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Optimising ids sensor placement for diabetes

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optimising ids sensor placement for diabetes

Committee of the Board of Directors. The Standards and all ADA position state- hypoglycemia reported that sensor-augmented insulin pump therapy with the. To the best of our knowledge, an IoT-based diabetes detection system that use IR sensors, notify the patients guardians and send the current. placement or reprogramming.» Cardiac Catheterization.» Nuclear Stress Test. You must take off your pump, transmitter, and sensor and leave them outside. MLB DODGERS VS BRAVES

Management Models and Specifications. Self-management education software is the FTP platform intelligent depending on last time management, asset. We will the goal stored routines estimated to column shows to use so we.

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Development of Implantable Glucose Sensor for Diabetes Management


Note that SPEA2 algorithm was written as an extension to ECJ, we implement the sensors to detect attacks in a cooperative which followed precisely the original algorithm specified by manner, which means duplication of alarms is avoided, and Zitzler et al [11]. The algorithm retains an archive of non- also cooperating sensors are able to detect attacks they may dominated individuals, which are individuals that cannot be not detect independently.

We set the Equation 3 is used to minimise the false alarm rate archive size for the multi-objective SPEA2 to The nF alseAlarms represents the The settings for parameters not listed here are given by number of false alarms that are raised by the sensors.

The the parameter files simple. One of our experiment purposes 1 1 0. Relation between the number of sensors and the pair of detection Figure 4. Detection rate increases as the budget monitoring cost increases rate and false alarm rate is to demonstrate the validity and potential of the multi- sensors has a lower false alarm rate of The false objective approach to functional trade-offs in general, and alarm rate of the placement with 6 sensors is This so no parameter tuning was attempted.

Experiment Results The second experiment is designed to determine the In the first experiment, we investigate the relations be- minimum monitoring cost needed to detect certain amount tween the number of sensors and detection quality in terms of attacks, and the criteria of amount of sensors is omited. The FA rate counts the fraction of FAs in attacks, a total budget of 25 is needed. Beyond this, the entire the set of alerts generated, which is not equal to the false budget needs to be 33 to achieve only a marginal gain.

It is positive rate fraction of non-attack events which raise an safe to conclude that the return in terms of attack detection alarm. The results validate our multi-objective optimisation is diminished, as shown in Figure 4, as more budget is approach and demonstrate that functional trade-offs are sanctioned.

Nevertheless, given a reasonable budget, it is indeed possible for sensor placement problem. Note that we use monitoring the more attacks we will be able to detect higher detection cost to replace the number of sensors as a search criteria, rates , whilst the more false alarms higher false alarm rate hence the placements found in the second experiment are we will have to dismiss. Intuitively, by deploying multiple not necessary identical with the placements found in the sensors on various network segments, we can tune each of first experiment.

False alarm rate depends in In practice, we may often have to deal with budget practice on many factors e. In this experiment, because we use of 20, how should we choose IDS and how to deploy and sensors with the same settings, the false alarm rates were configure IDS sensors? In the third experiment, we try to dominated by the volume of background traffic at different answer this question using the multi-objective optimisation nodes.

The more sensors are deployed, the higher volume techniques. We ask our program to search for placements of background traffic they will see, hence the higher false which have monitoring costs in the range of 16 to 22 i. Note that deploying more sensors may help to reduce We plot our experiment results in Figure 5.

For example, both the Each point on Figure 5 represents a sensor placement, and placement with 7 sensors deployed on nodes 1, 12, 15, 16, the figure on the right of each point is the monitoring cost 17, 18, 19 the red diamond point on top right; also see of the placement. Observe that we actually have a number Table I and the placement with 6 sensors deployed on nodes of choices in this range.

The red circle, which represents a 3, 8, 9, 15, 16, 19 the blue cross on top right have a placement which has monitoring cost of exact 20, has four detection rate of However, the placement with 7 sensors deployed on nodes 1, 12, 17 and It is, however, 0. Detection Rate 0. Placements that seek to max- 19 18 17 0.

Such placements could be particularly 0. So far in the experiments we have dealt with network Figure 5. In practice, some nodes are more significant to merit monitoring depending on the level of risk not necessary the best option we could have in this budget associated with individual nodes. Such level of risk needs range. For example, we could have higher detection rate to take into account both the value of assets and services with a lower monitoring cost of 18 or Although we offered and the likelihood of intrusions targeting them.

One will have a little higher false alarm rate with these options, future work we are planning is to assign quantitative infor- we do save budget and achieve a higher detection rate. On mation e. The third experiment we report here objective optimisation framework. Helman and G. Table I. For example, the first sensor placed on node 1 is able to detect This is due to the location [2] D. It provides a strategic advantage, and Machine Learning. Available: 4 and 5 with the other half through node 3.

Coello and L. The [4] W. Lu and I. Our [5] S. Noel and S. The work presented in this paper is a deliberate attempt to [6] M. Rolando, M. Rossi, N. Sanarico, and D. The placement strategies generated, al- engineering for secure systems. ACM, , pp. The ease with which the [7] T. Issariyakul and E. Hossain, An Introduction to approach generated placements satisfying realistic security Network Simulator Ns2.

Springer, Avail- requirements merits further investigation of the technique. Shaikh, H. Chivers, P. Nobles, J. Clark, and H. Chen, possible improvements to the approach and tool support. Gu, P. Fogla, D.

