Technical seminar report on Fog computing, Lecture notes of Computer science

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TECHNICAL SEMINAR REPORT

ON

FOGCOMPUTING

ABSTRACT

Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. For securing user data from such attacks a new paradigm called fog computing can be used. 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. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network .This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.

• 1 Introduction……………………………………………………………………
  • 1 1 Introduction to fog computing
  • 1 2 Cloud Computing
  • 1 3 Disadvantages.................................................................................................
  • 1 4 Concept of fog computing
  • 1 5 Characteristics of the fog
  • 1 6 Existing System
  • 1 7 Proposed System............................................................................................
• 2 Literature Survey ……………………………………………………………...
• 3 Methodology…………………………………………………………………...
• 4 Securing Clouds With Fog ……………………………………………………
  • 4 1 Implementing Security Features …………………………………………….
• 5 Applications..………………………………………………………………….
  • Conclusion…………………………………………………………………….
  • References…………………………………………………………………….
• Fig1.1 Fog between edge and cloud…………………………………...……… LIST OF FIGURES
• Fig1.2 Represents the edge network in Fog computing……………………….
• Fig4.1 Decoy System………………………………………………………...
• Fig4.2 Architecture of Fog Computing………………………………………
• Fig5.1 Fog computing in smart grid………………………………………….
• Fig5.2 Fog computing in smart traffic lights and connected vehicles..............

Department of Computer Science & Engineering Page 1

Chapter 1 INTRODUCTION

1.1 Introduction to fog computing

In this era, Cloud computing is achieving popularity every day. The ease of use and storage which is provided to users for personal and business purposes is increasing its demand. Although, cloud computing provides an environment through which managing and accessing of data becomes easier but it have consequences such as data leakage, data theft, insider attacks etc. Very common risks now days are data theft attacks. The Twitter incident is one example of a data theft attack from the Cloud. Several Twitter corporate and personal documents were ex-filtrated to technological website Tech Crunch and customers’ accounts, including the account of U.S. President Barack Obama, were illegally accessed. The attacker used a Twitter administrator’s password to gain access to Twitter’s corporate documents, hosted on Google’s infrastructure as Google Docs. The damage was significant both for Twitter and for its customers. Van Dijk and Juels have shown that fully homomorphic encryption, often acclaimed as the solution to such threats, is not a sufficient data protection mechanism when used alone. To resolve these issues a mechanism which can detect such malicious activities is required. For this, Fog computing is paradigm which monitors the data and helps in detecting an unauthorized access.

1.2 Cloud Computing

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.

Department of Computer Science & Engineering Page 3 In this framework, each smart thing is attached to one of Fog devices. Fog devices could be interconnected and each of them is linked to the Cloud. As Fog computing is implemented at the edge of the network, it provides low latency, location awareness, and improves quality- of-services (QoS) for streaming and real time applications. Typical examples include industrial automation, transportation and networks of sensors and actuators. The Fog parad igm is well positioned for real time big data analytics, supports densely distributed data collection points, and provides advantages in entertainment, advertising, personal computing and other applications.

1.5 Characteristics of the fog are

a) Low latency and location awareness. b) Wide-spread geographical distribution. c) Mobility. d) Very large number of nodes. e) Predominant role of wireless access. f) Strong presence of streaming and real time application. g) Heterogeneity. Fig1.2 Represents the edge network in Fog computing

Department of Computer Science & Engineering Page 4 The main Feature of Fog Computing is its ability to support applications that require low latency, location awareness and mobility. This ability made possible by fact that fog computing systems are developed closer to the End users in a widely disturbed manner.

1.6 Existing System

Existing data protection mechanisms such as encryption was failed in securing the data from the attackers. 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. Encryption does not provide much security to our data. In 2009 we have our own confidential documents in the cloud. This files does not have much security. So, hacker gains access the documents. Twitter incident is one example of a data theft attack in the Cloud. Difficult to find the attacker. In 2010 and 2011 Cloud computing security was developed against attackers. Finding of hackers in the cloud. Additionally, it shows that recent research results that might be useful to protect data in the cloud.

1.7 Proposed System

We proposed a completely new technique to secure user’s data in cloud using user behavior and decoy information technology called as Fog Computing. We use this techniques to provide data security in the cloud. A different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. In this technique when the unauthorized person try to access the data of the real user the system generates the fake documents in such a way that the unauthorized person was also not able to identify that the data is fake or real .It is identified thought a question which is entered by the real user at the time of filling the sign up form. If the answer of the question is wrong it means the user is not the real user and the system provide the fake document else original documents will be provided by the system to the real user.

