An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction

Hussein Alrammahi · Mohammed Thakir Mahmood

Intrusion Detection Systems (IDS) are elementary building blocks of network security that can be used to detect unauthorized access and malicious activity. But traditional IDS approaches often suffer from problems such as high false positives, inability to adapt quickly to new threats, and scalability. This paper presents an advanced intrusion detection model that uses machine learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to enhance detection. Using the KDD Cup 1999 data, the framework was highly preprocessed, feature engineered, and hyperparameters adjusted to achieve optimal performance. The Neural Network model outperformed other algorithms at 92.5% accuracy, 93.8% recall, and 92.4% F1-score, proving its ability to identify complex attack patterns with minimal false positives effectively. Additionally, the proposed framework reflected significant improvement over existing IDS solutions that always achieve accuracies of 80–85%. Intrusion Detection Systems (IDS) are important components of security, assuming the task of monitoring, detecting, and responding to unauthorized activities in network frameworks. This work's most notable contributions are its integration of sophisticated machine learning methods, systematic assessment of detection performance on a wide range of attack types, and comparison with well-established IDS benchmarks. In spite of facing issues like the complexity of the dataset and computational requirements, findings point to the efficacy of machine learning-based IDS in countering modern-day cybersecurity threats. Real-time data fusion and improving model interpretability for real-world implementation are areas that need to be addressed in the future.

Enhancing Malware Detection Through Machine Learning Techniques

Zeina S. Jassim · Mohamad M. Kassir

Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.