‘…Now more than ever, businesses need to be concerned about the security of their networks. The number, variety and strength of the threats to computer and network security have dramatically increased and businesses need to be prepared…’
An area of significant concern for increasing malware attacks to business security is in mobile devices. Mobile devices are the fastest growing device segment. With the expanding adoption comes increased vulnerability for threats to business security. Mobile device users not only send emails and edit documents, but also check stock portfolios and conduct financial transactions from their phones. Mobile devices are now full of sensitive personal data—making it easy for criminals to steal identities and fraudulently collect personal financial information. The trend towards providing mobile devices with web browsers and always-on internet access has brought all the security concerns of the web to the mobile world and their connected enterprises.
As capabilities expand, security is traded for functionality, giving rise to a whole new class of opportunities for malicious attacks. Much like viruses on a computer, viruses on mobile devices can delete files, infect files, send private information from the mobile device and facilitate external attacks. The most common way attackers gain access to confidential information is through the loss or theft of a mobile device. With the size and portability of these devices, the loss or theft of a mobile phone has become a reality. Worms and Trojans, as well as spam and phishing are making their way to mobile devices. Also known as smishing, these threats use SMS to transport spam and phishing attacks to the user’s phone – jeopardizing confidential information. Another threat targeting mobile devices is spyware also known as Snoopware. Snoopware can secretly activate the microphone and camera on a device to snoop on conversations and other dialogue in the immediate vicinity of the phone. This particular threat can be especially dangerous to users who may pass along sensitive business and personal data in conversation. To make sure businesses and users to keep their business and personal information safe, policies must be set in place to protect both the business and users when they are accessing critical information from mobile devices.
Users of mobile devices for business needs have to use the best practices for safeguarding their information. These include:
- Adopting a multi-layer security approach to mobile security, this involves protecting mobile devices with anti-virus, firewall, anti-SMS spam, and data encryption technologies and install regular security updates to protect phones and corporate information from viruses and other malware. Businesses should provide this technology to their employees and teach them how to use it properly...
- Businesses should encourage employees to be vigilant about personal security, they should ensure that their mobile devices are safe and securely with them when in places that they could be stolen.
- Businesses should set policies to password-protect mobile devices, all employees who use these devices should use strong passwords and change them regularly to ensure against theft.
- Businesses should encourage employees to regularly back up their information just as they would on a normal computer to counter against he loss of the device or loss of information on the mobile device.
- Businesses should encourage employees to only use secured networks when they are accessing company information.
Wednesday, November 18, 2009
Can neural networks be useful for business?
Neural networks can provide significant benefits in business applications. Neural networks are applicable in virtually every situation in which a relationship between the predictor variables and predicted variables exists, even when that relationship is very complex and not easy to articulate in the usual terms of "correlations" or "differences between groups". Examples of problems to which neural network analysis has been applied are:
-Detection of medical phenomena. A variety of health-related indices can be monitored. The onset of a particular medical condition could be associated with a very complex combination of changes on a subset of the variables being monitored. Neural networks have been used to recognize this predictive pattern so that the appropriate treatment can be prescribed.
-Stock market prediction. Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially-deterministic phenomenon. Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as past performance of other stocks and various economic indicators.
-Credit assignment. A variety of pieces of information are usually known about an applicant for a loan. For instance, the applicant's age, education, occupation, and many other facts may be available. After training a neural network on historical data, neural network analysis can identify the most relevant characteristics and use those to classify applicants as good or bad credit risks.
-Monitoring the condition of machinery. Neural networks can be instrumental in cutting costs by bringing additional expertise to scheduling the preventive maintenance of machines. A neural network can be trained to distinguish between the sounds a machine makes when it is running normally versus when it is on the verge of a problem. After this training period, the expertise of the network can be used to warn a technician of an upcoming breakdown, before it occurs and causes costly unforeseen downtime.
-Engine management. Neural networks have been used to analyze the input of sensors from an engine. The neural network controls the various parameters within which the engine functions, in order to achieve a particular goal, such as minimizing fuel consumption.
Neural networks are also actively being used for such applications as bankrutcy predictions, predicting costs, forecast revenue, processing documents and more..
The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. There is no need to devise an algorithm in order to perform a specific task i.e there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.
-Detection of medical phenomena. A variety of health-related indices can be monitored. The onset of a particular medical condition could be associated with a very complex combination of changes on a subset of the variables being monitored. Neural networks have been used to recognize this predictive pattern so that the appropriate treatment can be prescribed.
-Stock market prediction. Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially-deterministic phenomenon. Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as past performance of other stocks and various economic indicators.
