## 4 Results
### Future Improvements and Challenges  
Source datasets are crucial for both cybersecurity and machine learning in a similar way. Most publicly available data is older, which might not be enough to identify trends in undetected cyberattacks. The most recent attacks and their repeated patterns remain a mystery, even though current data can be processed into knowledge through several different processing steps. As a result, it's possible that certain conclusions won't be drawn with exceptional precision using future processing or machine learning approaches. The production of numerous new cybersecurity datasets for particular problems like predicting attacks or detecting intrusions is a significant difficulty when using machine learning techniques in cybersecurity. Security datasets may be unbalanced, noisy, lacking crucial details, meaningless, or include examples of security vulnerabilities that are inconsistent with one another. The training of machine learning models may be more challenging and time-consuming when a dataset contains this kind of consistency [169]. These data concerns need to be fixed before using machine learning to create a data-driven cybersecurity solution. Knowing everything there is to know about the issues with cybersecurity data is essential, as is finding reasonable solutions to these issues by using either current algorithms or brand-new algorithms to, among other things, locate malware and intrusions. One method to directly address these problems is feature engineering [170], which examines model features to remove related parts. This method reduces the complexity of the data. A key strategy for dealing with measurement errors is using hybrid models, as explained in [73], or data creation, as described in [171]. More vulnerabilities that could lead to data leakage need to be addressed. The most popular and well-known techniques in cyber security use signatures to find intrusion attempts [172]. Due to data shortages, overly simplified characteristics, or inadequate profiling, these systems might overlook some assaults or events. These problems can be mitigated by signature-based or hybrid detection strategies combining signature-based and anomaly-based detection techniques. With a hybrid learning strategy that makes use of numerous machine learning techniques, intrusion detection, malware analysis, phishing detection, etc., all perform better. Machine learning, statistical analysis, and deep learning algorithms can be combined to make wise cybersecurity management decisions. Due to the enormous amount of network traffic data and the high number of minute traffic features, a security model based on machine learning has frequently been questioned regarding its effectiveness and performance. Principal component analysis, singular value decomposition [73], and linear discriminant analysis have all been used by researchers to handle high-dimensional data [173]. Contextually, it might be advantageous to include low-level information in datasets that could be connected to problematic behaviors. This kind of contextual data may be categorized using an ecosystem or taxonomy for upcoming research. Therefore, choosing the best features or extracting the most important ones while considering machine-readable aspects and the context presents another challenge for machine-learning approaches in cybersecurity. To create effective cybersecurity solutions, this is necessary. When models are used to produce predictions even while essential data is missing or significantly varies between datasets, this is known as data leakage [169]. Prediction models frequently result in too hopeful conclusions when they are being created. Still, when evaluated on new data, unsatisfactory findings list this problem, known as \"leaksfrom the future,\" as \"one of the top 10 data mining defects.\" They suggest using exploratory data analysis to find and fix leakage sources. EDA enables machine learning models to collect more accurate data, enhancing the dataset's usefulness.