### Making the most of forensic downtime

As a computer forensic investigator, at times the caseload can become a bit overwhelming. Sometimes the requests come pouring in; other times the queue will be empty. Looking back through several months of work, you can put together a reasonable estimate of the average arrival rate and average completion rate for forensic investigation requests. Armed with these two pieces of data and some equations from queuing theory, you’ll be able to estimate the amount of cumulative non-investigation time that will likely be available for other tasks over the course of a year.

Naturally much of that down time should be spent “sharpening the saw;” maintaining tools and scripts, etc. But as discussed in the last post, it may also be helpful to leverage those forensic tools and skills to measure risks in the end user environment. Taken periodically, these measurements support the execution of a security strategy by providing the evidence needed to drive changes that will ultimately reduce the frequency and impact of incidents that do occur.

Queuing theory can become complex in a hurry, but there are a few formulas that are easy to use and very helpful if you make a few reasonable simplifying assumptions. For the long version, check out Contemporary Management Science by Anderson, Sweeney and Williams. Or an online excerpt of the relevant chapter is available here.

If you know the average arrival rate of requests, and have a good feel for how long it takes to complete the typical investigation, you can calculate the following:

1. Probability of an empty queue with no requests in process:

1 – (arrival rate / service rate)

2. Average number of pending requests:

((arrival rate) ^ 2) / (service rate * (service rate – arrival rate))

3. Average number of investigations in the systems:

Average number of pending requests + (arrival rate / service rate)

4. Average time a request has to wait before the investigation starts:

Average number of pending requests / arrival rate

5. Average resolution time; from initial request to completed resolution:

Request wait time + (1 / service rate)

6. Probability that a new request has to wait for service:

Arrival rate / service rate

7. The probability of N number of investigations and requests that are in the system at a given point in time:

(arrival rate / service rate)^N * probability of an empty queue

These equations provide a good approximation if the following assumptions hold true:

1. For a given time period, you’re almost always going to get between zero and 2 requests (i.e. 95% likely) and only rarely do you get a bunch of requests (5% chance of 3 or more requests arriving at once).

2. Few service requests will take significantly than the average service time to complete.

3. You’ve got one investigator servicing requests – a “single service channel.”

So, as an example, suppose the average request arrival rate is about 3 cases per month, and an investigator can complete about 4 cases each month. Calculate expected downtime using this proces:

First, convert the monthly numbers to a weekly rate, 3 arrivals per month is a 0.75 weekly arrival rate, and 4 completions per month is a 1.0 service rate. Then, plug and go:

The probability of zero requests in queue is:

1 – (.75 / 1) = 0.25

A 25% chance of having a week with no requests in progress.

So in this “best case” scenario, roughly 25% of an annual 2000 hours worked won’t be directly allocated to investigating live cases; 500 hours. In reality, this estimate is almost certainly too low for a couple of reasons. First, management isn’t likely to over-staff a forensic role; demand will rise to fill that capacity, and inevitably many long-running difficult cases will come up that fall outside the average completion rate by a big margin. And investigators will need to factor in time for script development, system administration, and other tasks.

Assuming that a residual 200 hours (10%) of time remains available throughout the course of the year, this can provide the perfect opportunity to quantify policy compliance against specific goals.

So how much can you do with 200 hours? Turns out, quite a bit.

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