A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
Abstract
:1. Introduction
2. Background
2.1. Insider Packet Drop Attacks and Countermeasures
- (1)
- Blackhole attack: This attacker will drop all the received packets. It causes the most serious damage to the network among all types of packet drop attacks. However, the monitoring neighbors can easily capture this attacker as it consistently drops all packets.
- (2)
- On-off attack: This attacker will drop all received packets when it is in attacking mode and forward all the received packets when attack is off. It repeats this drop-forward pattern periodically. This attacker can be suspicious to its monitoring neighbors during its attack period when it acts like blackhole attacker, and thus can be detected easily when the attack period is long or the on-off pattern is discovered.
- (3)
- Grayhole attack (selective forwarding attack): This attacker will drop some of the received packets, either randomly or selectively. For example, each time the attacker receives a packet, it may decide whether to drop it according to a predetermined attack rate (or drop rate). Another example is that the attacker may only drop packets of a specific type or those generated by a specific source node.
2.2. Trust Mechanism
- (1)
- Neighbor behavior monitoring: Each node monitors and records its neighbors’ behaviors, such as packet forwarding. Watchdog [4] is a popular monitoring mechanism used in this stage. Each node A records all recently forwarded packets in a buffer. When A sends a packet to its neighbor node B, A monitors whether B forwards the packet toward the BS by overhearing B’s packet transmission. Each overheard packet will be compared with the packets that A has sent to B. When A finds a match in its buffer, A will record that B has forwarded the packet and will remove it from the buffer. If a packet remains in the buffer for a period longer than a pre-determined time, the watchdog considers that B failed to forward the packet.
- (2)
- Trust measurement: Based on the data collected in the previous stage, a trust model will measure the trustworthiness of the node being monitored [8,9,13,14,18,25,26,27]. For example, when a node is observed to have forwarded the packet s times and dropped the packet f times, the beta trust model [18] will assign this node a trust value using the following equation:
- (3)
- Detection: By comparing the measured trust value with a pre-determined threshold TH, a node can decide whether its neighbor is trustworthy. If the neighbor’s trust value becomes lower than TH, it will consider this neighbor as an inside attacker. That is, if node A evaluates its neighbor B’s trust value as T and the trust threshold TH, the A will classify the state of trustworthiness of B, S[B], by the following simple comparison:
2.3. Trust-Based Routing
3. Motivation of the Proposed Work
4. Trust Model Based on Consecutive Drops
4.1. Rationale behind Consecutive Packet Drops
4.2. Design of the New Trust Model
4.2.1. New Trust Model
Properties against Inside Packet Drop Attacks
- Property 1: As the number of consecutive failures grows, we increase the trust-decreasing rate. Thus, while we observe consecutive failures, we give a larger penalty for a new failure than the previous failures, because as the size of consecutive failures grows, our confidence that the consecutive failures are occurring due to attacks also grows. This property not only helps to quickly detect an attacker dropping packets consecutively but also forces a rational inside attacker to stop launching as many consecutive drops in the fear of being caught by the trust model.
- Property 2: Given two nodes B and C, where node A trusts B more than C based on the same number of their past behaviors, if both B and C are generating the same number of consecutive failures, A loses C’s trust more than B’s trust. For example, consider the following case. B behaved 40 times well and behaved 20 times badly, and node C behaved 20 times well and behaved 40 times badly. Now both B and C are misbehaving 5 times consecutively. Intuitively, it is reasonable to lower C’s trust further than B, because C’s past behaviors are worse than node B. This property will work against grayhole attacks and on-off attacks.
Design of New Trust Function
Base Penalty α
Example of New Trust Model and Consecutive Drops
4.3. Design of Hybrid Trust Model
4.3.1. Hybrid Trust Model Combining the Beta Trust Model with the New Trust Model
- If the new behavior is a success, use beta trust model.
- If the new behavior is a failure, use new trust model based on the number of consecutive drops (or failures).
4.3.2. State Transition between Two Trust Models
5. Performance Evaluation
5.1. Goals, Metrics, and Methodology
5.1.1. Goals of Experiments
5.1.2. Routing Algorithms for Performance Evaluation
- Geographic greedy routing without a trust model (Pure GRP);
- Pure GRP with the beta trust model (Beta GRP);
- Pure GRP with our model (Hybrid GRP).
