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These stages are the way-points at which the project team can evaluate their performance and the project s overall status This life cycle is relevant only after the contract has been signed It begins with project development, the creation of myriad plans and schedules, the work of project development being performed, and the completion of the project and reassignment of personnel When we evaluate projects in terms of this life-cycle model, the key determinants of success often relate far more to those traditional, internal metrics of time, schedule, and performance than under the alternative model In the second-life cycle model, we consider the whole project life cycle starting with initial response to a client need or RFP, sales contact, mutual needs assessment between the client and the bidding companies, bidding process, contract award, project development, delivery, installation, and operation of the completed product That is, the second model includes the original four-stage life cycle plus several additional activities related to both front-end and back-end operations Figure 16 gave an example of this large-scale project life cycle This model, which mirrors a number of situations from new product development to construction and engineering support services, is becoming increasingly prevalent Further, it is easy to see that when the project life cycle is evaluated in this larger sense, a wide variety of external relationships with customers, suppliers, and distributors come into play Clearly, the implications of the larger project life cycle suggest that greater challenge of running projects when all the relevant external stakeholders must be considered as well

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A third less widely used technique is called speci cation-based detection We now discuss each of these techniques in detail The misuse detection technique involves analyzing the collected data for speci c behavior patterns known to be consistent with speci c attacks These behavior patterns are called signatures For example, a UDP packet destined to port 0 can crash some machines [79] The signature of a ping-of-death attack is a very large ping packet; the signature of an RPC locator attack is a packet intended for port 135 that contains a command that the system is not expecting; the signature of a Bubonic attack is made from various values, such as a TTL of 255, a TOS eld value of 0 C9, exactly 20 byte payload in the IP datagram, and the fragment ID value with consistent increments of 256 [79] The misuse detection technique is being used widely today by commercial systems because it allows for very precise detection of attacks, thereby resulting in low false alarm rates When a signature for an attack has been identi ed (typically a bit-by-bit precise speci cation of an attack packet), it is straightforward to detect the attack by comparing a packet against the precise attack signature The problem with misuse detection intrusion detection techniques is that these techniques can only detect previously known attacks with wellde ned patterns of malicious behavior Novel attacks that have not been seen before cannot be detected until a new signature for the attack is created The anomaly detection technique involves looking for behavior that is outside the normal expected behavior This is usually done by utilizing statistical techniques that compare the observed behavior against the statistics of the expected normal behavior These techniques often utilize thresholds (such as amount of excessive loading of CPU, counts of traf c of speci c type, etc) that if exceeded indicate an attack Therefore, detectors utilizing this technique require prior training so that the thresholds used for detecting abnormal behavior are set at the appropriate values The advantage of anomaly-based detection is that this technique does not require the existence of precise signatures and can therefore be used for detecting attacks not previously seen The disadvantage of anomaly-based detection is that, due to its statistical nature, it is more prone to false positives depending on how the thresholds are set There is always a trade-off between setting very tight thresholds (close to normal behavior), thus triggering a lot of false positives when users deviate even slightly from expected behavior, and setting very loose thresholds that minimize false positives but allow.

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Figure 111 (a) Generalized Weierstrass function (4096 points), regularity h(t) = t; (b) regularity estimation by W CR; (c) regularity estimation by W L; (d) regularity estimation by OSC

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