Detecting malicious behaviour in participatory sensing settings

Security is crucial in modern computer systems hosting private and sensitive information. Our systems are vulnerable to a number of malicious threats such as ransomware, malware and viruses.  Recently, a global cyberattack (ransomware) affected hundred of organisations, most notably the UK’s NHS.  This malicious software “locked” the content stored on organisations’ hard drives, requiring money (to be paid in bitcoins) to “unlock” it and make it available back to their owners. Crowdsourcing (the practice of obtaining information by allocating tasks to a large number of people e.g. Wikipedia) is not immune of malicious behaviour. On the contrary, the very openness of such systems make them ideal for malicious users to alter, corrupt or falsify information (data poisoning). In this post, we present an environmental monitoring example, where ordinary people take air quality readings (using mobile equipment) to monitor air pollution of their city or neighbourhood (see our previous post for more details on this example). Arguably, some people participating in such environmental campaigns can be malicious. Specifically, instead of taking readings to provide information about their environment,  they might deviate by following their own secret agenda. For instance, a factory owner might alter the readings showing that their factory pollutes the environment. The impact of such falsification is huge as it basically changes the overall picture of the environment, which in turn leads authorities to wrong actions regarding urban planning.

We argue that Artificial Intelligence (AI) techniques can be of great help in this domain. Given that measurements have a spatio-temporal correlation, a non-linear regression model can be overlaid over the environment (see previous post). The tricky part however is to differentiate between truthful and malicious readings. A plausible solution is to extend the non-linear regression model by assuming that each measurement has an individual and independent noise (variance) from each other (heteroskedasticity). For instance, a Gaussian Process (GP) model can be initially used and then extended to Heteroskedastic GP (HGP). The consequence of this action is that this individual noise can indicate the deviation of each measurement compared to the truthful measurements, which can either be attributed to sensor noise (which is always present in reality) or in malicious readings. An extended version of HGP, namely Trust-HGP (THGP), assigns a trust parameter to the model that captures the possibility of each measurement being malicious between the interval of (0,1).  The details of the THGP model as well as how it is utilised in this domain will be presented end of October at the fifth AAAI conference on human computation and crowdsourcing (HCOMP 2017). Stay tuned!

Submodularity in Sensor Placement Problems

Many problems in Artificial Intelligence and in computer science in general are hard to solve. What practically this means is that it would take a computer probably hundred/thousands/millions of years of computation to solve it. Thus, many scientists tend to create algorithms that approximately solve difficult problems but in a sensible time period, i.e., seconds/minutes/hours.

A problem like this is the sensor placement problem. The key question here is to find a number of locations to place some sensors in order to achieve better coverage of the interested area. In order to solve this problem the computer has to compute all the possible combinations of placing the sensors we have in all different locations. To give some numbers, having 5 sensors and 100 possible locations, one has to try 75287520 combinations in order to find the best arrangement. Imagine what happens when the problem is about placing hundreds of sensors in a city where there are hundreds or thousands of options.

In such problems submodularity comes handy. It is an extremely important property used in many sensor placement problems.  It is a theorem that describes the behavior of functions. In particular, the main idea is that an addition to a small set has a higher return/utility/value rather than adding the same thing to a larger set. This can be better understood with an example. Imagine having 10000 sensors scattered in a big room taking measurements of the temperature every 2 hours. Now imagine adding another sensor to that room. Have we really gained much for doing so? So, we have a large set and we add something. Similarly, imagine the same room having only 1 sensor. Adding 1 more can give us better understanding of probably some corner or get a better estimate of the true average temperature of the room. So, this sensor was much more valuable to have that in the previous case. This is what i mean by saying that adding something to a smaller set has a higher utility.

It turns out that this property is very useful at maths and in computer science and AI in particular as it allows us to build algorithms that have theoretical guarantees. It has been proved that a greedy algorithm has a 63% of the optimal algorithm in terms of performance. This was initially proved from Nemhauser in maths contents and later from Krause et. al in the field of computer science and especially for the sensor placement problem. The image below shows this property in terms of diagrams to get a better feeling of what this property is about.

Submodularity (taken from Meliou et al. power point presentation)
Submodularity (taken from Meliou et al. power point presentation)