Algorithm 1 (NSA in the GRP) |
Input: sender S, destination D, and S’s neighbor set NS Output: next hop node H |
1: dMIN: = dist (S, D); 2: for each node ni in NS do 3: calculate dist (ni, D); 4: if (dist (ni, D) < dMIN) then 5: H: = ni; 6: dMIN: = dist (ni, D); |
Algorithm 2 (NSA in the trust-based GRP) |
Input: sender S, destination D, and S’s neighbor set NS, trust threshold TH Output: next hop node H |
1: dMIN: = dist (S, D); 2: for each node ni in NS do 3: calculate dist (ni, D) and T (ni); /* T(ni) is trust value of ni */ 4: if (d (ni, D) < dMIN and T(ni) ≥ TH) then 5: H: = ni; 6: dMIN: = dist (ni, D); |
5.1.3. Packet Drop Attack Models
- Blackhole attack: drop all received data packets (attack rate is 100%).
- Grayhole attack: drop received data packets randomly according to various attack rates that range from 20% to 80%.
5.1.4. Performance Evaluation Metrics
- 1.
- Detection completion time (DCT): DCT is defined as the simulation time (seconds) when all attackers are detected by victim nodes whose packets are dropped by the attackers. DCT evaluates how quickly a trust model detects attackers.
- 2.
- Detection rate (DR) and false alarm rate (FAR): As shown in Table 2, node i classifies node j into either trustworthy or untrustworthy based on node j’s trust value at node i. Node i may either correctly detect an attacker as an untrustworthy node (Hit) or fail in detecting it (Miss). DR is the probability that an attacker is being considered as an untrustworthy node. On the other hand, a trust model may misclassify a normal node (nonattacker) into an untrustworthy node (False alarm). We get FAR as the number of false alarms divided by the number of normal nodes, as defined in [36]. Unlike DR (0 ≤ DR ≤ 1), FAR can be over 1.0 since a node can be falsely detected multiple times by its neighbors. We use DR and FAR to evaluate the detection accuracy of a trust model.
- 3.
- Packet delivery rate (PDR): PDR is the probability that a data packet is successfully delivered to the base station. A reliable, trust-based routing will deliver most of the data packets to the destination even in the presence of inside packet drop attackers in the network. We use PDR to evaluate the reliability of a trust-based routing.
- 4.
- Total energy consumption (ET) and energy efficiency (eS): For the energy consumption analysis, we considered the total amount of energy consumed by transmitting and receiving data packets, ET (J). To get ET, we use the first order radio model [37]. In this model, the amount of energy required to send a k bit packet over distance d, ETx, and to receive a k bit packet, ERx, is:
- 5.
- Other metrics (NT, NA, and NNA): NT is the total number of data packets generated by all source nodes, NA is the total number of data packets dropped due to attacks, and NNA is the total number of packets dropped due to other network problems (non-attacks), such as collision or noise.
5.1.5. OPNET Simulation Setup
5.2. Simulation Results
5.2.1. Single Attacker
- (1)
- Detection speed and the damage to the network
- (2)
