A lightweight privacy-preserving scheme for metering data collection in smart grid

Privacy Preserving Smart Meter Streaming Against Information Leakage of Appliance Status User daily behavior disturbance model to preserve privacy for smart metering
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Dr.LindaBarros,United States,Researcher
Published Date:14-08-2017
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A Privacy Preserving Approach to Smart Metering Abstract. High frequency power consumption readings produced by smart meters introduce a major privacy threat to residential consumers as they reveal details that could be used to infer information about the activities of home oc- cupants. In this paper, we question the need to disclose high frequency read- ings produced at the home’s level. Instead, we propose equipping smart meters with sufficient processing power enabling them to provide the utility company with a set of well-defined services based on these readings. For demand side management, we propose the collection of high frequency readings at a higher level in the distribution network, such as local step-down transformers, as this readily provides the accumulated demand of all homes within a branch. Fur- thermore, we study the effect of the proposed approach on consumers’ privacy, using correlation and relative entropy as measures. We also study the effect of load balancing on consumers’ privacy when using the proposed approach. Fi- nally, we assess the detection of different appliances using high frequency read- ings collected for demand side management purposes. Keywords: Smart Grid, Smart Meter, Privacy, Advanced Metering Infrastruc- ture (AMI). 1 Introduction The electric grid in use today is undergoing a transformation to improve its efficiency and reliability through the use of computation and communication technologies. A major part of this is the enhancement of the distribution network through the introduc- tion of smart meters. These meters collect power consumption readings and transmit them to the utility company in an automated way. Unlike traditional meters, smart meters produce detailed chronological high frequency readings that reveal both the time of consumption and the amount of power consumed. Utility companies argue that high frequency readings form the basis for time of use billing schemes, which are expected to cause consumers to shift part of their consumption to off-peak hours re- sulting in a flat demand profile (i.e., one with a small peak-to-average ratio) and im- proving energy production and consumption efficiencies 1. Furthermore, it is argued that a clearer vision of the distribution network helps in improving service quality and reliability. For example, a utility company could centrally detect and respond to A Privacy Preserving Approach to Smart Metering 165 blackouts and brownouts more effectively in comparison to user initiated notifications 2. Because of this, smart metering is viewed as the fundamental platform that facili- tates service enhancement, reliability, and efficiency and is considered an enabler for technologies such as proactive energy consumption management and load balancing techniques 3 5. By their nature, electrical appliances consume power in specific patterns which produce detectable signatures. For example, a standard incandescent lamp constantly consumes a fixed amount of power during its operation period, whereas a refrigerator consumes most of its power during its cooling cycles, when the compressor is run- ning, and significantly less power during its idle cycles. Such patterns can be used to produce signature libraries which can be used for appliance detection and identifica- tion 6 7. Given a library of power consumption signatures of appliances, and the detailed power consumption of a home, this home’s consumption can be decomposed and individual appliances can be detected using Nonintrusive Appliance Load Monitoring technologies (NALM) 8. Fig. 1 shows an example of appliance detection using power signatures. As shown, many appliances can be identified through their distinct power consumption patterns. That is, with NALM technologies, high frequency read- ings produced by smart meters offer a window into the activities of homes’ occupants. This includes the identification of appliances and any other information possibly in- ferable from the appliances used. Furthermore, by observing the real time power con- sumption of a given home, an intruder can identify when the occupants are awake/asleep or whether the home is occupied or not 9. Fig. 1. Appliance identification through power consumption patterns 8 Although high frequency readings produced by smart meters enable improving the efficiency of the electric grid, they introduce a privacy threat that was not present in the classical grid. In this paper, we present an alternative approach to smart metering with the objective of maintaining its advantageous functionality while preserving consumers’ privacy. In the following section, we briefly review some related work in this area. Our approach to smart metering is explained in section 3. In section 4, we assess the privacy gained from the proposed approach. Finally, in section 5, we present our conclusion. 