Day 1 - 25 April 2019
Enterprise Security – IOT & Digital Transformation: Chair’s welcome and opening comments
Vulnerability management for enterprises
- Exploitation of known vulnerabilities in software remains the greatest cause of security incidents within the enterprise.
- How can enterprises identify vulnerabilities within their organisations? What tools and methods are available?
- What are key factors to consider during the process, from identification through to prioritisation of fixes?
- Real life examples from across industry.
Senior Representative, OneLogin
10:50AM - Day 1
11:50AM - Day 1
10:20AM - Day 1
12:10PM - Day 2
Keynote: Common cyber security mistakes made by enterprises and how to avoid them
Examining the common mistakes made my enterprises globally, and what business leaders can do to mitigate them. How cyber security can tackle the exponential growth in data from business systems that are incorporating more and more digital technology into their day to day business activities. Covering cloud, big data, AI, IOT and mobile devices and the security sensors meant to protect them from bad agents.
Panel: The importance of collaboration for enterprise cybersecurity
- Understanding the unique challenges of cyber security across industry, and that every party in the supply chain needs to understand these risks from chip vendors, cloud providers and software developers to OEM’s and customers.
- How can companies across the ecosystem collaborate to ensure stronger security?
- What influence can different verticals have here – from securing smart cities to connected vehicles, and what are the different considerations for each?
- Analysing the role and efficacy of standards and regulatory organisations in helping manage enterprise cyber security.
End to End security in IoT
Panel: An E2E approach to tackling data security challenges of the IoT
- Enabling data security in IoT – protecting integrity, authenticity and confidentiality of information
- Addressing the risks of big data – greater volume of sensitive data creating a greater risk of data and identity theft, device manipulation, data falsification, IP theft and server/network manipulation etc.
- What are the different considerations for consumer owned connected device vs. those owned by legal entities?
- Importance of other aspects of IoT security; testing, education of staff and physical security for devices
01:00PM - Day 1
Security Automation & Orchestration for IoT/Enterprise Delivered Through “Intent-Based Networking”
- The “Intent-Based Networking” concept
- Leveraging an open standard-based platform approach that consists of generic compute, NFV, and orchestration concepts
- Automating and orchestrating security by using the platform to create reusable templates that maximize accuracy, efficiency, and repeatability (significantly improving OPEX).
- Using this platform approach to address both Enterprise and IoT based use cases
Training and education for staff -the first line of defence
Threats to infrastructure: cyber security for energy
The energy sector is an area of particular concern for cyber security authorities as the industry continues to embrace the Internet of Things with smart metering and other networked technologies which bring about potential vulnerabilities.
Cyber security for the transport industry
This talk will cover a specific case study from the transport industry around how cyber security affects all parts of the supply chain – from the factory floor, through to connected cars and vehicles themselves. Issues such as addressing customer fears around hacking, upgrading legacy equipment and working with third party providers will be covered.
Director of Industrial Security Solutions
04:00PM - Day 1
Cognitive Security for the Industry 4.0 – Anomaly Detection and Integrity Protection
The Fraunhofer Institute for Secure Information Technology realized a conceptual study to detect attacks and other anomalies by means of Machine Learning, and report those to production control based on monitoring of industrial plants.
- Contrast to conventional Intrusion Detection Systems, this version does not need any type of defined attack patterns
- The continuous evaluation of field bus-, sensor-, production and ERP-data is the basis of the anomaly detection
- Means of autonomous process of machine learning, the system has the capability to recognize the standard range and raises an alarm by leaving it