-Credit assignment. A variety of pieces of information are usually known about an applicant for a loan. For instance, the applicant's age, education, occupation, and many other facts may be available. After training a neural network on historical data, neural network analysis can identify the most relevant characteristics and use those to classify applicants as good or bad credit risks.
-Monitoring the condition of machinery. Neural networks can be instrumental in cutting costs by bringing additional expertise to scheduling the preventive maintenance of machines. A neural network can be trained to distinguish between the sounds a machine makes when it is running normally versus when it is on the verge of a problem. After this training period, the expertise of the network can be used to warn a technician of an upcoming breakdown, before it occurs and causes costly unforeseen downtime.
-Engine management. Neural networks have been used to analyze the input of sensors from an engine. The neural network controls the various parameters within which the engine functions, in order to achieve a particular goal, such as minimizing fuel consumption.
Neural networks are also actively being used for such applications as bankrutcy predictions, predicting costs, forecast revenue, processing documents and more..
The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. There is no need to devise an algorithm in order to perform a specific task i.e there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.
Tuesday, October 27, 2009
The workings of a Genetic Algorithm
Genetic algorithms are a way of solving problems by mimicking processes that mother nature uses. They use the same combination of selection, recombination and mutation to evolve a solution to a problem. Solutions are encoded on chromosomes. The chromosomes are altered using methods which are loosely based on the theories of Evolution first suggested by Charles Darwin.
A genetic algorithm works like this: A population is created with chromosomes created randomly. The chromosomes in the population are then evaluated to rate their fitness. Two chromosomes are then selected, the higher the fitness of the chromosome, the higher chance it has of being selected to "reproduce". These two chromosomes then crossover to create a new chromosome. This process continues until a suitable solution has been found or a certain number of generations have passed, depending on the need of the programmer.
Outline of the Basic Genetic Algorithm
1. [Start] Generate random population of n chromosomes (suitable solutions for the problem)
2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population
3. [New population] Create a new population by repeating following steps until the new population is complete
- [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)
- [Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents.
- [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).
- [Accepting] Place new offspring in a new population
4. [Replace] Use new generated population for a further run of algorithm
5. [Test] If the end condition is satisfied, stop, and return the best solution in current population
6. [Loop] Go to step 2
A genetic algorithm works like this: A population is created with chromosomes created randomly. The chromosomes in the population are then evaluated to rate their fitness. Two chromosomes are then selected, the higher the fitness of the chromosome, the higher chance it has of being selected to "reproduce". These two chromosomes then crossover to create a new chromosome. This process continues until a suitable solution has been found or a certain number of generations have passed, depending on the need of the programmer.
Outline of the Basic Genetic Algorithm
1. [Start] Generate random population of n chromosomes (suitable solutions for the problem)
2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population
3. [New population] Create a new population by repeating following steps until the new population is complete
- [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)
- [Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents.
- [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).
- [Accepting] Place new offspring in a new population
4. [Replace] Use new generated population for a further run of algorithm
5. [Test] If the end condition is satisfied, stop, and return the best solution in current population
6. [Loop] Go to step 2
Tuesday, October 13, 2009
Social Networking....Is it hype or a real business tool?
Social networks focuses on building online communities based on common interests and activities. For years social networking sites have been used as a place where friends have been able to communicate online, share information about themselves, upload their photos and videos and join groups. In this blog entry what I am going to discuss is whether social networking sites can be used as a real business tool...
Let me first begin with the social networking site that i am most familiar with, Facebook. I have noticed an increasing amount of local business who have set up an account and added me as their "friend" along with people who have joined specific networks for example the NUIGalway network. This has allowed these businesses to spread the word about their products, events, offers to an ever growing community at very little cost and at very little effort. Businesses can use facebook as their very own broadcasting channel. For Example A company called bazaarvoice used facebook to aid in their recruiting campaign. They got all their employees to post "Bazaarvoice is hiring" on their facebook profiles and twitter. They averaged 180 visits per day to their jobs page. The day they did their Social Network recruiting campaign they got over 1,600 visits the first day, and then over 500 each of the next two days.
Facebook is a social networking site that was created without the intention of it being used as a business tool. Others however have been created with businesses in mind. LinkedIn is such a networking site that is used for professional networking. With over 48 million members LinkedIn connects you to your trusted contacts and helps you exchange knowledge, ideas, and opportunities with a broader network of professionals. This can only be a helpful tool for businesses and employees.
Social networking is just an alternative way of communicating. It has a large audience and is very cost effective. It is a place to demonstrate your expertise to people and raise awareness. Its a place for contacting existing and potential customers. With the ultimate aim to put your product or services out there infront of your customers, this is precisely what social networking does.
