Year: 2015

Mobile IP is an Internet Engineering Task Force (IETF) standard communications protocol. In many applications (e.g., VPN, VoIP) sudden changes in network connectivity and IP address can cause problems. Mobile IP protocols were designed to support seamless and continuous internet connectivity. Mobile IP was developed as a means for transparently dealing with problems of mobile users. It enables hosts to stay connected to the Internet regardless of their location and to be tracked without needing to change their IP address. It requires no modifications to IP addresses or IP address format. It has no geographical limitations.
Mobile IP Entities
• Mobile Node (MN)It is the entity that may change its point of attachment from network to network in the Internet. MN assigned a permanent IP called its home address to which other hosts send packets regardless of MN’s location. Since this IP doesn’t change it can be used by long-lived applications as MN’s location changes.

• Home Agent (HA): This is a router with additional functionality. It is Located on home network of MN. HA does mobility binding of MN’s IP with its COA (care of address). It forwards packets to appropriate network when MN is away.

• Foreign Agent (FA): it is a router with enhanced functionality. If MN is away from HA, it uses an FA to send/receive data to/from HA. FA advertises itself periodically. It forwards MN’s registration request. FA de-encapsulates messages for delivery to MN.

• Care-of-address (COA): It is an address which identifies MN’s current location. This address is sent by FA to HA when MN attaches. It is usually the IP address of the FA.

• Correspondent Node (CN): CN is the end host to which MN is corresponding (e.g. a web server).

Home agent and foreign agent

Mobile IP Operations
o A MN listens for agent advertisement and then initiates registration. If responding agent is the HA, then mobile IP is not necessary.
o After receiving the registration request from a MN, the HA acknowledges and registration is complete. Registration happens as often as MN changes networks.
o HA intercepts all packets destined for MN. This is simple unless sending application is on or near the same network as the MN. HA masquerades as MN. There is also a de-registration process with HA if an MN returns home.
o HA then encapsulates all packets addressed to MN and forwards them to FA
o FA de-encapsulates all packets addressed to MN and forwards them via hardware address (learned as part of registration process)

Enhancements to the Mobile IP standard, such as Mobile IPv6 and Hierarchical Mobile IPv6 (HMIPv6), were developed to advance mobile communications by making the processes involved less cumbersome.

Big data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Despite these problems, big data has the potential to help companies improve operations and make faster, more intelligent decisions.
An example of big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible.

History and Consideration
While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
Complexity. Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
Advantages of Big data:
1) Cost reductions
2) Time reductions
3) New product development and optimized offerings
4) Smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

Forensic means to use in court. Computer forensics is the collection, preservation, analysis, extraction, documentation and in some cases, the court presentation of computer-related evidence which has either been generated by a computer or has been stored on computer media.
Traditional Forensic v/s Computer Forensic
The difference between traditional forensics workers and the computer forensics folks is that the traditionalists tend to work in two locations only. The initial crime scene but the majority of the investigation goes on back at the lab. The computer forensics investigators may have to take on their analyses wherever the equipment is located or found rather than just in the pristine environment and the products that are used by investigators come from a market-driven private sector. Traditional forensics investigators are expected to interpret their findings and draw conclusions accordingly whereas computer forensics investigators must only produce direct information and data that will have significance in the case they are investigating.
Component steps for Computer Forensics:
Make a Forensic Image
• Requires Extensive Knowledge of Computer Hardware and Software, Especially File Systems and Operating Systems.
• Requires Special “Forensics” Hardware and Software
• Requires Knowledge of Proper Evidence Handling.

Create Indexes and setup “case”
Access Data Forensic Toolkit (FTK)
• Implements “Hashing” concept.
• Hashing is done using hash functions which is a well-defined procedure or mathematical function for turning some kind of data into a comparatively small integer.
• The values returned by hash function are called hash values

Look for evidence within the image
• View Emails, Documents, Graphics etc.
• Searching for Keywords
• Bookmark relevant material for inclusion into report
• Good investigation skills needed to interview the client to get background material needed to focus the CF investigation.

Generate CF Report
• Usually in HTML format
• Can be printed or on CD-ROM
• Basis for Investigation Report, Deposition, Affidavits, and Testimony.
• CF Report often supplemented with other investigation methods (Online Databases, Email / Phone Interviews)

Forensic Software:
Byte Back, Drive Spy, EnCase , Forensic Tool Kit (FTK), Hash Keeper, Ilook, Maresware, Password Recovery Toolkit (PRTK), Safeback, Thumbs Plus

Pros and cons of Computer Forensics
Pros:
• Searching and analyzing of a mountain of data quickly and efficiently.
• Valuable data which has been deleted and lost by offenders can be retrieved. It may become substantial evidence in court.
• Preservation of evidence.
• Ethical process
Cons:
• Retrieval of data is costly.
• Evidence on the other hand can only be captured once.
• Privacy concerns.
• Data corruption

With computers becoming more and more involved in our everyday lives, both socially and professionally, there is a need for computer forensics. This field will enable crucial electronic evidence to be found, whether it was deleted, lost, hidden, or damaged and used to prosecute individuals that believe they have successfully beaten the system.

Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. One starts at the root and explores as far as possible along each branch before backtracking.
A version of depth-first search was investigated in the 19th century by French mathematician Charles Pierre Trémaux as a strategy for solving mazes.

The order of Excution of nodes are as :1, 2, 3, 4, 5, 6, 7, 8, 9, 10.
Pseudo code:
Input: A graph G and a vertex v of G
Output: All vertices reachable from v labeled as discovered
A recursive implementation of DFS:
1 procedure DFS(G,v):
2 label v as discovered
3 for all edges from v to w in G.adjacentEdges(v) do
4 if vertex w is not labeled as discovered then
5 recursively call DFS(G,w)
A non-recursive implementation of DFS:
1 procedure DFS-iterative(G,v):
2 let S be a stack
3 S.push(v)
4 while S is not empty
5 v = S.pop()
6 if v is not labeled as discovered:
7 label v as discovered
8 for all edges from v to w in G.adjacentEdges(v) do
9 S.push(w)

Applications: There are various applications of DFS.
• Finding connected components.
• Detecting cycle in a graph.
• Finding connected components.
• Finding the bridges of a graph.
• Generating words in order to plot the Limit Set of a Group.
• Finding strongly connected components.
• Planarity testing.
• Solving puzzles with only one solution, such as mazes. (DFS can be adapted to find all solutions to a maze by only including nodes on the current path in the visited set.)
• Maze generation may use a randomized depth-first search.
• Finding biconnectivity in graphs.