Advantages of Fog computing
• Bringing data close to the user. Instead of housing information at data center sites far from the end-point, the Fog aims to place the data close to the end-user.
• Creating dense geographical distribution. First of all, big data and analytics can be done faster with better results. Second, administrators are able to support location-based mobility demands and not have to traverse the entire network. Third, these edge (Fog) systems would be created in such a way that real-time data analytics become a reality on a truly massive scale.
• True support for mobility and the IoT. By controlling data at various edge points, Fog computing integrates core cloud services with those of a truly distributed data center platform. As more services are created to benefit the end-user, edge and Fog networks will become more prevalent.
• Numerous verticals are ready to adopt. Many organizations are already adopting the concept of the Fog. Many different types of services aim to deliver rich content to the end-user. This spans IT shops, vendors, and entertainment companies as well
Decoy data, such as decoy documents, honey pots and other bogus information can be generated on demand and used for detecting unauthorized access to information and to poison the thief’s ex-filtrated information. Serving decoys will confuse an attacker into believing they have ex-filtrated useful information, when they have not. This technology may be integrated with user behavior profiling technology to secure a user’s data in the Cloud. .
Whenever abnormal and unauthorized access to a cloud service is noticed, decoy information may be returned by the Cloud and delivered in such a way that it appear completely normal and legitimate. The legitimate user, who is the owner of the information, would readily identify when decoy information is being returned by the Cloud, and hence could alter the Cloud’s responses through a variety of means, such as challenge questions, to inform the Cloud security system that it has incorrectly detected an unauthorized access. In the case where the access is correctly identified as an unauthorized access, the Cloud security system would deliver unbounded amounts of bogus information to the attacker, thus securing the user’s true data from can be implemented by given two additional security features:
1. Validating whether data access is authorized when abnormal information access is detected
2. Confusing the attacker with bogus information that is by providing decoy documents.
We have applied above concepts to detect unauthorized data access to data stored on a local file system by masqueraders, i.e. attackers who view of legitimate users after stealing their credentials. Our experimental results in a local file system setting show that combining both techniques can yield better detection results .This results suggest that this approach may work in a Cloud environment, to make cloud system more transparent to the user as a local file system.
Cloud computing is a delivery platform which promises a new way of accessing and storing personal as well as business information. Cloud computing refers to the practice of transitioning computer services such as computation or data storage to multiple redundant offsite locations available on the Internet, which allows application software to be operated using internet-enabled devices.
In Existing data protection mechanisms such as encryption was failed in securing the data from the attacker. It does not verify whether the user was authorized or not.
Cloud computing security does not focus on ways of secure the data from unauthorized access.
In 2009 we have our own confidential documents in the cloud. This file does not have much security. so, hacker gains access the documents. Twitter incident is one example of a data theft attack in the Cloud.
• Proximity to end-users, its
• Dense geographical distribution
• Support for mobility.
Fog reduces service latency, and improves QoS (Quality of Service), resulting in superior user-experience. Fog Computing supports emerging Internet of Everything (IoE) applications that demand real-time/predictable latency (industrial automation, transportation, networks of sensors and actuators). Fog paradigm is well positioned for real time Big Data and real time analytics, it supports densely distributed data collection points, hence adding a fourth axis to the often mentioned Big Data dimensions (volume, variety, and velocity).
Unlike traditional data centers, Fog devices are geographically distributed over heterogeneous platforms, spanning multiple management domains. That means data can be processed locally in smart devices rather than being sent to the cloud for processing.
Fog computing is a model in which data, processing and applications are concentrated in devices at the network edge rather than existing almost entirely in the cloud.Fog Computing is a paradigm that extends Cloud Computing and services to the edge of the network, similar to Cloud, Fog provides data, compute, storage, and application services to end-users.
Fog computing is a paradigm which extends cloud computing paradigm to the edge of the network. Terms Edge Computing and Fog Computing are often used interchangeably. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. This enables new breed of applications and services.