (Markov chains, Markov renewal processes, Markov reward processes, Markov decision processes, hidden Markov models); hybrid stochastic/deterministic models;
stochastic analysis – convergence of random variables, stochastic calculus/stochastic differential equations (Ito-calculus, jump-diffusion processes);
statistical (Bayesian) inference and modelling – parametric and non-parameteric; estimation theory; posterior predictive distributions; prequential/statistical forecasting systems; asymptotic theory (Slutsky’s theorem, delta-method, portmanteau theorem, weak/strong laws); hypothesis testing; goodness-of-fit analyses; ROC techniques;
Machine Learning Approaches and Considerations: supervised learning (Regression, GLMs, SVMs, ANNs, CNNs, LSTMs); deep reinforcement learning (approximate Q-learning, temporal difference methods)
Mathematical Finance: mean-variance portfolio theory; CAPM, option-pricing, Wiener processes/Brownian motion, Martingales, risk-neutral measures, the Black-Scholes model;
Mathematical Analysis and Vector-Space Theory: real and complex analysis; metric spaces; infinite-dimensional vector-spaces – functional analysis (Banach/Hilbert spaces); finite-dimensional vector-spaces – linear/multilinear algebra, differential geometry/calculus on manifolds, tensor calculus; applications of fixed-point theorems; non-linear dynamical systems theory;
Mathematical/Statistical Optimisation: linear programming, convex optimisation, stochastic gradient methods, dynamic programming, simulated annealing, steepest descent;
Publications by category
- (2019). Resilience of Cyber-Physical Systems. In Springer International Publishing. ISBN 978-3-319-95596-4.
- Netkachov, O., Popov, P. and Salako, K. (2019). Quantitative Evaluation of the Efficacy of Defence-in-Depth in Critical Infrastructures. Resilience of Cyber-Physical Systems (pp. 89–121). Springer International Publishing. ISBN 978-3-319-95596-4.
Conference papers and proceedings (9)
- Aghazadeh Chakherlou, R., Salako, K. and Strigini, L. (2022). Arguing safety of an improved autonomous vehicle from safe operation before the change: new results. RAIS 2022 2nd International Workshop on Reliability of Autonomous Intelligent Systems 31 Oct 2022 – 3 Nov 2022, Charlotte, NC, USA.
- Salako, K., Strigini, L. and Zhao, X. (2021). Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence. 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 21-24 June.
- Salako, K. (2020). Loss-Size and Reliability Trade-Offs Amongst Diverse Redundant Binary Classifiers.
- Zhao, X., Robu, V., Flynn, D., Salako, K. and Strigini, L. (2019). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) 28-31 October.
- Popov, P., Salako, K.O. and Stankovic, V. (2015). Stochastic modeling for performance evaluation of database replication protocols. 12th International Conference on Quantitative Evaluation of Systems 1-3 September, Madrid, Spain.
- Jones, K. and Salako, K. (2013). Modeling Security Policy and the Effect for End-Users. HCI International 2013: 15th International Conference on Human-Computer Interaction 21-26 July, Las Vegas, Nevada, US.
- Bloomfield, R.E., Chozos, N. and Salako, K. (2009). Current Capabilities, Requirements and a Proposed Strategy for Interdependency Analysis in the UK.
- Bloomfield, R.E., Buzna, L., Popov, P.T., Salako, K. and Wright, D. (2009). Stochastic Modelling of the Effects of Interdependencies between Critical Infrastructure.
- Salako, K. (2007). Bounds on the Reliability of Fault-Tolerant Software Built by Forcing Diversity.
- Salako, K. Home Page.
Journal articles (6)
- Salako, K., Strigini, L. and Zhao, X. (2021). Proofs of Conservative Confidence Bounds on PFD, Using Claims of Improved Reliability..
- Salako, K., Stankovic, V. and Popov, P. (2015). Stochastic model for performance evaluation of database replication protocols..
- Popov, P., Salako, K. and Stankovic, V. (2015). Stochastic Modeling for Performance Evaluation of Database Replication Protocols. Springer International Publishing.
- Netkachov, O., Popov, P. and Salako, K. Simulation model of the extended Nordic32 network..