# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### False Data Injection Attacks (FDIA)
The injecting FDIA types of energy systems are [116]:  
* Energy-request Receiving Attack;
* The attacker compromises demand-nodes and injects a forged quantity of demanded energy;
* Energy-supply Receiving Attack;
* The attacker compromises supply nodes and injects a forged quantity of energy that it could provide to the grid. Different FDIAs classifications divided into three-level classifications proposed in [117]. The FDIAs are categorized concerning the targeted systems at the first level, second is targeted subsystems and can be divided into subsystems and the attack's impact, which can be physical and economic attacks targeting the subsystems at the third level. According to [116], another FDIA attack assumes that the hacker can only reach specific measurements due to the meters' different physical protections. With this study \"building a valid FDIA by minimizing the number of attached meters\", research started, and several attacks with various conclusions and aims have been suggested on the basis of this analysis. FDI attacks are divided into random and target FDI attacks in [118; 119]. In random FDI, attackers aim to inject any false data to cause bad state estimation in the state variables. The target FDI is injecting an attack vector that causes an error into certain state variables. Other types of FDI attacks, such as scaling, ramp, and pulse are proposed in [120]. Previous studies have mostly focused on the FDIA issue in the transmission network. Unlike them, [110] proposes attack models in transmission, storage, and micro-grid networks, with a focus on determining the effect of FDIA on the power grid's economic and stable activity. FDI attacks are described into three major categories as Bad Data Injection Attacks, Replay Attacks (RA), and Zero Dynamics Attacks (ZDA), and many end devices that enable the smooth functionalities of energy systems even from a remote area are proposed in [85]. The RAs are challenging to detect due to cryptography operations' limited capability [96; 109]. ZDAs indicate a cluster of attacks using unstable zeros as the bug to attack smart meters [121; 122]. Then, they will inject the false output through the communication channel. In the end, the real state increases as the time passes, while nearing close to the output-nulling space. In this way, the corresponding outcome is referred to as a stealthy attack and too close to zero [82]. FDI attacks aim to mislead the service providers, disable the sensor nodes to cause service failure between physical systems and the networks, or hijack the communication channels [123]. There are two points to consider in order to understand the success of an attack target:  
* The first is to access data by infiltrating the current energy system. This way, data in sandboxes can also be manipulated. * Second, they control data without being detected [24; 25; 42; 78; 124; 125]. A successful attack can reduce the actual flow of power to destabilize energy systems [126]. As a result, FDI Attacks pose major threats to both energy systems and communication and other physical systems and are difficult to detect in real-time [100; 117; 127; 128]. The FDIA and all its derivatives aim to damage transmitted data; this may cause a chain reaction between different systems in SG, when an attack is accessed in the communication system can affect the transmission or generation system [17; 114; 119; 129; 130]. According to the general nature of all FDI, attacks have the same goal [126]. The objectives are to use physical systems or malicious packets to deceive service providers, capture communication channels, or disable sensor nodes and create an attack that bypasses the traditional bad data detector [71; 127; 131]. By the way, Detection methods are examined in the following section.