‘…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.
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