The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface, making IoT networks increasingly vulnerable to diverse cyber threats. Traditional intrusion detection systems (IDSs) often struggle to effectively identify sophisticated attacks in the dynamic and heterogeneous IoT environment. This paper proposes an adaptive intrusion detection system based on a hybrid deep learning model combined with feature engineering techniques to enhance cybersecurity in IoT networks. The proposed system leverages feature engineering to extract relevant and informative features from network traffic data, which are then fed into a hybrid deep learning model consisting of a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) network for temporal pattern analysis. The CNN component automatically learns hierarchical features from the preprocessed data, while the LSTM network captures long-range dependencies in the sequential network traffic, enabling the system to detect complex and evolving attack patterns. The performance of the proposed system is evaluated using a publicly available IoT network traffic dataset. The experimental results demonstrate that the proposed system achieves superior detection accuracy, precision, recall, and F1-score compared to existing IDSs, highlighting its effectiveness in mitigating cyber threats in IoT environments. Furthermore, the adaptive nature of the system allows it to dynamically adjust its parameters and feature selection based on the evolving threat landscape, ensuring robust and reliable cybersecurity protection for IoT networks.
The proliferation of Internet of Things (IoT) devices has created a vast and complex attack surface, rendering traditional centralized intrusion detection systems (IDS) inadequate for effectively safeguarding these networks. This paper proposes a novel architecture for an adaptive Intrusion Detection System (IDS) that leverages federated learning (FL) and blockchain-based trust management to enhance security in IoT networks. The proposed system allows IoT devices to collaboratively train a global intrusion detection model without sharing sensitive data, preserving privacy and reducing communication overhead. A blockchain is employed to establish a decentralized trust mechanism, ensuring the integrity and reliability of the federated learning process by tracking and verifying contributions from individual devices. The system's performance is evaluated through simulations and real-world experiments, demonstrating its ability to detect a wide range of IoT-specific attacks with high accuracy and minimal false positives. The results highlight the potential of this approach to significantly improve the security posture of IoT networks while addressing key challenges related to privacy, scalability, and trust.
Advanced Persistent Threats (APTs) pose a significant and evolving challenge to modern cybersecurity. Traditional defense mechanisms often prove insufficient against their sophisticated techniques and patient persistence. This paper explores the application of adaptive cyber deception strategies to enhance cyber resilience against APTs. We propose a novel framework that dynamically adjusts deception tactics based on real-time threat intelligence, attacker behavior, and system vulnerability analysis. This framework leverages honeypots, honeynets, and decoy data strategically deployed throughout the network to detect, analyze, and disrupt APT activities. We present a detailed methodology for implementing and evaluating these adaptive deception strategies, including algorithms for deception selection, deployment, and maintenance. The results demonstrate a significant improvement in early threat detection, reduced attacker dwell time, and enhanced overall cyber resilience compared to static deception approaches. The research contributes to a more proactive and dynamic approach to cybersecurity, enabling organizations to better defend against the persistent and evolving threat posed by APTs.
The proliferation of Internet of Things (IoT) devices has created a vast attack surface, making these networks increasingly vulnerable to cyberattacks. Traditional Intrusion Detection Systems (IDS) often struggle to cope with the resource constraints of IoT devices, the dynamic nature of IoT traffic, and the need for real-time threat detection. This paper presents a novel Adaptive Intrusion Detection System (A-IDS) designed specifically for IoT networks. A-IDS employs a hybrid approach that combines federated learning (FL) and edge computing to achieve distributed, adaptive, and efficient intrusion detection. Edge devices perform local anomaly detection using lightweight machine learning models trained collaboratively via FL. This minimizes latency and conserves bandwidth. A centralized server aggregates and refines the global model, enabling the system to adapt to evolving threats. The proposed A-IDS is evaluated using a simulated IoT environment with realistic traffic patterns and attack scenarios. The results demonstrate that A-IDS achieves high detection accuracy, low false positive rates, and minimal resource consumption compared to traditional IDS approaches. This research highlights the potential of FL and edge computing to enhance the security of IoT networks by enabling adaptive and distributed intrusion detection.
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, making IoT networks increasingly vulnerable to diverse cyberattacks. Traditional intrusion detection systems (IDS) often struggle to adapt to the dynamic and heterogeneous nature of IoT environments, requiring centralized data processing that raises privacy concerns. This paper proposes an adaptive intrusion detection system (A-IDS) that leverages federated learning (FL) and blockchain-based trust management to enhance security in IoT networks. The A-IDS utilizes FL to train a global intrusion detection model collaboratively across multiple IoT devices without sharing raw data, preserving data privacy. Furthermore, a blockchain-based trust management system is integrated to ensure the integrity of the FL process and mitigate potential attacks from malicious participants. The proposed system is evaluated through extensive simulations using a realistic IoT network scenario. The results demonstrate that the A-IDS achieves high detection accuracy while maintaining data privacy and resilience against adversarial attacks, offering a promising solution for securing IoT environments. The system's performance is compared against existing centralized and decentralized approaches, highlighting its advantages in terms of accuracy, privacy, and robustness.