Department of Computer Science & Engineering Page 6 real life projects. Fog computing is primarily performed for the requirement of the geographical distribution of resources rather than having a centralized one. A multi-tier architecture is adopted in Fog computing platforms. In first tire there is machine to machine communication and the higher tiers handle visualization and reporting. The higher tier is shown by the Cloud. They said that making Fog computing projects are challenging [5] but there are algorithms and techniques exist that handle reliability and assure fault tolerance. With their support such real life projects are possible. Claycomb, W. R. (2012) [8] has featured a hierarchy of administrators within cloud service suppliers and also provide examples of attacks from real insider attack ca ses. They talked about how cloud architecture let intruders to breach the security. They have also shown two extra cloud related insider risks: the insider who exploits a cloud - related susceptibility to steal information from a cloud system, and the insider who utilizes cloud systems to carry out an attack on user’s local resource. They specified the key challenges faced by cloud suppliers and clients for protected their highly confidential data. Park, Y. Et al. (2012) [9] formulated a method that was a software decoy for protecting cloud data utilizing software. They introduced a software-based decoy system that purposes to deceive insiders, to determine the ex-filtration of proprietary source code. The system makes a Java code which seems as valuable information to the intruder. Further static obfuscation method is utilized to create and transform original software. Bogus programs are combined by software that is automatically transformed from actual source code, but designed to be dissimilar to the original[9].This deception method confuses the insider and also obfuscation supports the secure data by hiding it and making bogus information for insider. Beacons are also inserted into the bogus software to determine the ex-filtration and to build an alert if the decoy software is touched, compiled or executed. Ivan Stojmenovic & Sheng Wen (2014)[10], showed 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 and elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. Investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.

Department of Computer Science & Engineering Page 7

Chapter 3 METHEDOLOGY

In Fog computing, services can be hosted at end devices such as set-top-boxes or access points. The infrastructure of this new distributed computing allows applications to run as close as possible to sensed actionable and massive data, coming out of people, processes and things. Such Fog computing concept, actually a Cloud computing close to the ground, creates automated response that drives the value. Both Cloud and Fog provide data, computation, storage and application services to end-users. However, Fog can be distinguished from Cloud by its proximity to end-users, the dense geographical distribution and its support for mobility. We adopt a simple three level hierarchy as in Figure 1.2 In this framework, each smart thing is attached to one or more of Fog nodes. Fog devices could be interconnected and each of them is linked to the Cloud. While Fog and Cloud use the same resources (networking, compute, and storage), and share many of the same mechanisms and attributes (virtualization, multi-tenancy) the extension is a non-trivial one in that there exist some fundamental differences. The Fog vision was conceived to address applications and services that do not fit well the paradigm of the Cloud. They include:

• Applications that require very low and predictable latency— the Cloud frees the user from many implementation details, including the precise knowledge of where the computation or storage takes place. This freedom from choice, welcome in many circumstances becomes a liability when latency is at premium (gaming, video conferencing).
• Geo-distributed applications (pipeline monitoring, sensor networks to monitor the environment).
• Fast mobile applications (smart connected vehicle, connected rail).
• Large-scale distributed control systems (smart grid, connected rail, smart traffic light systems).

Chapter 4 SECURING CLOUDS WITH FOG

Department of Computer Science & Engineering Page 9 “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, and
2. Confusing the attacker with bogus information that is by providing decoy documents. The decoy documents carry a keyed-Hash Message Authentication Code (HMAC), which is hidden in the header section of the document. The HMAC is computed over the file’s contents using a key unique to each user. When a decoy document is loaded into memory, we verify whether the document is a decoy document by computing a HMAC based on all the contents of that document. We compare it with HMAC embedded within the document. If the two HMACs match, the document is deemed a decoy and an alert is issued.

Department of Computer Science & Engineering, JCE, Belagavi Page 10 Fig4.1 Decoy System

3. Combining the Two Techniques: The correlation of search behavior anomaly detection with trap-based decoy files should provide stronger evidence of malfeasance, and therefore improve a detector’s accuracy. We hypothesize that detecting abnormal search operations performed prior to an unsuspecting user opening a decoy file will corroborate the suspicion that the user is indeed impersonating another victim user. This scenario covers the threat model of illegitimate access to Cloud data. Furthermore, an accidental opening of a decoy file by a legitimate user might be recognized as an accident if the search behavior is not deemed abnormal. In other words, detecting abnormal search and decoy traps together may make a very effective masquerade detection system. Combining the two techniques improves detection accuracy.