166 M. Shinwari, A. Youssef, and W. Hamouda 2 Related Work The privacy impact of collecting power consumption readings in high frequency is well known and widely studied. Reports such as 10, 11, and 12 indicate that the privacy concerns of smart metering must be taken into consideration and addressed at the design stage rather than as a later addition. Furthermore, many researchers have proposed various approaches to address this problem. In this section, we present a selection of the main contributions in this area highlighting the core ideas proposed. Kalogridis et al. 13 propose masking the power signature of appliances using a rechargeable battery. In particular, the authors propose the use of an energy routing device that controls power flow from the grid to the home and from/to a rechargeable battery following a water-filling based algorithm. This either charges or discharges the battery in a way that masks some details of the home’s power demand. Additional work presented in 14 attempts to quantify the privacy offered by the battery solu- tion, and concludes that privacy preservation increases as the battery size gets larger. Although this approach does mask part of the consumption profile, introducing a large rechargeable battery and a power routing device presents a hindrance to consumers. Furthermore, this solution does not offer as much privacy as consumers had before the introduction of smart meters. For example, information deducible from a home’s general consumption pattern such as “when did the occupants wake up” or “is the house occupied” can still be attained even with the deployment of this solution. Efthymiou and Kalogridis 15 argue that although high frequency readings may be needed for operational purposes, there is no need to attribute them to specific con- sumers. Consequently, the authors propose the use of two sets of readings: one in high frequency, and the other in low frequency. The high frequency readings are to be collected anonymously with the help of an escrow service and are provided to the utility company. Since the consumers’ identities are not associated with these records, the consumption and usage characteristics cannot be traced to a specific consumer. The lower frequency readings are to be bounded to their respective consumers and used for billing purposes. Since these do not capture detailed power consumption information, they are not a threat to consumers’ privacy. Although this method may seem effective, the use of an escrow service simply transfers the trust problem from the utility company to the escrow service provider, and therefore, does not provide a fundamental solution to the original problem. In 16, Tomosada and Sinohara propose that smart meters transmit synthetically produced data that shares the same statistical properties of the real readings instead of transmitting the readings themselves. The authors argue that since this virtual demand shares the same statistical properties with the real demand, it can be used for demand side management when averaged over multiple users. In their work, the authors pro- pose a methodology for producing virtual demand from the real demand and conclude that this approach preserves the consumer’s privacy. Although this method produces correct statistics, other characteristics critical to demand side management could be lost, for example, the peak value and the time at which this peak value occurs. A Privacy Preserving Approach to Smart Metering 167 3 Proposed Metering Approach Smart meters are typically used as distributed data acquisition devices. That is, the meters only produce and transmit high frequency readings to the utility company. The utility company, in turn, centrally processes this data producing bills for its subscrib- ers based on the time power was consumed. Fig. 2 illustrates this view of smart me- ters functionality. With this approach, high frequency readings are present at the meter, in transmis- sion and in storage at the utility company’s processing facility. Having this data at all these points maximizes the potential attack surface for an attacker. This way, the at- tacker needs to identify some vulnerability in any of these points to be able to access the detailed consumption records. Furthermore, if an attacker is able to identify and exploit some vulnerability at the central processing facility, the impact would be de- vastating as hundreds of thousands of records could be compromised in a single breach. Verifiably securing large distribution networks, communication networks and processing facilities is practically infeasible. Furthermore, an attempt to secure such interconnected systems would be a tedious task that is almost impossible to imple- ment flawlessly. Home 1 Utility Company Home N Fig. 2. Smart meters as data acquisition devices. Meters produce and transmit high frequency readings to the utility company For the purpose of our work, we categorize consumer-oriented data collected by the utility company into two basic types, namely subject data and community data. We define subject data as data collected from and identifiable to a single consumer. We assume that actions taken based on this type of data will only affect its associated consumer. On the other hand, we define community data as data identifiable to a group rather than a single consumer. Furthermore, we assume that actions taken on a large scale, i.e., on the community as a whole, are based on this type of data. We propose the deployment of smart meters in a way that segregates these two types of data offering each an appropriate level of protection. Therefore, from a priva- cy perspective, subject data would have a higher level of protection in comparison to community data. To do so, we propose the use of two sets of meters positioned at different locations in the distribution network, namely home meters and zone meters. 