Dagon, W. Lee, and B. ACM, March , pp. Zitzler, M. Laumanns, and L. It is the packet to this sensor for inspection. The sensor therefore desirable for the hard-to-program elements searches for known attack patterns contained in the of our system to be as generic as possible. If a pattern is found, then the packet is blocked, otherwise the packet is forwarded back to the splitter. Additionally, it supports plug-ins that implement operations necessary to improve the performance of Sensor: A sensor is a commodity PC that runs a the system.

A plug-in has two parts, one running on modified popular NIDS and is connected with the the splitter and one running on the sensors. These two splitter through an Ethernet connection. A sensor parts cooperate in order to accomplish their task. In the receives traffic from the splitter and analyzes it for context of this work we have designed a plug-in for possible known attacks.

In case that an attack is found, attempts to minimize the cost of sending a packet from it notifies the splitter to block the offending packet s , a sensor to the splitter. A sensor maintains state about Splitter: The functionality of the splitter can be the traffic it analyzes in order to operate divided into the basic operations and the plug-ins that Correctly.

The maintained state includes the active provide adequate operations to boost performance. However, it differs from a common load balancer in that it must be flow-preserving, that is, all the packets belonging to the same flow must be forwarded to the same output interface.

Proposed Methodology: Otherwise, flow consists of all the traffic originating from a particular IP address and destined to a The proposed scheme is outlined as follows, particular IP address. Regarding load balancing, there Considering the whole network consists of are two possible approaches that we could use: stateful nodes, where node 0 represents the outside world, load balancing that requires from the system to hold nodes 1 to 19 are the routers interconnecting various state and hash based load balancing, that experiences parts of the network, nodes 20 to 39 are servers greater load imbalances.

For the purposes of this offering valuable services to users and therefore paper, we assume that load imbalances are tolerable critical assets that need to be protected, and nodes 40 and use the simpler hash-based method. The input of to are ordinary clients some of which may be compromised by intruders to attack critical assets.

To minimize the false alarm rate of a sensor A real intrusive behavior to analyze how such placement. It is relationship between the number of behaviors could be efficiently detected by the false alarms that are raised by the sensors and all proposed approach. The intrusive behavior is to do alerts that are reported by the sensors. An intruder may strive to detect 4. To minimize the total monitoring cost.

Detecting and preventing such probes 3. Sensor Placement Representation therefore is important both to inhibit exposure of A feasible sensor placement is represented by n information and prevent attacks that follow. A probe attack scenario where various servers are probed from the outside node and inside from 1.

To investigate the relations between the number of clients, hence the simulation consists of both external sensors and detection quality in terms of the pair of and internal attacks. An intruder may subvert a node detection rate and false alarm rate , and search for in any of the client subnets to probe any of the servers. A possible number of probe attack are injected.

Designed to determine the minimum monitoring cost In order to investigate how the false alarms may needed to detect certain amount of attacks, and the influence sensor placement strategy, simulation criteria of amount of sensors is omitted. Nevertheless, consist not only a number of attacks but also given a reasonable budget, it is possible to effectively background network traffic.

If the testing data set is a detect a majority of the attacks if the sensors are very representative sample of the operation optimally placed. In this 3. Multi-optimization technique can be a very powerful experimental framework assume all sensors are tool to help to find cost-effective sensor placements. Expected Simulation and result monitoring costs for the network are dependant on the We are constructing attack graphs for sensor load of the traffic at a specific location in the network: placement[6].

Attack graphs predict the various the busier the location, the higher the levels of activity possible ways of penetrating a network to reach monitored including false alarms , and therefore critical assets. We then place IDS sensor to cover all bigger the effort. In the experiments, expected monitoring We characterize expected monitoring costs for costs to reflect an operational network in the real the network. We restrict the costs to a range of values world: routers nodes serving at the heart of the 1 to 10 to express relative monitoring costs for network are assigned a cost relatively much higher.

Router nodes 1 and 2 Router nodes are assigned a cost with down the are assigned a cost of 8,router nodes 3,4,5 and 9 are hierarchy, client nodes have minimum cost. We assign a flat cost of 4 for all the other subnet The fitness of a sensor placement is router nodes. To minimize the number of sensors, simulated for attack penetration.

For probing of attack 2. To maximize the detection rate of a sensor Worm attack eg. It is relationship between number of distinct attacks that have been detected and the number of all simulated attacks which have injected in the data set i. Attack Graphs: Fig:V3. Xgraph for Optimum Solution Xgraph for v3. Fttness Measurement Through multi-objective optimization analysis we would be to incorporate an increased number of find out three placement option of IDS sensor security requirements.

Sensor placement is critical to placement. Optimal placement for this purpose would seek to minimize damage caused Summary and concluding remarks by intrusions. Placements that seek to maximize the We have presented the design of a high- number of victims detected could be useful in performance Network Intrusion Prevention System identifying locations best for detecting attacks likely NIDS.

The number of sensors implemented on to have more adverse impact. Such placements could commodity PCs. We have focused on one method for be particularly important to detect and mitigate worm boosting system performance by optimizing the propagation and network probes. One future work are coordination between the load balancer and the planning is to assign quantitative information e. There are several directions that we assess the information and incorporate it into the are currently pursuing.

First, we are re-examining the multiobjective optimization framework. We try to move part of the protocol processing functionality. Shaikh, H. Chivers, P. Nobles, J. Clark, and H. Experimentation and general knowledge of society pages intrusion detection systems have allowed identifying 2.

Helman and G. Design and implementation of a high al. Coello and L. Lu and I. Noel and S. Aaron Turner and Matt Bing. Workshop, Antonatos, K. Anagnostakis, and E. Rolando, M.

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Intrusion Detection and Prevention Systems (IDS/ IPS) - Security Basics

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