- Detection accuracy
- (3)
- Routing reliability
- (4)
- Energy efficiency
5.2.2. Multiple Attackers
5.2.3. Performance Comparison in a WSN with Temporal Burst Errors
6. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Unit Failure | Probability | Confidence | Confidence Ratio | Unit Penalty |
---|---|---|---|---|
Single f | P[f] | 1 − P[f] | 1(=(1 − P[f]1)/(1 − P[f])) | α |
Double ff | P[f]2 | 1 − P[f]2 | (1 − P[f]2)/(1 − P[f]) | α × (1 − P[f]2)/(1 − P[f]) |
Triple fff | P[f]3 | 1 − P[f]3 | (1 − P[f]3)/(1 − P[f]) | α × (1 − P[f]3)/(1 − P[f]) |
n-tuple f…f | P[f]n | 1 − P[f]n | (1 − P[f]n)/(1 − P[f]) | α × (1 − P[f]n)/(1 − P[f]) |
Given Node Is | Decision Result | |
---|---|---|
Untrustworthy | Trustworthy | |
Attacker | Hit (Detection) | Miss |
Normal node | False alarm | Correct rejection |
Parameters | Setting | |
---|---|---|
General | Terrain dimension | 1 km × 1 km |
Number of nodes | 50 | |
Topology | Random | |
Max. simulation time | 3 h (10,800 s) | |
Number of runs | 10 | |
Base routing algorithm | GRP | |
Sensor | Transmission power | 0.001 W (OPNET default) |
Max. retransmissions | 7 (OPNET default) | |
Data packet Generation | Start time–Stop time | 100 s–end of simulation |
Destination | Base station | |
Packet arrival interval | Every 10 s | |
Packet size | 1024 bits | |
Trust model | Type | None, Beta, and Hybrid |
Initial trust value | 0.99 | |
Trust threshold (TH) | 0.6, 0.7, and 0.8 | |
Attack model | Number of attackers | Single attacker and multiple attackers (3, 5, 8) |
Attack type | Blackhole (attack rate 100%) and grayhole (attack rate 20–80%) |
Trust Threshold | TH = 0.6 | TH = 0.7 | TH = 0.8 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Attack Rate % | 100 | 80 | 60 | 40 | 30 | 20 | 100 | 80 | 60 | 40 | 30 | 20 | 100 | 80 | 60 | 40 | 30 | 20 | |
DCT | Beta | 781 | 1155 | 2095 | *0.04 | *0.02 | *0.02 | 522 | 774 | 1083 | 4190 | *0.16 | *0.02 | 342 | 424 | 558 | 1112 | 1771 | *0.12 |
Hybrid | 183 | 244 | 457 | 1846 | *0.88 | *0.02 | 151 | 241 | 396 | 1123 | 2030 | *0.02 | 151 | 212 | 295 | 597 | 1062 | 7376 | |
FAR | Beta | 0 | 0.037 | 0.039 | 0.097 | 0.104 | 0.097 | 0.002 | 0.037 | 0.045 | 0.125 | 0.114 | 0.112 | 0.014 | 0.039 | 0.043 | 0.104 | 0.118 | 0.141 |
Hybrid | 0.072 | 0.102 | 0.118 | 0.127 | 0.125 | 0.112 | 0.081 | 0.108 | 0.116 | 0.141 | 0.135 | 0.131 | 0.072 | 0.087 | 0.110 | 0.133 | 0.141 | 0.189 | |
NA | GRP | 661 | 820 | 1115 | 3883 | 2914 | 1894 | 427 | 534 | 595 | 1643 | 2914 | 1894 | 256 | 280 | 288 | 397 | 474 | 1894 |
Beta | 331 | 397 | 551 | 4176 | 3177 | 2078 | 213 | 236 | 278 | 580 | 2858 | 2113 | 123 | 128 | 139 | 183 | 240 | 2063 | |
Hybrid | 44 | 64 | 96 | 265 | 989 | 2139 | 39 | 53 | 77 | 163 | 255 | 2134 | 29 | 42 | 57 | 84 | 120 | 432 | |
NNA | GRP | 336 | 509 | 946 | 4942 | 4931 | 4964 | 225 | 335 | 479 | 1975 | 4931 | 4964 | 141 | 182 | 243 | 508 | 838 | 4964 |
Beta | 364 | 515 | 858 | 2690 | 2702 | 2703 | 243 | 336 | 460 | 1190 | 2320 | 2118 | 148 | 183 | 231 | 375 | 482 | 1759 | |
Hybrid | 282 | 364 | 591 | 1983 | 2002 | 1859 | 188 | 251 | 304 | 828 | 1853 | 1719 | 121 | 142 | 175 | 259 | 365 | 1474 | |