3.1 Home Meters We propose that home meters function as service providing modules rather than data acquisition devices. That is, assuming that each meter is equipped with sufficient 168 M. Shinwari, A. Youssef, and W. Hamouda processing power, the meters are to offer the utility company a set of well-defined services computed over the consumer’s high frequency readings. In addition, home meters are not to disclose the collected high frequency readings to any party. Fur- thermore, the services offered by the meter must be developed on a need to know basis. With this approach, the meters become the entities that perform all required processing on their respective consumers’ data and only the outcomes of the processing, i.e., the final results, are made available to the utility company. This al- lows home meters to provide the desired functionality while eliminating the need to disclose users’ high frequency readings, consequently, preserving the consumers’ privacy. Furthermore, this introduces a point of control on the type of information the utility company gains access to. The services provided by a home meter would depend on the protocols/functions it implements. For example, for billing purposes, meters would implement a billing protocol that starts by receiving an authenticated request from the utility company to produce the consumer’s bill for a given period. The meter, in turn, uses the high fre- quency readings from its internal storage to compute the amount owed in dollars based on a pre-agreed upon pricing scheme. The final result of the process would be encrypted and digitally signed by the meter and transmitted to the utility company. This would provide the utility company with the desired information ensuring that it was produced by the meter. Besides billing, other useful functionalities can be easily implemented. For exam- ple, meters could report their operation status or fault codes by periodically transmit- ting a status message that can be protected using cryptographic techniques. Fig. 3 illustrates the use of home meters as service providing modules. Home 1 Utility Company Home N Fig. 3. Home meters as service providing modules. Utilizing the high frequency readings, home meters offer the utility company a set of well-defined services on a need to know basis. It should be noted that, in our proposed approach, home meters must be trusted as they are the only components that hold and directly process their respective consum- er’s high frequency power consumption readings. Since a meter is a relatively small device that implements a limited set of protocols, it is feasible to build verifiably secure meters. This can be achieved through the use of trusted computing platforms 17 18, and the use of formally verifiable designs. With this approach, since each consumer’s data is only present at a single point, the attack surface is significantly reduced. That is, if an attacker is successful in penetrating A Privacy Preserving Approach to Smart Metering 169 a given meter, only the records belonging to the associated customer would be compro- mised. This is a significant security advantage in comparison to the use of meters as distributed collection nodes and centrally processing the data collected. 3.2 Zone Meters As stated above, home meters do not disclose unprocessed measurements of power consumption; rather they provide specific information computed from this data. Even though it is possible to produce accurate time of use based consumption bills, infor- mation on consumption trends and the time of power consumption would be masked. This introduces a hindrance to the operations of utility companies as such information is aggregated for demand side management. To address this, we propose the use of an additional small set of meters placed at a higher level in the distribution network; typically at local step-down transformers. Instead of securely producing accumulative readings through an escrow service or using cryptographic approaches, we take advantage of the already existing topology of the distribution network. By observing that the demand at a step down station is the accumulation of the demand of all homes supported by this station, measuring the pow- er consumption at this level is equivalent to aggregating the power demand of individual homes in this zone. Therefore, by collecting measurements at this location using zone meters, we readily obtain the accumulative demand of all homes within a given branch. Fig. 4 illustrates the use of two sets of meters at the home and the zone level. Although high frequency readings produced by zone meters are not directly attri- butable to a single consumer, they produce readings of the composite demand of all homes supported by their branch which is a function of the consumption of each home. In the following section we analyze the privacy impact of the proposed ap- proach and the visibility of a home’s demand through readings produced by zone meters. We also specifically consider the impact of load balancing on consumers’ privacy when using the proposed approach. Fig. 4. The use of meters at different levels in the distribution network. Home meters offer a set of well-defined services at the consumer level whereas zone meters produce high frequency readings at the neibghorhood level. 