SPHINX AI Honeypots are realised both as virtual and hardware appliances. The virtual one is based on docker framework making it suitable for both cloud and on premise installations; the hardware one mostly suited for on premise installations, comes in two flavours, the first one based on a low-cost Advanced RISC Machine (ARM) system – Quad-core Cortex-A7 central processing unit (CPU) – that is able to support lightweight detection algorithms whereas the second one is capable of operating as a honeypot and a router system, with the capability of handling computing intensive algorithms without a noticeable delay for the attacker. To achieve this, the system utilises programmable logic in the form of a compact low power field-programmable gate array (FPGA) module. SPHINX AI Honeypots are to be part of an organisation’s cyber defence arsenal aiming to detect manifested cyber-attacks as early as possible by luring the adversaries to attack them instead of the real production IT systems. In this direction, the honeypots host vulnerable services that may be considered targets from an adversary.
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Listing Description
Addressing target audiences and expressing needs
- Collaboration
We would like to test the honeypots on additional use cases and gather the end-user and algorithmic feedback towards enhancing the honeypots performance (in terms of attack analysis and number of emulated vulnerable services) and maximising their user value.
- Public or private funding institutions
- Research and Technology Organisations
R&D, Technology and Innovation aspects
Currently, the SPHINX honeypots are near TRL4 and are not expected to pass TRL5 at the project’s end. We would like funding from Private Investors and/or public or private funding Institutions to continue developing the honeypots towards reaching them at least in TRL7 stage. After that and based on the TRL7 results a second round of funding may be needed for going to TRL9.
The business model does not rely on heavy investments as the HP solution builds on open-source software and is offered either in pure software or in low cost, low power embedded systems. Furthermore, the supported automation (exploiting for example docker and Kubernetes frameworks), especially in the solution’s deployment and management keeps low the relevant costs. The virtualised nature of the HP allows also for the replicability of the business model in different vertical and also horizontal domains.
Assuming that the same configuration applies (e.g. same ML model is deployed) and the same (or statistically similar) input datasets are used, the Honeypots can be deployed from different stakeholders in different environmetns and the observed results would be consistent.
The business model is sustainable as the impact of the specific solution in the environment is pretty low and is related mainly to the resources consumed from the Cloud platforms or/and the embedded systems hosting the HP solution; for the former, green friendly cloud providers are to be preferred for the solution’s hosting whereas for the latter the used embedded systems have a low-energy footprint. From the societal perspective the offering of the solution in both HW and containerised forms enables for deployment in the customers’ premises meeting as such any privacy needs. Finally in the economy side, the model is sustainable as the solution fulfils an important current and future need of the business sector in managing and addressing the risks and costs from cyber-attacks.
Offer Details
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Value Proposition:
Our unique value consists of providing a solution that can be both accommodated in cloud and on-premise (addressing as such any Cloud-derived security/privacy concerns) deployments providing a consistent experience form the users; further to that the utilisation of acceleration from the hardware versions enables for the edge-based cyber-attack detection and analysis by providing results on the spot; finally the, offer of an open API and the adoption of standards (e.g. STIX) for sharing the cyber-attacks information promotes the solution’s inter-operability by a) facilitating its integration in already deployed cyber-attack detection warning ecosystems and b) by enabling it to exploit third-party AI algorithms towards extending the detection capabilities range. In this context, the honeypots will contribute to detecting faster the launched cyber-attacks, thus enabling the business to take faster the proper actions and as such minimise the negative impact on the business in terms of affected assets (e.g. disrupted IT systems and services), lost profits from the services’ disruption, lost reputation, and lost work hours.
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LIMASSOL
Cyprus
3032
Cyprus
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