Pragmatic Network Security: Avoiding Real-World Vulnerabilities

Peter Wood and his team analysed the results from a series of network penetration tests over the past two years, in a variety of sectors including banking, insurance and retail. They identified the most common vulnerabilities, how they can be exploited and the consequences for each business. This presentation demonstrates in detail how criminals can take advantage of these weaknesses and how you can secure your networks using straightforward techniques
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Spotlight

Personalized Web Search (PWS) is very effective in improving the quality of search services on the internet. The information on internet has increased day-by-day and user demand for the accurate result, for the accurate result the user has option to PWS. PWS works on the basis of information that user provide to search provider, the current result based on that information. This paper model makes use of hierarchical user profiles, it simultaneously maintaining privacy protection required by the user. Greedy DP (Discriminating Power) & Greedy IL (Information Loss) are used for runtime generalization and it have online prediction that query requires personalization or not.


OTHER ON-DEMAND WEBINARS

Risk Mitigation Services in Cyber Insurance Underwriting

"Cyber insurance is becoming an increasingly competitive market. In order to differentiate their offerings, underwriters are beginning to offer unique risk mitigation services to their insureds. But with all the noise in this space, how do risk managers find and choose the policy that is best for them? In this webinar hosted by Advisen, Tracie Grella, Global Head of Professional Liability at the world's largest insurer, AIG, Neeraj Sahni, Vice President, FINEX North America—Cyber and Technology Risks at Willis, and Ira Scharf, General Manager of Cyber Insurance at BitSight Technologies, to learn how underwriters, brokers and technology firms are working together to bring risk mitigation services to their clients"

"Data Level Security for the Public Sector"

informatica

"Join us on October 28th for a webinar where you’ll learn: •How Informatica’s data security solutions control the access and viewing of sensitive information to prevent unauthorized disclosure •How preventative security can be rapidly deployed without the need to write code •How policy driven security and full integration with authentication software can be deployed across the application and data portfolio •How Informatica’s solutions address the critical shortfall of virtually all security infrastructures, the inside threat •Why Informatica is a leader in Gartner’s Magic Quadrant for data masking technology "

Teaming Together to Prevent Attacks and Protect Your Data

Splunk

Targeted attacks — including advanced persistent threats (APTs) — and today’s sophisticated malware threats are one of the biggest challenges facing customer’s as the threats multiply and create unique compromises within their networks.

Mobile Security is More than Just Mobile Device Management (MDM)

In an increasingly mobile workforce, mobile security challenges continue to bubble up to the surface. New devices, OSs and attack vectors can keep a team of security professionals busy. This 3-part TechBytes series takes an in-depth look at the evolution of enterprise mobility and the how securing devices, users and applications has rapidly evolved over the past few years. Viewers will leave with actionable insights as to how to ease the mobile security burden for their enterprise. Part 1, Mobile Security is More than Just Mobile Device Management (MDM), Fiberlink expert Jimmy Tsang discusses the evolution of devices, applications and use cases that have led to the evolution of Mobile Device Management (MDM) into Enterprise Mobility Management (EMM) which takes a holistic view of device, user, application and back-end infrastructure, enabling end to end security in ways not previously available

Spotlight

Personalized Web Search (PWS) is very effective in improving the quality of search services on the internet. The information on internet has increased day-by-day and user demand for the accurate result, for the accurate result the user has option to PWS. PWS works on the basis of information that user provide to search provider, the current result based on that information. This paper model makes use of hierarchical user profiles, it simultaneously maintaining privacy protection required by the user. Greedy DP (Discriminating Power) & Greedy IL (Information Loss) are used for runtime generalization and it have online prediction that query requires personalization or not.

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