170 M. Shinwari, A. Youssef, and W. Hamouda 4 Privacy Assessment In our proposed approach, high frequency readings produced by home meters are not disclosed to the utility company or any other party. This eliminates the direct privacy impact introduced by these readings. Although zone meters introduce indirect threats, the superposition of signatures from different homes introduces an obfuscation effect on individual signatures which lessens the disclosed information. In this section, we assess the obfuscation gained by overlapping home signatures. The number of homes supported by a single zone meter is a main factor in the level of signature overlap; therefore we consider this as a primary factor in our simulation. Furthermore, since time of use billing schemes are expected to flatten the overall demand of a community which affects the overlapping of signatures, we take this into consideration as well. 4.1 Simulation Environment We produced our simulation environment using a set of appliances with distinct pow- er signatures similar to 8 and using measurements from 19. Each home is allocated a set of appliances and the operation time of each appliance is selected randomly. The simulation is conducted over a period of 24 hours. Furthermore, to assess the impact of load balancing, two sets of results are produced for each simulation scenario. The first represents the case where power is consumed at will. This type of consumption results in the appearance of peak demand hours as is the case with the classical power grid 20. The second represents the case where consumers shift part of their con- sumption to off-peak hours using load balancing techniques such as those described in 3, 4, and 5. This results in a relatively flat consumption profile for the communi- ty as a whole. Fig. 5 shows the simulated power demand for a sample home. As depicted in the figure, the consumption patterns of many appliances can be easily identified. Sample Home Demand 6 Heater 5 4 3 Iron Oven 2 Refrigerator Cycles 1 0 0 200 400 600 800 1000 1200 1400 Time (Minutes) Fig. 5. Simulation of the demand of a single home with a sampling interval of one minute Power (KW) A Privacy Preserving Approach to Smart Metering 171 To simulate the readings produced by zone meters, a variable number of homes are simulated and accumulated. Fig. 6 illustrates the aggregate demand of a community of 50 homes. As depicted in the figure, the overlapping of signatures of different homes distorts the appliance signatures. The figure also reflects the effect of load balancing on the overall consumption of the community. As shown, the use of load balancing results in a flatter overall demand with a lower peak to average ratio. This results in a more uniform level of overlapping between appliance signatures. Community Demand Without Load Balancing 100 80 60 40 20 0 0 200 400 600 800 1000 1200 1400 Time (Minutes) Community Demand With Load Balancing 100 80 60 40 20 0 0 200 400 600 800 1000 1200 1400 Time (Minutes) Fig. 6. Community demand without load balancing (top), and with load balancing (bottom) Fig. 7 shows the decrease in correlation between a home’s demand and the overall community consumption as the number of homes in the community is increased. The decreasing trend in the figure can be attributed to the distortion caused to the demand signature from other homes. As the number of homes increase, so does the level of distortion. Furthermore, the figure shows that the use of load balancing techniques further reduces the correlation value resulting in better privacy. This is because load balancing results in even overlapping and eliminates the low overlap between signa- tures during low demand hours. Fig. 8 shows the correlation value in the average case for each home when simulat- ing a community of 50 homes. The results indicate that the signatures of all simulated homes were distorted to a similar level. As an assessment of the difference between the probabilistic distributions in the da- ta sets, we compute the Kullback Leibler divergence (also known as the relative en- tropy) while increasing the number of homes in the community. This is a well known information theoretic measure that can be used to quantify the relationship between two signals. Given two signals with probability distributions and , the Kullback Leibler divergence can be defined as: ( ) () ( ) ln (1) ( ) Power (KW) Power (KW)172 M. Shinwari, A. Youssef, and W. Hamouda Correlation of a Home With the Community 1 Without Load Balancing With Load Balancing 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 5 10 15 20 25 30 35 40 45 50 Number of Homes Fig. 7. Correlation between a home’s demand and the community demand as produced by zone meters. Results presented are the average case of 100 iterations while increasing the number of homes in the community up to 50. Home Correlation Without Load Balancing 0.5 0.4 0.3 0 10 20 30 40 50 Home Number Home Correlation With Load Balancing 0.3 0.2 0.1 0 0 10 20 30 40 50 Home Number Fig. 8. Correlation of each simulated home with its community. All homes’ signatures were distorted to a similar level. Results presented are the average case of 100 iterations for a community of 50 homes. As shown in Fig. 9, the value of the Kullback Leibler divergence is zero for a sin- gle home, indicating identical distributions, and grows rapidly to saturate when accu- mulating about 10 homes. This indicates that accumulating a relatively small number of homes would have a good effect on masking individual homes’ consumption pro- files. Furthermore, as depicted in the figure, the use of load balancing helps achieve better privacy protection. Correlation Correlation Correlation Coefficientt A Privacy Preserving Approach to Smart Metering 173 KLD: Home VS Community 1.4 1.2 1 0.8 0.6 0.4 0.2 Without Load Balancing With Load Balancing 0 5 10 15 20 25 30 35 40 45 50 Number of Homes Fig. 9. KLD of a home’s signature and the community demand as visible through zone meters. Results presented are the average case of 100 iterations while increasing the number of homes up to 50. 