Let me first begin with the social networking site that i am most familiar with, Facebook. I have noticed an increasing amount of local business who have set up an account and added me as their "friend" along with people who have joined specific networks for example the NUIGalway network. This has allowed these businesses to spread the word about their products, events, offers to an ever growing community at very little cost and at very little effort. Businesses can use facebook as their very own broadcasting channel. For Example A company called bazaarvoice used facebook to aid in their recruiting campaign. They got all their employees to post "Bazaarvoice is hiring" on their facebook profiles and twitter. They averaged 180 visits per day to their jobs page. The day they did their Social Network recruiting campaign they got over 1,600 visits the first day, and then over 500 each of the next two days.
Facebook is a social networking site that was created without the intention of it being used as a business tool. Others however have been created with businesses in mind. LinkedIn is such a networking site that is used for professional networking. With over 48 million members LinkedIn connects you to your trusted contacts and helps you exchange knowledge, ideas, and opportunities with a broader network of professionals. This can only be a helpful tool for businesses and employees.
Social networking is just an alternative way of communicating. It has a large audience and is very cost effective. It is a place to demonstrate your expertise to people and raise awareness. Its a place for contacting existing and potential customers. With the ultimate aim to put your product or services out there infront of your customers, this is precisely what social networking does.
Sunday, October 11, 2009
History of Artificial Intelligence
Artificial Intelligence is the capability of a device to perform functions that are normally associated with human intelligence, such as reasoning and optimization through experience. Alan Turing who is regarded as the father of modern computer science devised a test of a machine's ability to demonstrate intelligence.
Turing Test: Can someone tell which is the machine, when communicating to human and to a machine in another room? If not, can we call the machine intelligent?
In this blog i will give a brief history into the ground breaking devices that have shown this ability to demonstrate intelligence.
The History of Artificial Intelligence in Robotics
SRI's Shakey, called because of its jerky motion, was the first mobile robot that could claim to reason about its actions. The design was a box of electronics on wheels, with bump detectors at the base and a TV camera and triangulating range finder for a head. Shakey could locate items, navigate around them, and reason about its actions. Shakey's great accomplishment was that it could take general instructions that were not "step-by-step" and still figure out how to accomplish the objective.
The Stanford Cart, it was the first computer controlled autonomous vehicle. Like shakey, the stanford cart saw using tv cameras. The Cart followed smudgy white lines seen from a high vantage point in variable illumination quite reliably using adaptation and prediction methods. It successfully crossed a room full of chairs in 1979.
Asimo was crated by honda and is a humanoid robot. The experimental models were first introduced in 1986 and the first Asimo unveiled in 2000. Asimo uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. It uses recognition technology were asimo can recognise moving objects, postures and gestures, its environment, it can distinguish sounds and has facial recognition.
Kismet is a robot with rudimentary social skills, created in the late 1990's at MIT it is able to interact with humans in social situations and express human emotions. Kismet reads both audio and visual social cues thanks to an advanced system of cameras, microphones and computers.
TOPIO is a bipedal humanoid robot that can play ping-pong, developed by TOSY in 2005.The hydraulics manipulated robot is composed of a carbon fiber body allowing for fast reactions and a flexible range of movements with 20 degrees of freedom. The ball's trajectory and spin is detected using 4 high speed cameras and 2 processing units, and it's advanced Artificial Intellegence module allows it to continuously improve itself while playing.
The History of Artificial Intelligence in Games
Artificial Intelligence is used in games so that the non-playing character, the computer controlled characters, offer the illusion of intelligence. The first game to incorporate AI using the ferranti mark 1 machine was a checkers game and it eventually developed sufficient skill to challenge a respectable amateur.
In Pac-Man Artificial Intelligence gave each enemy ghost its own particular behaviour and movement. This unpredictability of movement and behavior gave the ghosts an "inner-life" and is why those ghosts would be considered the fathers of Artificial Intelligence in games. During the 80's more complex games were developed but all using this same AI, basic and heavily scripted movement. During the 90's with better machines and using formal AI tools like finite state machines, real time strategy games were developed including Dune 2 but these had many problems including using numerous cheats. In first person shooters such as Golden Eye, Half-life, Halo, they featured enemies which had intelligence, teamwork skills and could recognise threats such as grenades etc. AI has continued to improve, with aims set on a player being unable to tell the difference between computer and human players.
