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The advantage of HIDS is that it is not affected by the use of end-to-end cryptography or by changes in topology, and routing that may be triggered by node mobility in case of ad hoc networks However, HIDS has several serious drawbacks First, an intruder can avoid detection and produce maximum damage by exploiting the knowledge that only the destination of the traf c analyzes the packets For example if the intruder knows that the destination is using a particular version of the operating system, then it can try to transmit a packet that immobilizes the machine as soon as the destination s network layer assembles the packet and before the HIDS at the application layer analyzes the packet Second, an attack against a host may affect the HIDS itself Often this makes it impossible for the IDS to detect and report the attack since the detection mechanism uses the computation resources and network interfaces at the end host under attack For example, a DoS attack on the target may simultaneously exhaust the bandwidth and CPU on the target These same resources might be needed for detecting and reporting the attack by the HIDS, however HIDS is also not suited for detecting activity such as network scans that could be the precursor to the launch of attacks because such attacks cannot be detected by a single host and require that the IDS looks at traf c across the whole network in order to detect the malicious activity Some limitations of HIDS are more relevant for ad hoc networks than for enterprise networks In ad hoc networks, many of the destination nodes may not be able to execute the IDS due to limited computational resource and low residual energy Further, if only the end hosts execute the IDS, then the malicious packets will not be dropped until they reach the destination This is of concern in ad hoc networks where several nodes may expend their limited energy and bandwidth in relaying malicious packets The other approach to intrusion detection in NIDS has several advantages First, an intruder can no longer be certain that only the destination is executing the IDS Moreover, the IDS active nodes can be selected so that they have different characteristics, thus distributing the IDS load among devices with appropriate capabilities and making it more dif cult for the intruders to devise attacks to bypass the IDS Second, the NIDS approach results in fewer entities (such entitites are also loosely referred to as sensors11), protecting multiple hosts This reduces the cost of running the IDS Also, the sensors themselves are more easily secured in this approach since they can be designed to focus on monitoring in the stealth mode Third, it is easy to retro t existing networks with minimal effort since the IDS only needs to be installed in a subset of the nodes Fourth, the IDS active nodes can be selected only among those that have the required capability Finally, NIDS captures malicious packets in transit and thus limits the wastage of bandwidth and energy in relaying them as discussed earlier The last two advantages in particular are very signi cant when considering ad hoc networks There are also several disadvantages associated with NIDS NIDS might not always be able to work with encrypted information NIDS can analyze encrypted traf c when.

Sn (q) =

Conceptualization refers to the initial goal development and technical specifications for the projects The scope of the work is defined, the necessary resources are identified, and the important organizational contributors or stakeholders are signed on to the process Planning is the stage in which all detailed specifications, schematics, schedules, and other plans are developed The work

log Sn (q) log(n)

The Legendre multifractal spectrum of X is de ned as ( denotes the Legendre transform of ): fl ( ) := ( ) = inf (q (q))

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Being de ned through a Legendre transform fl is a concave function The two spectra fg and fl are related as follows De ne the sequence of random variables k Zn := log |Yn | where the randomness is through a choice of k uniformly in the set {0, , n 1} Consider the corresponding moment generating functions: cn (q) := log En [exp(qZn )] log(n)

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where En denotes expectation with respect to Pn A version of G rtner-Ellis theorem ensures that if lim cn (q) exists (in which case it equals 1 + (q)), and is differentiable, then c = fg 1 We then say that the weak multifractal formalism fg = fl holds 113 H lderian regularity estimation 1131 Oscillations (OSC) The most natural way to estimate regularity is to use the oscillation method This method is a direct application of the de nition of the H lder exponent (see [TRI 95] for more on this topic)

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