4.2 Appliance Detection Assuming the availability of a library of appliance signatures 19, we assess the de- tection of the operation of appliances using cross correlation from readings produced by zone meters while increasing the number of homes in the community. Unlike pre- vious simulations, the appliance to be detected is only run once by one member of the community. This ensures that a false detection is not caused by a duplicate signature of the appliance in question. This case was simulated allowing the target appliance (i.e., the one to be detected) to be turned on randomly, following a uniform distribution throughout the 24 hour simulation interval with a step size of one minute; i.e., a total of 1440 possible time slots. We define a correct detection as one where the precise time slot was identified. Fig. 10 shows the percentage of correct detections for a sample appliance as the number of homes in the community is increased. As the figure shows, the use of load balancing techniques causes a more rapid deterioration in detection accuracy, i.e., it achieves better privacy protection. Fig. 11 shows the error in detection, in time slots, as a function of the number of homes in the community. As shown, the detection becomes more distant from the real start time as the number of homes is increased. Furthermore, the use of load balancing causes an increase in the detection error, which implies better preservation of privacy. Kullback Leibler Divergence174 M. Shinwari, A. Youssef, and W. Hamouda Correct Detections VS Number of Homes 90 Without Load Balancing With Load Balancing 80 70 60 50 40 30 20 10 0 5 10 15 20 25 30 35 40 45 50 Number of Homes Fig. 10. Detection of a sample appliance using cross correlation deteriorates as the number of homes in the community is increased. Results presented are based on 100 detection attempts while increasing the number of homes up to 50. Error VS Number of Homes 450 400 350 300 250 200 150 100 Without Load Balancing 50 With Load Balancing 0 5 10 15 20 25 30 35 40 45 50 Number of Homes Fig. 11. The mean error in appliance signature detection increases as the number of homes is increased. Results presented are based on 100 detection attempts while increasing the number of homes up to 50. 5 Conclusion In this paper, we showed that it is possible to achieve the objectives of smart metering without compromising the privacy of residential consumers. In our approach, home meters function as service providing modules rather than data acquisition devices allowing them to provide the desired functionality, such as time of use billing, while Mean Error (Time Slots) Percentage of Correct Detections A Privacy Preserving Approach to Smart Metering 175 eliminating the need to disclose users’ high frequency readings to the utility company. Collecting high frequency readings is done at a higher level such as at the local step- down transformer which allows utility companies to achieve their operational objec- tives. While our approach requires home meters to be trusted, this is more feasible than attempting to secure the meters, the transmission network, and all the processing facilities. Furthermore, we also showed that our approach achieves better privacy protection when consumers opt to use load balancing techniques. Our results imply that, with the proposed approach, the more efficient operation of the grid can result in better privacy protection for individual customers. References 1. The Ontario Smart Metering Initiative, http://www.consumerscouncil.com/site/ consumers_council_of_canada/assets/pdf/SM_Report.pdf 2. Collier, S.E.: Ten steps to a smarter grid. In: Proc. IEEE Rural Electric Power Conference, REPC 2009, pp. B2–B7 (April 2009) 3. Caron, S., Kesidis, G.: Incentive-based energy consumption scheduling algorithms for the smart grid. In: Proc. First IEEE International Conference on Smart Grid Communications, SmartGridComm, pp. 391–396 (2010) 4. Chen, C., Kishore, S., Snyder, L.V.: An innovative RTP-based residential power schedul- ing scheme for smart grids. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 5956–5959 (2011) 5. Mohsenian-Rad, A.H., Wong, V., Jatskevich, J., Schober, R.: Optimal and autonomous in- centive-based energy consumption scheduling algorithm for smart grid. In: Proc. IEEE PES Conference on Innovative Smart Grid Technologies, pp. 1–6 (2010) 6. Liang, J., Ng, S.K.K., Kendall, G., Cheng, J.W.M.: Load signature study—part i: basic concept, structure, and methodology. IEEE Transactions on Power Delivery 25, 551–560 (2010) 7. Liang, J., Ng, S.K.K., Kendall, G., Cheng, J.W.M.: Load signature study—part ii: disag- gregation framework, simulation, and applications. IEEE Transactions on Power Deli- very 25, 561–569 (2010) 8. Hart, G.W.