# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 4 Future Improvements and Challenges for ML-Based Cybersecurity
### Homomorphic Encryption  
Homomorphic Encryption (HE) is considered as one of the greatest advancements in cryptography [175]. HE provides access to a non-trustworthy third-party to process data without any clue, thereby allowing access to confidential data. The user end or the unauthorized remote server obtains access to the encrypted data and not the secret key for decryption. Hence, the host can be assured that the data are not leaked outside of the domain. HE has a long list of applications, including cloud computing, financial transactions, quantum computing threat shields, etc. HE can be applied in two ways, partial and fully. Fully Homomorphic Encryption (FHE) makes the machine learning training process easier without data leakage. Deep learning and shallow machine learning algorithms heavily rely on domain data, which are often difficult to share publicly [176]. FHEhas facilitated a new process to delegate these kinds of sensitive data sharing without sharing the actual meaningful data. One of the major drawbacks of FHE is its limitation to the use of integers [177]. Thus, researchers are trying to find matrix-based schemes for FHE. Recent research has proven effective by using the lowest degree of polynomial approximation functions such as Chebyshev with a continuous function such as sigmoid. This has created a new encryption over FHE to use in homogeneous networking [178]. Federated learning has accelerated multi-party joined learning processes by applying FHE in case of image data with sample expansions [179]. Medical and health-related data are always highly confidential since they contain Protected Health Information (PHI). One of the major breakthroughs happened when healthcare data became accessible through FHE. Researchers proved multiple effective ways to use medical images or other data from Internet of Medical Things (IoMT) using machine learning-based HE. In 2019, HE combined with chaotic mapping successfully secured data transfer, but the computational privacy was vulnerable [180]. In 2021, HE was combined with secret sharing and computation was performed on the edge computation layer. Moreover, the mathematical operation was conducted in a distributed manner, but no data leakage happened [181]. A recurrent neural network, CryptoRNN, has been introduced recently, which is mostly focused on the privacy preservation of blockchain technology [182]. Cloud-based FHE integration is the most advanced and commonly used because of the versatility of domain data access and vast computational power. The Machine Learning as a Service Platform (MLaa5) provides a wide variety of machine learning algorithms to enable FHE to protect confidential data [183]. In the early exploration of HE in Wireless Sensor Networks (WSNs), researchers experimented with the performance of HE in a network simulation NS-2 tool, where, for each experimental agent, the environment remained similar. For data aggregation operation, FHE consumed less time than DAA, which decrypts hop-by-hop, and achieved the time complexity of O(n) [184]. HE increases the global data stream and machine learning's practical applications by scaling, along with enhancing the overall cybersecurity [185].