PDR | GRP | 0.711 | 0.747 | 0.790 | 0.828 | 0.847 | 0.866 | 0.712 | 0.747 | 0.784 | 0.828 | 0.847 | 0.866 | 0.708 | 0.739 | 0.779 | 0.822 | 0.843 | 0.866 |
Beta | 0.799 | 0.825 | 0.856 | 0.866 | 0.885 | 0.906 | 0.798 | 0.833 | 0.850 | 0.904 | 0.899 | 0.917 | 0.801 | 0.823 | 0.845 | 0.888 | 0.912 | 0.925 | |
Hybrid | 0.906 | 0.918 | 0.929 | 0.956 | 0.941 | 0.922 | 0.899 | 0.911 | 0.922 | 0.947 | 0.958 | 0.925 | 0.890 | 0.894 | 0.903 | 0.932 | 0.941 | 0.962 | |
ET (J) | GRP | 84.9 | 129.8 | 242.1 | 1272.1 | 1272.9 | 1274.8 | 55.2 | 84.5 | 121.3 | 498.8 | 1272.9 | 1274.8 | 33.1 | 43.3 | 59.1 | 125.1 | 203.8 | 1274.8 |
Beta | 86.0 | 131.7 | 245.6 | 1290.2 | 1290.5 | 1292.9 | 56.0 | 86.0 | 123.2 | 511.7 | 1295.1 | 1291.7 | 33.7 | 43.8 | 60.0 | 127.8 | 208.4 | 1294.2 | |
Hybrid | 88.0 | 133.9 | 248.7 | 1305.7 | 1306.0 | 1293.3 | 57.0 | 87.1 | 124.6 | 512.5 | 1304.7 | 1293.6 | 34.3 | 44.2 | 60.7 | 128.4 | 208.9 | 1304.9 | |
eS (mJ) | GRP | 34.61 | 32.98 | 31.34 | 29.88 | 26.97 | 28.63 | 34.29 | 32.95 | 31.48 | 29.87 | 29.26 | 28.63 | 34.63 | 33.31 | 31.66 | 30.06 | 29.35 | 28.63 |
Beta | 31.14 | 30.27 | 29.35 | 28.97 | 27.50 | 27.73 | 31.18 | 30.06 | 29.50 | 28.05 | 28.00 | 27.41 | 31.08 | 30.27 | 29.65 | 28.40 | 27.74 | 27.18 | |
Hybrid | 28.00 | 27.67 | 27.35 | 26.57 | 26.57 | 27.29 | 28.06 | 27.86 | 27.48 | 26.80 | 26.49 | 27.19 | 28.28 | 28.17 | 27.97 | 27.20 | 26.95 | 26.35 |
Error Period | 600 s | 1200 s | 1800 s | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Error Rate % | k | FAR | Packet Drop Rate % | FAR | Packet Drop Rate % | FAR | Packet Drop Rate % | |||||||||
Beta | Hybrid | GRP | Beta | Hybrid | Beta | Hybrid | GRP | Beta | Hybrid | Beta | Hybrid | GRP | Beta | Hybrid | ||
4 | 1 | 0 | 0.09 | 35.96 | 5.03 | 1.30 | 0 | 0.09 | 35.96 | 5.03 | 1.30 | 0 | 0.09 | 35.96 | 5.03 | 1.30 |
12 | 3 | 0 | 0.09 | 36.07 | 5.29 | 1.46 | 0 | 0.09 | 36.03 | 5.45 | 1.64 | 0 | 0.09 | 36.17 | 5.53 | 1.66 |
28 | 7 | 0 | 0.09 | 36.20 | 5.58 | 1.65 | 0 | 0.09 | 36.27 | 5.73 | 1.98 | 0 | 0.09 | 36.36 | 5.82 | 2.14 |
56 | 14 | 0 | 0.09 | 36.34 | 5.82 | 1.84 | 0 | 0.09 | 36.61 | 6.33 | 2.65 | 0 | 0.09 | 36.76 | 6.56 | 3.04 |
72 | 18 | 0 | 0.09 | 36.40 | 5.92 | 2.25 | 0 | 0.09 | 36.75 | 6.62 | 3.02 | 0.02 | 0.09 | 36.93 | 6.72 | 3.45 |
100 | 25 | 0 | 0.11 | 36.56 | 6.40 | 2.50 | 0 | 0.11 | 37.02 | 7.22 | 3.39 | 0.02 | 0.11 | 37.38 | 7.59 | 3.81 |
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Cho, Y.; Qu, G. A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks. Sensors 2023, 23, 4407. https://doi.org/10.3390/s23094407
Cho Y, Qu G. A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks. Sensors. 2023; 23(9):4407. https://doi.org/10.3390/s23094407
Chicago/Turabian StyleCho, Youngho, and Gang Qu. 2023. "A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks" Sensors 23, no. 9: 4407. https://doi.org/10.3390/s23094407
APA StyleCho, Y., & Qu, G. (2023). A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks. Sensors, 23(9), 4407. https://doi.org/10.3390/s23094407