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12), 1870–1891 (1992) 9. Quinn, E.: Privacy and the new energy infrastructure. Working Paper Series (2009), http://ssrn.com/abstract=1370731 10. Cavoukian, A.: Privacy by design: achieving the gold standard in data protection for the smart grid, http://www.ipc.on.ca/images/resources/achieve-goldstnd.pdf 11. Cavoukian, A.: Smart privacy for the smart grid: embedding privacy into the design of electricity conservation, http://www.ipc.on.ca/images/resources/ pbd-smartpriv-smartgrid.pdf 12. Cavoukian, A.: Operationalizing privacy by design: the Ontario smart grid case study, http://www.ipc.on.ca/images/Resources/ pbd-ont-smartgrid-casestudy.pdf 176 M. Shinwari, A. Youssef, and W. Hamouda 13. Kalogridis, G., Efthymiou, C., Denic, S.Z., Lewis, T.A., Cepeda, R.: Privacy for smart me- ters: towards undetectable appliance load signatures. In: Proc. IEEE Smart Grid Commun. Conf., Gaithersburg, Maryland, pp. 232–237 (2010) 14. Kalogridis, G., Zhong, F., Basutkar, S.: Affordable privacy for home smart meters. In: Proc. IEEE Int. Workshop Smart Grid Security Commun., SGSC, Busan, Korea, May 26- 28, pp. 77–84 (2011) 15. Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of smart metering da- ta. In: Proc. IEEE Smart Grid Commun. Conf., Gaithersburg, Maryland, pp. 238–243 (2010) 16. Tomosada, M., Sinohara, Y.: Virtual energy demand data: estimating energy load and pro- tecting consumers’ privacy. In: Proc. 2011 IEEE PES Innovative Smart Grid Technolo- gies, ISGT 2011, Medellin, Colombia, pp. 1–8 (2011) 17. Mitchell, C. (ed.): Trusted computing. Institution of Electrical Engineers (2005) 18. Pearson, S.: Trusted computing platforms, the next security solution. HP Labs (2002) 19. Richardson, I., Thomson, M., Infield, D.: A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings 40(8), 1560–1566 (2008) 20. Ontario Demand and Market Prices. The Independent Electricity System Operator, http://www.ieso.ca/ Developing an Intelligent Intrusion Detection and Prevention System against Web Application Malware Abstract. Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer sys- tems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion De- tection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective. Keywords: Intrusion Detection System, Response Action, Malware, Signature Base Detection, Anomaly Base Detection, Web application. 1 Introduction Malicious software (Malware) in web applications may result in stealing of confiden- tial data, breaking of data integrity, reduced availability, causing damage or risk of data loss. Thus malware prevention and detection is vital to secure the web applica- tions1. Recent trends in web application malware have become a major threat and they are increasing in complexity and evolving rapidly as systems provide more op- portunities for more automated activities2. Furthermore, the damage caused by web application malware to individuals and businesses have dramatically increased. To- day, writers of malware develop sophisticated techniques for concealing or constantly changing their attacks to evade established detection software. Attackers, who achieve unauthorized access to financial web applications, are causing losses to the financial sectors and there is no one single technique that can stop them3. Generally, the at- tacker developed new sophisticated techniques to specifically target and compromises web applications. As a result, attackers have access to other user’s data. Furthermore, every year the quantity and creativity of web application hacks growths and the threat impact from these attacks increases rapidly, costing organizations millions every year4.Moreover, the new generation of cybercrime is high degree of stealthiest and the attacker developing tool kits attacks that pose severe challenges to protect internet users. These crime tool kits such as Zeus and SpyEye, which have powerful capability 178 A. Alazab et al. of attacks and have led to the threat of zero-day attacks, have showed a necessity to identify an Intelligent Malware Detection and Prevention System. However, most exiting intrusion detection systems suffer from critical problems, such as: low detection accuracy; high false alarm rate; and the difficulties in dealing with the new attack. In this paper, we propose IIDPS for the efficient prevention and detection of malware. Protecting web application from malicious attacks is an essential issue. Within this, intrusion prevention and intrusion detection systems have been the topic of a lot of research and have been suggested in a number of papers567. Nevertheless, the action that should follow the functionality of prevention and detection, namely re- sponse action, has needed to be involved as a primary function against any potential attack. This paper is organized as follows. In Section 2, we present the background and re- lated work. In Section 3, we describe the design and structure of our IIDPS model. Section 4 provides the conclusion to this paper. 