Resilient Information Systems Security
Resilient Information Systems Security
We have released the code with a demo or our poisoning attack described in the paper “Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization.” You can access the code in this link.
Ransomware has become one of the most prominent threats in cyber-security and recent attacks has shown the sophistication and impact of this class of malware. In essence, ransomware aims to render the victim’s system unusable by encrypting important files, and then, ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. In our previous work, we developed EldeRan, a machine learning approach to analyse and classify ransomware dynamically. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics […]
Wireless Sensor Networks (WSNs) have become popular for monitoring critical infrastructures, military applications, and Internet of Things (IoT) applications. However, WSNs carry several vulnerabilities in the sensor nodes, the wireless medium, and the environment. In particular, the nodes are vulnerable to tampering on the field, since they are often unattended, physically accessible, and use of tamper-resistant hardware is often too expensive. Malicious data injections consist of manipulations of the measurements-related data, which threaten the WSN’s mission since they enable an attacker to solicit a wrong system’s response, such as concealing the presence of problems, or raising false alarms. Measurements inspection […]
In this paper we propose a modelling formalism, Probabilistic Component Automata (PCA), as a probabilistic extension to Interface Automata to represent the probabilistic behaviour of component-based systems. The aim is to support composition of component-based models for both behaviour and non-functional properties such as reliability. We show how addi- tional primitives for modelling failure scenarios, failure handling and failure propagation, as well as other algebraic operators, can be combined with models of the system architecture to automatically construct a system model by composing models of its subcomponents. The approach is supported by the tool LTSA-PCA, an extension of LTSA, which […]
Software systems are constructed by combining new and existing services and components. Models that represent an aspect of a system should therefore be compositional to facilitate reusability and automated construction from the representation of each part. In this paper we present an extension to the LTSA tool that provides support for the specification, visualisation and analysis of composable probabilistic behaviour of a component-based system using Probabilistic Component Automata (PCA). These also include the ability to specify failure scenarios and failure handling behaviour. Following composition, a PCA that has full probabilistic information can be translated to a DTMC model for reliability […]
Dickens, L. and Lupu, E. On Efficient Meta-Data Collection for Crowdsensing. In Crowdsensing Workshop at PerCom, 2014. (To appear.) Building trustworthy systems that themselves rely on, or integrate, semi-trusted information sources is a challenging aim, but doing so allows us to make good use of floods of information continuously contributed by individuals and small organisations. This paper addresses the problem of quickly and efficiently acquiring high quality meta-data from human contributors, in order to support crowdsensing applications. Crowdsensing (or participatory sensing) applications have been used to sense, measure and map a variety of phenomena, including: individuals’ health, mobility & social […]
Refining policies from high level goals to enforceable specifications in asemi-automated and principled ways remains one of the most significant challenges in policy based systems. We have on two occasions attempted to tackle this challenges in collaboration with Dr Alessandra Russo at Imperial, Dr Arosha Bandara at the Open University and Dr Jorge Lobo at IBM. The first attempt wast done during the Dr Bandara’s PhD thesis.
The Self-Managed Cell is an architectural pattern for building autonomous pervasive systems. It was developed in collaboration with Prof. Joe Sventek at the University of Glasgow, and with my colleagues Dr. Narnaker Dulay and Prof. Morris Sloman at Imperial College.
Ponder2 combines a general-purpose, distributed object management system with a Domain Service, Obligation Policy Interpreter, Command Interpreter and Authorisation Enforcement. The Domain Service provides an hierarchical structure for managing objects. The Obligation Policy Interpreter handles Event, Condition, Action rules (ECA). The Command Interpreter accepts a set of commands, compiled from a high-level language called PonderTalk, via a number of communications interfaces which may perform invocations on a ManagedObjectregistered in the Domain Service. The Authorisation Enforcement caters for both positive and negative authorisation policies, provides the ability to specify fine grained authorisations for every object and implements domain nesting algorithms for conflict resolution.
Secure dissemination of data in crisis management scenarios is always difficult to achieve because network connectivity is intermittent or absent. In this work we have combined data-centric information protection techniques based on usage control, sticky policies and rights management with opportunistic networking to enable the dissemination of information between first responders in crisis management situations. The dissemination of keys for access to the information is controlled by a policy hierarchy that describes the permitted devolution of control. Policies are evaluated whenever two users are in proximity in the field and keys are distributed upon successful evaluation. Simulations with conservative mobility […]