Department of Computer Science & Engineering, JCE, Belagavi Page 12 provide the detail knowledge of attack done on their personal file/document with details like date, time, number of times the attacker trying to hack that file/document .Best thing of fog Computing is after each successful login the user get SMS on the mobile that “login successful”. From this the user get alert when other else trying to gain access to his/her personal fog account and when attacker trying to download some files/documents then user also get SMS that contain attacker ip-address, attacker’s server name, date, time details on his/her mobile so that become easy to catch attacker by tracing all these things.

Chapter 5 APPLICATIONS

Department of Computer Science & Engineering, JCE, Belagavi Page 13 Connected car: Autonomous vehicle is the new trend taking place on the road. Tesla is working on software to add automatic steering, enabling literal "hands free" operations of the vehicle. Starting out with testing and releasing self - parking features that don't require a person behind the wheel. Within 2017 all new cars on the road will have the capability to connect to cars nearby and internet. Fog computing will be the best option for all internet connected vehicles why because fog computing gives real time interaction. Cars, access point and traffic lights will be able to interact with each other and so it makes safe for all. At some point in time, the connected car will start saving lives by reducing automobile accidents. Smart Grids: Smart grid is another application where fog computing is been used. Based on demand for energy, its obtainability and low cost, these smart devices can switch to other energies like solar and winds. As shown in Figure 5.1,The edge process the data collected by fog collectors and generate control command to the actuators. The filtered data are consumed locally and the balance to the higher tiers for visualization, real-time reports and transactional analytics. Fog supports semi-permanent storage at the highest tier and momentary storage at the lowest tier. Fig5.1 Fog computing in smart grid Smart Traffic lights: Fog enables traffic signals to open lanes on sensing flashing lights of the ambulance. It detects presence of pedestrian and bikers, and measures the distance and speed of the close by vehicles. As shown in Figure 5.2,Sensor lighting turns on, on indentifying movements and vice-versa. Smart lights serves as fog devices synchronize to send warning

Department of Computer Science & Engineering, JCE, Belagavi Page 15 IoT and Cyber-physical systems (CPSs): Fog computing based systems are becoming an important class of IoT and CPSs. Based on the traditional information carriers including Internet and telecommunication network, IoT is a network that can interconnect ordinary physical objects with identified address. CPSs feature a tight combination of the system’s computational and physical elements. CPSs also coordinate the integration of computer and information centric physical and engineered systems. IoT and CPSs promise to transform our world with new relationships between computer-based control and communication systems, engineered systems and physical reality. Fog computing in this scenario is built on the concepts of embedded systems in which software programs and computers are embedded in devices for reasons other than computation alone. Examples of the devices include toys, cars, medical devices and machinery. The goal is to integrate the abstractions and precision of software and networking with the dynamics, uncertainty and noise in the physical environment. Using the emerging knowledge, principles and methods of CPSs, we will be able to develop new generations of intelligent medical devices and systems, ‘smart’ highways, buildings, factories, agricultural and robotic systems

CONCLUSION

This proposal of monitoring data access patterns by profiling user behavior to determine if and when a malicious insider illegitimately accesses someone’s documents in a Cloud service. Decoy documents stored in the Cloud alongside the user’s real data also serve as sensors to detect illegitimate access. Once unauthorized data access or exposure is suspected, and later verified, with challenge questions for instance, this inundate the malicious insider

Department of Computer Science & Engineering, JCE, Belagavi Page 16 with bogus information in order to dilute the user’s real data. Such preventive attacks that rely on disinformation technology could provide unprecedented levels of security in the Cloud and in social networks.

REFERENCES

[1] Madsen, Henrik, et al. "Reliability in the utility computing era: Towards reliable Fog computing." Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on. IEEE, 2013. [2] Zhu, Jiang,“Improving Web Sites Performance Using Edge Servers in Fog Computing Architecture”, Service Oriented System Engineering (SOSE), IEEE. 2013. [3] Sabahi, F. “Cloud computing security threats and responses”, In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on 2011,pp. 245-

249. [4] Stolfo, Salvatore J., Malek Ben Salem, and Angelos D. Keromytis. "Fog computing: