Identification of algorithm from the given dataset using AI/ML Techniques
Abstract : Background: There are large number of cryptographic algorithms available for ensuring data confidentiality and integrity in secure communication. Identification of the algorithm is a research activity that leads to better understanding of the weakness in its implementation, in order to make the algorithm more robust and secure.
Description: The above Problem Statement envisages that approach(s) be developed using AI/ML techniques for identification of the cryptographic algorithm by analyzing given data. The provided datasets are generated using modern cryptographic algorithms.
The algorithm is expected to be identified by using a combination of AI/ML and innovative approaches. The successful approaches may also be automated by developing a software solution which takes the given dataset as input and gives probable cryptographic algorithms as output. Expected Solution: Logical approach be developed to successfully identify the algorithm for the given dataset.
The approaches should either be implemented in software form or should be feasible to be developed as a software.
AI/ML have made most cities untenable for traditional tradecraft. Machine learning can integrate travel data (customs, airline, train, car rental, hotel, license plate readers…,) integrate feeds from CCTV cameras for facial recognition and gait recognition, breadcrumbs from wireless devices and then combine it with DNA sampling.
The result is automated persistent surveillance. China’s employment of AI as a tool of repression and surveillance of the Uyghurs is a dystopian of how a totalitarian regimes will use AI-enable ubiquitous surveillance to repress and monitor its own populace.
AI will enable new levels of performance and autonomy for weapon systems. Autonomously collaborating assets (e.g., drone swarms, ground vehicles) that can coordinate attacks, ISR missions, & more.
Overfitting: AI/ML models, especially complex ones, can become too tailored to the specific dataset they are trained on. This means they might perform well on the training data but poorly on new, unseen data. Overfitting can result in an inability to generalize findings accurately.
Data Quality and Quantity: The effectiveness of AI/ML techniques largely depends on the quality and quantity of the dataset. Incomplete, noisy, or biased data can lead to inaccurate or misleading results. Additionally, obtaining a sufficiently large and representative dataset can be challenging.
Interpretability: Many AI/ML models, especially deep learning models, act as "black boxes," making it difficult to understand how they arrived at a particular conclusion. This lack of transparency can be problematic for verifying results and understanding the underlying patterns.
Bias and Fairness: AI/ML models can inadvertently reinforce or amplify existing biases present in the data. This can lead to unfair or discriminatory outcomes, especially in sensitive applications.
Initially, the system starts with data acquisition and preprocessing, where raw data is collected from various sources and cleaned to address errors and missing values. This step also includes transforming the data into a suitable format for analysis through normalization and encoding.
Next, the system focuses on feature engineering, which involves selecting and creating the most relevant features to enhance model performance. This is followed by exploratory data analysis (EDA), where data visualization and descriptive statistics are used to uncover patterns and relationships within the data, guiding the subsequent model selection.
In the model selection and training phase, the system evaluates a range of algorithms tailored to the problem at hand, whether it’s classification, regression, or clustering. Techniques like Decision Trees, Random Forests, and Neural Networks are trained on the dataset, with hyperparameters tuned to optimize performance.
Automation and Efficiency: AI/ML techniques can automate the process of identifying and optimizing algorithms, reducing the need for manual intervention and speeding up data analysis.
Pattern Recognition: These techniques excel at recognizing complex patterns and relationships within large datasets that might be difficult or impossible to detect manually. This can lead to the discovery of new insights or trends.
Adaptability: AI/ML models can adapt to new data over time. Once trained, they can continually learn and update their predictions or classifications as new data becomes available.
Scalability: AI/ML techniques can handle large-scale datasets efficiently, making it feasible to analyze and identify algorithms or patterns in big data environments.
Predictive Power: AI/ML models can predict future trends or behaviors based on historical data, providing valuable foresight that can inform strategic decisions.
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