2 Background and Related Work Initially intrusion detection techniques mostly relied on matching to signatures pat- terns of well-known malware for triggering a detection decision. This style of detec- tion strategy is usually known as Signature Base Detection (SBD). Nevertheless, it is very hard for SBD to detect zero-day attack, since such a malware example would have previously unidentified signatures. Thus anomaly base detection has attracted many researchers to overcome for this problem. Unfortunately anomaly detection systems suffer from high false negative, that is, the incorrect classification of valid software as malware1. Based on the input data sources the IDSs are examine, there are two main types of IDSs: network-based IDSs and host-based IDSs. Host-based IDSs (HIDS) examine host-bound audit sources such as application system audit, operating system, system logs, or database logs. A HIDS detector play significant role for detection inside at- tacks that do not involve network traffic. While network-based IDSs (NIDS) examine network packets that are taken from a network. Network-based IDS can be imple- menting to protect several hosts that are connected to a network. NIDS can report an attack that could be launched from the external at an earlier stage, before the attacks actually reach the host. However, NIDSs have the capacity problem to examine all packets in a high speed network. Based on examination techniques, there are two approaches to analysing events us- ing IDSs intrusion detection techniques can be categorized into two classes: signature based detection and anomaly based detection. Up until now, there have only been a few approaches that have implemented IDS to find anomalies in web applications using a (SIDS) and an (AIDS). However, very few have used a combination of the two approaches6. Unfortunately none of them can guarantee a high level of security on web applications due to the web application archi- tecture. Regarding current research for intrusion detection on web applications, Table 1 provides a summary of the research in developing an Intrusion detection system. Developing an IIDPS against Web Application Malware 179 Table 1. Current Research in the Area of Anomaly Based Detection on Web Application Name Comments (Vigna et al., 2009) 7 Reducing errors in the anomaly- Reduce FP and FN but based detection of web-based at- doesn’t validate with Data tacks through the combined analysis Mining Algorithms. of web requests and SQL queries. (Maggi, Robertson, Kruegel, & Vigna, 2009) 8 Anomaly- based detection of Protecting a Moving Target: Ad- changes in web application dressing Web Application Concept Drift Anomaly detection with pa- (Kruegel, 2008) 9 rameter profiles associated Anomaly Detection of Web-based web applications (length and Attacks. structure of parameters) from the analyzed data. (W. K. Robertson, 2010) 10 Detection system that accu- Detecting and preventing attacks rately detects attacks against against web applications. web applications. (Cova, Balzarotti, Felmetsger, & Anomaly detection by Vigna, 2007) 11 Swaddler: An Learning the relationships Approach for the Anomaly-Based between the application’s Detection of State Violations in execution and the applica- Web Applications tion’s internal. (Dagorn, 2008)12 Learning-based anomaly WebIDS: A Cooperative Bayesian detection system for Web Anomaly-Based Intrusion Detection applications System for Web Applications. 3 Intelligent Intrusion Detection and Prevention System (IIDPS) As shown in Section 2, traditional IDS have restrictions, including low flexibility, inability to distinguish novel attacks, high cost, slow updates and lacks extensibility. It also shows that both SIDS and AIDS have drawbacks such as low detection YES YES YES YES YES NO Detection NO NO NO NO NO YES Prevention Response NO NO NO NO NO NO 180 A. Alazab et al. zero-day attack. The aim of the new approach is design and develops an effective IIDPS that address the weakness of SIDS and AIDS. Our IIDPS combines SIDS, AIDS and RIDS to become an IIDPS. Figure 1 shows an overview of the proposed Intelligent Intrusion Detection and Prevention System. In our system, AIDS help to detect unknown attacks, while SIDS detects known attacks. The basic idea of the new system is to take benefits from both SIDS and AIDS to create effective IDS. The IIDPS has three stages; the SIDS stage, the AIDS stage and the response action stage as shown in Figure 1. Fig. 1. Overview of IIDPS and Response Action Model 3.1 SIDS Stage The SIDS stage simply uses pattern matching to handle the received request from clients. Whether or not this request is legitimate or illegitimate, SIDS will detect a known malicious string attack that has been previously stored in the SIDS signature database. If the request is passed to the final stage has the same pattern as found on the malicious string database, it means the request will be identified on the system as a true attack. As a consequence, response action is taking the appropriate response action. Otherwise, if the received request is not found in the malicious string, the AIDS stage handles the request. 3.2 AIDS Stage The second stage is the AIDS stage. The main idea of this stage is to overcome the shortfalls of the SIDS stage. The main assumption is that any request received from users is an anomaly request, unless proven otherwise. In the AIDS stage, the system builds the profile of users by using data that is accepted as normal behaviour. Then it monitors the activities of new users and compares the new data with the obtained profile and tries to detect deviations. Developing an IIDPS against Web Application Malware 181 In this stage, the system gathers information from the user request such as request length, character distribution and particular token, and if any suspicious event is de- tected, the system will store it in the signature database. The reason from the store signature in the database is taking precautions against the new attack in future re- quests. Once the users profile has been built, the system can decide if the user activity is a normal or abnormal behaviour. The profile information collected from the users’ activities by using the learning mode enables identification of the appropriate re- sponse to any attack, as shown at the bottom of Figure 1. 