The History of Artificial Intelligence in Toys
Not all uses of Artificial Intelligence are specifically designed to advance the interests of the science or technology communities. Some are just fun. In 1996 The Tomogotchi was created which was a handheld digital pet that you would look after, feed, walk etc, just as you would a real pet. Furbys were the next major development in AI in toys. Furby includes both sensors and affectors enabling it to “sense” information from the environment and permit a response to the environment. Aibo, sonys robotic dog is probably the most famous of the AI toys. It could speak up to 1000 words, react to its owner's motions and commands, keep blogs, take pictures with its eyes, and play music. Toys that learn are becoming increasingly more and more complex exhibiting greater and more advanced Artificial Intelligence.
Turing Test: Can someone tell which is the machine, when communicating to human and to a machine in another room? If not, can we call the machine intelligent?
In this blog i will give a brief history into the ground breaking devices that have shown this ability to demonstrate intelligence.
The History of Artificial Intelligence in Robotics
SRI's Shakey, called because of its jerky motion, was the first mobile robot that could claim to reason about its actions. The design was a box of electronics on wheels, with bump detectors at the base and a TV camera and triangulating range finder for a head. Shakey could locate items, navigate around them, and reason about its actions. Shakey's great accomplishment was that it could take general instructions that were not "step-by-step" and still figure out how to accomplish the objective.
The Stanford Cart, it was the first computer controlled autonomous vehicle. Like shakey, the stanford cart saw using tv cameras. The Cart followed smudgy white lines seen from a high vantage point in variable illumination quite reliably using adaptation and prediction methods. It successfully crossed a room full of chairs in 1979.
Asimo was crated by honda and is a humanoid robot. The experimental models were first introduced in 1986 and the first Asimo unveiled in 2000. Asimo uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. It uses recognition technology were asimo can recognise moving objects, postures and gestures, its environment, it can distinguish sounds and has facial recognition.
Kismet is a robot with rudimentary social skills, created in the late 1990's at MIT it is able to interact with humans in social situations and express human emotions. Kismet reads both audio and visual social cues thanks to an advanced system of cameras, microphones and computers.
TOPIO is a bipedal humanoid robot that can play ping-pong, developed by TOSY in 2005.The hydraulics manipulated robot is composed of a carbon fiber body allowing for fast reactions and a flexible range of movements with 20 degrees of freedom. The ball's trajectory and spin is detected using 4 high speed cameras and 2 processing units, and it's advanced Artificial Intellegence module allows it to continuously improve itself while playing.
The History of Artificial Intelligence in Games
Artificial Intelligence is used in games so that the non-playing character, the computer controlled characters, offer the illusion of intelligence. The first game to incorporate AI using the ferranti mark 1 machine was a checkers game and it eventually developed sufficient skill to challenge a respectable amateur.
In Pac-Man Artificial Intelligence gave each enemy ghost its own particular behaviour and movement. This unpredictability of movement and behavior gave the ghosts an "inner-life" and is why those ghosts would be considered the fathers of Artificial Intelligence in games. During the 80's more complex games were developed but all using this same AI, basic and heavily scripted movement. During the 90's with better machines and using formal AI tools like finite state machines, real time strategy games were developed including Dune 2 but these had many problems including using numerous cheats. In first person shooters such as Golden Eye, Half-life, Halo, they featured enemies which had intelligence, teamwork skills and could recognise threats such as grenades etc. AI has continued to improve, with aims set on a player being unable to tell the difference between computer and human players.
The History of Artificial Intelligence in Toys
Not all uses of Artificial Intelligence are specifically designed to advance the interests of the science or technology communities. Some are just fun. In 1996 The Tomogotchi was created which was a handheld digital pet that you would look after, feed, walk etc, just as you would a real pet. Furbys were the next major development in AI in toys. Furby includes both sensors and affectors enabling it to “sense” information from the environment and permit a response to the environment. Aibo, sonys robotic dog is probably the most famous of the AI toys. It could speak up to 1000 words, react to its owner's motions and commands, keep blogs, take pictures with its eyes, and play music. Toys that learn are becoming increasingly more and more complex exhibiting greater and more advanced Artificial Intelligence.
Tuesday, October 6, 2009
Cloud Computing
As Oracle CEO Larry Ellison stated "cloud computing is going to be or already is the most important computing architecture in the world"
Cloud computing is where data, software applications and computer processing power are acceded from a cloud of online resources. This permits individual users access to their data and applications form any device. It also allows organisations to reduce their costs by purchasing software and hardware as a service.
There are three main reasons why cloud computing is becoming so popular.
1. Scalability - With cloud computing it is easy to grow or shrink your hardware/sofware to match your demand.
2. Instant - Cloud computing is instant to match your demand, whatever the needs of the organisation, these resources are instantly available to them using cloud computing.
