# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer
#### 4.3.3 Gray Hole Attacks  
Unlike black hole attacks, a malicious node in the gray hole attack (GHA) randomly drops packets. Thus, detecting GHAs is more difficult than SFAs because a malicious node can appear to be working properly before it selectively discards packets.  
[PERSON] et al. [63] addressed the impact of GHAs on the sink node in WSNs. To prevent this attack, the authors designed a fuzzy logic model to identify suspicious activity through the rule. This fuzzy logic model can be used to distinguish GHAs with differing drop probabilities. In fuzzy logic, the first task is to determine whether incoming and outgoing packets are traveling at the same rate. If a GHA occurs, some data cannot reach their destination.  
[PERSON] and [PERSON] [64] presented a simple GHA detection method, namely the prevention and reduction approach, by examining the storage table. The proposed scheme consists of four functionalities, namely network data collection, gray hole detection, gray hole prevention, and gray hole reduction. If the traffic limit is exceeded, an analysis of the flow at the higher level is conducted to validate flooding. If the attack is ongoing, the corresponding node is identified as a malicious user and blocked. [PERSON] et al. [65] proposed a trust-based cellular automata energy drainage detection and prevention scheme (CAT-EDP) to detect GHAs and scheduling attacks over a low-energy adaptive clustering hierarchy (LEACH) routing protocol. In the proposed scheme, a direct and indirect trust protocol to designate the CH with a trust value and received signal strength index (RSSI) is used to prevent malicious nodes from being the cluster header. To achieve this objective, every node measures its energy value and shares it with its neighbors. When an energy drain attack occurs, a node notices that energy draining is faster than in the normal case. Because the cluster header is set by the remaining energy, distance, and trust values, the attacker node is blocked from being the cluster header. Another paper that addresses gray holes and scheduling attacks under LEACH was presented in 2024. [PERSON] et al. [66] proposed space-time-efficient probabilistic data structures to overcome the resource limitations of sensor nodes. The data structure is crucial for selecting trust metrics, designing cellular automata (CA) rules, updating trust rates, and propagating them to the whole network. The Bloom filter, CM sketch, and CA structure are used to address energy drainage attacks are presented. This method is used to prevent a malicious node from being a cluster header. If a node detects an energy drainage attack, then it is inserted into the Bloom filter. In the next round, when no frame is received from the CH listed in the Bloom filter, it is excluded from the nominated cluster header. [PERSON] et al. [67] incorporated game theory to detect GHAS. The interaction between THE attack and its victim is modeled as an incomplete information game with three main actors: normal, gray hole, and destination node attacks. Optimal strategies through a mathematical model are dependent on various scenarios. The proposed scheme aims to reach the optimal Nash equilibrium by determining and evaluating action profiles of gray hole nodes.