This paper thoroughly compares thirteen unique Machine Learning (ML) models utilized for Intrusion detection systems (IDS) in a meticulously controlled environment. Unlike previous studies, we introduce novel approach that avoids data leakage, enhancing the reliability of our findings. The study draws upon comprehensively labeled 5G-NIDD dataset covering broad spectrum network behaviors, from benign real-user traffic to various attack scenarios. Our preprocessing and experimental design have...

Cyber warfare has emerged as a critical aspect of modern conflict, state and non-state actors increasingly leverage cyber capabilities to achieve strategic objectives. The rapidly evolving threat landscape demands robust adaptive approaches protect against advanced cyberattacks mitigate their impact on national security. Traditional defense strategies often struggle keep pace with the changing landscape, resulting in need for more cyberattacks. This paper presents novel modeling framework,...

This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini’s effectiveness within domain intrusion detection systems (IDS). Recognizing need for improved interaction analysis methodologies IDS, this research aims investigate BukaGini context. is evaluated across four diverse datasets commonly used IDS research: NSLKDD...

Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, robustness are essential. This paper presents the Entropy Mixing Network (EMN), novel hybrid random generator designed to enhance randomness quality by combining deterministic pseudo-random with periodic entropy injection. To evaluate its effectiveness, we propose comprehensive assessment framework that integrates statistical tests, advanced metrics, visual...

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize inflow, storage, outflow by dynamically adjusting flow rates prevent congestion while maximizing resource utilization. Inspired physical dam mechanisms, employs intelligent sluice controls predictive analytics regulate based on system conditions such bandwidth...

Feature interaction is a vital aspect of Machine Learning (ML) algorithms, and gaining deep understanding these interactions can significantly enhance model performance. This paper introduces the BukaGini algorithm, an innovative robust approach for feature analysis that capitalizes on Gini impurity index. By exploiting unique properties index, our proposed algorithm effectively captures both linear nonlinear interactions, providing richer more comprehensive representation underlying data....

Due to the increased usage of Internet Things and heterogeneous distributed devices, development effective reliable intrusion detection systems (IDS) has become more critical. The massive volume data with various dimensions security features, on other hand, can influence accuracy raise computation complexity these systems. Fortunately, Artificial Intelligence (AI) recently attracted a lot attention, it is now principal component This work presents an enhanced intelligent model (E2IDS) detect...

This Spam emails have become a severe challenge that irritates and consumes recipients' time. On the one hand, existing spam detection techniques low rates cannot tolerate high-dimensional data. Moreover, due to machine learning algorithm's effectiveness in identifying mail as solicited or unsolicited, their approaches common systems. paper proposes lightweight learning-based model based on Random Forest (RF) algorithm. According empirical results, proposed achieved 97% accuracy spambase...

<title>Abstract</title> Data leakage during machine learning (ML) preprocessing is a critical issue where unintended external information skews the training process, resulting in artificially high-performance metrics and undermining model reliability. This study addresses insufficient exploration of data across diverse ML domains, highlighting necessity comprehensive investigations to ensure robust dependable models real-world applications. Significant discrepancies performance due were...

This paper explores targeted advertising, focusing on voice recognition technology and its potential effects user privacy, assessing the advantages challenges of such advertising methods. By emphasizing need to balance marketing efficacy privacy protection, analysis delves into evolution, current state, applications in while considering ethical legal aspects. Security implications overheard conversations are also addressed, highlighting importance consent, awareness, frameworks. Strategies...

This paper investigates the challenges and opportunities of implementing African Union Convention on Cyber Security Personal Data Protection (AUDPC) across Africa. Focusing legal, regulatory, technical, infrastructural, capacity building, awareness, Harmonization, cross-border cooperation challenges, identifies key findings that highlight diverse legal systems traditions, lack comprehensive data protection laws, need to balance national security privacy, digital divide, cybersecurity...

Turnkey technology has emerged as a game-changing tool in cyber warfare, offering state and non-state actors unprecedented access to advanced capabilities. The advantages of turnkey include rapid deployment adaptability, lower costs resource requirements, the democratization warfare capabilities, enhanced offensive defensive strategies. However, also introduces significant risks, such proliferation weapons, ethical considerations, potential collateral damage, escalation conflicts, legal...