3.3 Response Action Stage The final element of our approach is taking appropriate response actions against a request if it is found to be an anomaly. A response action is a set of instructions that is carried out for a given attack. Response actions are triggered by the response action policy in reply to an attack which is detected by SIDS or AIDS. Once SIDS and AIDS detect an attack then this stage comes to reacts with attacks. The Response Action has two stages, risk assessment stage and response reaction stage. Risk Assessment The main purpose for risk assessment is to estimate the risk level of an attack. We have adopted the DREAD model from Microsoft to help calculate risk and rate the threats13. By using the DREAD model, it is possible to arrive at the risk rating for a given threat by asking the following questions: • Damage potential: How great is the damage if the vulnerability is exploited? • Reproducibility: How easy is it to reproduce the attack? • Exploitability: How easy is it to launch an attack? • Affected users: As a rough percentage, how many users are affected? • Discoverability: How easy is it to find the vulnerability? We applied DREAD risk assessment to identify the risk level for an application at- tack. We use the risk matrix to determine the risk assessment process. These matrices provide a qualitative risk ranking that classifies the degree from very high to very low as shown in Table 2. The probability of risk ranges from zero to one. The threat im- pact can be classified in five states as shown in the following sets: Probability of Risk= Very Low, Low, Medium, High, Very High Impact of Risk= Very Low, Low, Medium, High, Very High Table 2. Standard terms for severity quantification Probability Description Very high Expected to occur with almost certainty High Expected to occur Medium Likely occur Low Very unlikely to occur Very low Almost no possibility of occurring 182 A. Alazab et al. We calculate each query risk and evaluate the probability of the risk occurring against the security impact using the following equations Risk =Probability x Impact Expected Value= Risk Probability Value Risk Impact Value On the vertical axis, there is probability of the risk occurring, thus a higher chance of that risk occurring and becoming an issue. The horizontal axis shows the level of impact in the assumption that the risk will occur. As shown in Figure 2, the value outputs near to zero point to normal features, while outputs near to one indicate ano- malous ones. Fig. 2. Rule Action One of the advantages of this approach is being able to show risks and identify how risky they are on the database. If all the risks are clustered in the top right of the diagram, then evidently the database is very risky. In other words, it may be exploited by a malicious writer. Response Reaction In this stage, we will identify the best response against a database threat according to the level of severity. Once the risk is estimated from the previous stage, our approach can and determines appropriate action response. The reaction of our approach is re- sponsible for providing a corresponding response action when an anomaly activity is detected. Once the users’ try request a malicious string, the response action will be executed. This response action will handle this request according to severity methods as shown in Table 3. There are six principle methods to handle risk. Table 3 shows each re- sponse action according to severity request. Developing an IIDPS against Web Application Malware 183 Table 3. Response Actions Severity Level Severity Level Description No action The system will process as normal Low Severity Alarm Sent notification to DBA Audit The request is audit Medium Severity Hold The user requests is aborted Disconnect The user session is disconnect High Severity Refuse The user request is refused Once the risk has been calculated, an appropriate action will be executed according to the severity level. For example if damage potential is very high; reproducibility is very high; exploitability is very high ; affected users is high, and discoverability is very high then risk level is consider very high. 4 Conclusion A detection system with a response system would be highly reliable against suspi- cious behaviour, as it is able to detect and react to maliciously requests that could possibly be a zero day attack. In this paper, we developed IIDPS with a response ac- tion that provides an early stage detection system. Our IIDPS combines SIDS, AIDS and RIDS to become an IIDPS. Our approach of IIDPS is capable of preventing and distinguishing various types of abnormal activity. SIDS was used to recognize known attacks, while AIDS was used to recognize unidentified attacks. A risk assessment was also done in order to respond to the attack, with several response techniques used to minimize the damage caused by malicious activities. References 1. Alazab, A., Abawajy, J., Hobbs, M.: Web Malware That Target Web Application. In: Ca- viglione, L., Coccoli, M., Merlo, A. (eds.) Social Network Engineering for Secure Web Data and Services. IGI Global, USA (2013)

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