3. Save Money - The organisation only pays for what it uses and when it uses it.
There are three main types of cloud computing.
Software as a service - This is the most commonly used type of cloud computing. It provides, through a web browser, all the functions of a traditional application. These are hosted by a service provider and are made available over a network. Examples of this type of cloud computing would be Clarizen's online project management tools, Salesforce's customer relationship management and human resource applications and webware's online office applications.
Hardware as a service - This is where computing processing capacity, including storage, servers and network components are purchased over the web. Amazon's Elastic Compute loud is a good example of this. EC2 allows users to purchase computer processing power online for whatever needs they may have.
Platform as a service - This is where users can use applications on visualized servers without having to worry about maintaining the operating systems, server hardware and computer capacity. Microsoft's Azure is a good example of this.
Cloud computing is where data, software applications and computer processing power are acceded from a cloud of online resources. This permits individual users access to their data and applications form any device. It also allows organisations to reduce their costs by purchasing software and hardware as a service.
There are three main reasons why cloud computing is becoming so popular.
1. Scalability - With cloud computing it is easy to grow or shrink your hardware/sofware to match your demand.
2. Instant - Cloud computing is instant to match your demand, whatever the needs of the organisation, these resources are instantly available to them using cloud computing.
3. Save Money - The organisation only pays for what it uses and when it uses it.
There are three main types of cloud computing.
Software as a service - This is the most commonly used type of cloud computing. It provides, through a web browser, all the functions of a traditional application. These are hosted by a service provider and are made available over a network. Examples of this type of cloud computing would be Clarizen's online project management tools, Salesforce's customer relationship management and human resource applications and webware's online office applications.
Hardware as a service - This is where computing processing capacity, including storage, servers and network components are purchased over the web. Amazon's Elastic Compute loud is a good example of this. EC2 allows users to purchase computer processing power online for whatever needs they may have.
Platform as a service - This is where users can use applications on visualized servers without having to worry about maintaining the operating systems, server hardware and computer capacity. Microsoft's Azure is a good example of this.
Wednesday, September 16, 2009
Integrated Systems in State Government
In this post I will be discussing a few of the processes the State of Louisiana went through while implementing SAP R/3.
Change management is necessary in the implementation of an integrated system for various reasons. As Michael Hammer stated that “The hard (technical) stuff is the easy stuff, the soft (people oriented) stuff is the hard stuff.” People do not easily adapt to change, and “Some of them feared the imminent change themselves”. It is vital to have change managemnt experts on any team that is implementing an integrated system for any lagre scale projects. It is necessay for change anagement to force people from different parts of the organisation to meet and discuss and understand each other’s business so that the needs of each department can be reached. Implementing a successful integrated system requires communication among all involved. It is also important for change management to reach a balance between users of the technology and IT department as they have different wants from the new system, users concentrate on the short term satisfactions, while the technologists concentrate on the mastery of the technology.
It was vital for the state to involve their own technical employees in the development and integration of the new system. Firstly the State technical employees know the end users of the system an know there requirements from the system. Once the system is in place it will be up to the technical employees to keep the functioning and to be able to update and new requirements that may be needed by the users. They will need to be able to fix any problems and answer any query that the end user might have so they have to be well versed with the new systems before the outsourced implementation team leave.
The scope of integrated systems can change while implementation is taking place. This can happen for a number of different reasons. One can be due to the end users needs. The end user may not know exactly what requirements they need or might need to change their requirements during implementation. This can lead to delays in the system being developed, it can also raise on costs on the new system. Another change in the scope of a project, as in the State of Louisiana’s system, a newer version of the software can be developed providing better functionality for the users, this can lead to changes to the scope of the project.
When newer versions of software become available to systems, organisations have to look at them closely to see what sort of impact they would have on their organisation. If the new capabilities are wanted and needed by their users they cant ignore this new technology. In the State of Louisiana integrated system they had a choice to make, whether to “go-live” with the integrated system or hold back and for this newly developed version which had capabilities that their users wanted and needed. I feel delaying the ‘go-live” date to wait for these new functionalities and capabilities would have been the best option for the State of Louisiana as the short term delay will lead to long term benefit. It saves on employee confusion and help with employee confidence in the new system, if the older system was to be implemented and then updated quite quickly, employees would question the system and confidence in it might be lost. It saves on training cost and time as the users would only have to have one training day, and one set of training aids. The time would also allow the State technical employees to further their knowledge on the new integrated system to deal with any confusion in the future.
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