### Literature Survey
The challenges in the enhancement of the cyber security of the smart grid are effectively handled in [PERSON], [PERSON] et al. [18]. It is important, as it is a key measure of the resilience of the smart grid. Three key parameters are adopted namely: accuracies, computational time, and robustness. However, an all-inclusive solution that suits all the requirements of the power system is yet to be established. To handle FDI attacks, an artificial intelligence-based detection method was proposed in [PERSON], [PERSON] et al. [19] to determine meters that have been compromised. The measurements are accurate when subjected to cyber attacks, and the faulty meters are identified. Photovoltaic (PV) farms are also vulnerable to such cyber threats. A framework is proposed in [PERSON], [PERSON] et al. [20] to detect data integrity attacks for PV farms. Many such schemes have been proposed based on machine learning and computational intelligence in [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. and [PERSON], and [PERSON] et al. [21; 22] for detection of cyber threats in a smart grid. In all these works, state estimation of the power system is also assessed simultaneously without compromising on convergence speed. Decision-making approaches as well as game theory have also been employed for analysis, detection and prevention of cyber threats in [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. [24; 25]. Intrusion-detection frameworks by means of state observer schemes and proactive intrusion-detection schemes have been incorporated for analytics and detection for smart inverters in [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. [26; 27]. Predictive control schemes for detection and mitigation of FDI on the inverter was discussed in [PERSON], [PERSON] et al. [28]. The authors employed the k-nearest neighbors (KNN) algorithm for mitigating FDI attacks. It was subjected to various types of attack signals. Results portrayed robust performance. In order to facilitate real-time monitoring and control of the electric grid, synchrophasor technologies were employed in [PERSON], [PERSON] et al. [29]. Phasor measurement units (PMUs) are used, owing to their enhanced situation-awareness capabilities. The amalgamation of block chain technologies in [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. [30; 31; 32] with IoT in [PERSON], [PERSON] et al. [33] for co-ordination and control is investigated. Several challenges exist in the adoption of block chain in IoT; several include co-simulation and secondary control. The primary concerns of the existing literature on cyber secure smart inverters are highlighted below:  
* Cyber threats pose a major hurdle to the operation and maintenance of smart grid;
* Sensors that form the most important interface in the cyber-physical systems in the smart grid are most susceptible to false data injection that can once again compromise the smart grid;
* Co-ordination and control of different components of a smart grid (particularly smart inverters) along with detection, analysis and mitigation of the cyber threats continue to occupy primary research space;
* Newer technologies such as block chain and IoT are increasingly being adopted for greater grid resilience and more reliable performance. Our work is an attempt to provide a comprehensive overview of the existing and upcoming technological advancements in cyber secure smart inverters at the distribution level. The design aspects, topologies and control strategies of the same, along with communication aspects and standard cyber security protocols are addressed. The different techniques of cyber attack detection and identification are also provided for the benefit of the readers. The emergence of block chain, Synchrophasor and IoT technologies is also detailed in the work.