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Figure 11-4 Scatter plot of oxygen purity y versus hydrocarbon level x and regression model y 74.20 14.97x.

ORYX is a stream cipher developed by the Telecommunications Industry Association (TIA) as part of an overall cell phone security a r ~ h i t e c t u r e . ~ The TIA system was briefly used in the late 1990s until its many security flaws were exposed. Before considering the ORYX cipher, we briefly discuss cell phone security in general. TIA is not the only flawed cell phone security architecture. The Global System for Mobile Communications (GSM) is another cellular system that has more than its share of security issues. In GSM there are several cryptographic weaknesses, along with protocol-level flaws that open the door to many different feasible attacks [142]. The cryptographic flaws include attacks on the encryption algorithms (specifically, A5/1 and A5/2), as well as serious problems with the hash function (COMP128) that is used for authentication and key generation. These crypto problems in GSM can be traced to the fact that Kerckhoffs' Principle was violated, since the GSM crypto algorithms

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Table 11-2 Minitab Output for the Oxygen Purity Data in Example 11-1 Regression Analysis The regression equation is Purity Predictor Constant HC Level S 1.087 74.3 14.9 HC Level Coef 74.283 14.947 R-Sq

0

3The all-uppercase rendering of ORYX is standard, but it is a mystery, since ORYX is not an acronym.

SE Coef 1.593 1.317 87.7%

T 46.62 11.35 R-Sq (adj)

never received public scrutiny before they were installed in millions of cell phones In defense of GSM, it should be noted that it was designed early in the cell phone (and wireless networking) era, and it was designed with very limited security goals In fact, contrary to what you are likely to read elsewhere, it is not unreasonable to consider GSM security a modest practical success, since none of the many potential attacks ever became a serious issue in practice [142] However, the GSM security design goals were clearly too limited, and this is the root cause of most of the exploitable flaws So-called third generation cell phones, as defined by the Third Generation Partnership Project (SGPP), have a security architecture modeled on GSM, with all of the known flaws patched [I].

P 0.000 0.000 87.1%

As discussed in the Quick Tour, you can change the language of the G11 from English, which is the default setting, to any one of 25 other languages, including Spanish, French, Italian, Polish, and Portuguese.

Analysis of Variance Source Regression Residual Error Total DF 1 18 19 SS 152.13 21.25 173.38 MS 152.13 1.18 F 128.86

P 0.000

The TIA cell phone security architecture was developed after most of the flaws in GSM were well-known, so you might expect that TIA would have learned from the crypto mistakes of GSM If so, you would be mistaken Like GSM before it, TIA violated Kerckhoffs Principle, with predictable results The weak ORYX cipher discussed here is one of the consequences of the decision to use ciphers that had not been thoroughly reviewed-the weak Cellular Message Encryption Algorithm (CMEA) discussed in 4 is yet another The ORYX cipher was designed to encrypt data sent to and from cellular phones Here, data includes voice and other messages sent over the phone This is in contrast to control or signaling information, such as the number called, which was not encrypted using ORYX Instead, the CMEA block cipher mentioned above was used to protect the confidentiality of the control information.

Predicted Values for New Observations New Obs 1 Fit 89.231 SE Fit 0.354 95.0% CI (88.486, 89.975) 95.0% PI (86.830, 91.632)

an estimate of the true population mean purity when x 1.00%, or as an estimate of a new observation when x = 1.00%. These estimates are, of course, subject to error; that is, it is unlikely that a future observation on purity would be exactly 89.23% when the hydrocarbon level is 1.00%. In subsequent sections we will see how to use con dence intervals and prediction intervals to describe the error in estimation from a regression model. Estimating 2 There is actually another unknown parameter in our regression model, 2 (the variance of the error term ). The residuals ei yi yi are used to obtain an estimate of 2. The sum of squares of the residuals, often called the error sum of squares, is

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