An Efficient Technique for Security of Mobile Agents

Abstract

In the realm of wireless sensor networks, the Mobile Agent (MA) paradigm presents significant advantages over the traditional client-server model, particularly in addressing the crucial issue of energy consumption. Optimizing the itinerary for efficient data collection and considering the detection of malicious nodes are pivotal factors. We delve into the challenges and risks of securing mobile agents within extensive and dynamic sensor networks. The security of large-scale networks is paramount, especially when harnessing mobile agents to optimize network efficiency and data processing capabilities. Securing large-scale networks is crucial for protecting sensitive data, ensuring uninterrupted operations, and upholding user trust.

This abstract delineates the security considerations associated with mobile agents in vast WSNs. Large-scale WSNs often operate in resource-limited and hostile environments, so they are vulnerable to various security threats, such as node compromise, data tampering, and unauthorized access. We assess potential vulnerabilities arising from mobile agent movement, communication, and data aggregation processes and scrutinize the impact of security breaches on network performance and reliability. To address these challenges, we survey state-of-the-art security mechanisms, including secure agent migration protocols, cryptographic methods for data protection, and trust management models for agent authentication and authorization.

In the first scheme, our research focuses on identifying and addressing attacks to prevent communication breakdown as sensor nodes become more vulnerable in dynamic environments. We use the SPIN protocol and machine learning models to classify attacks and propose an ensemble model with 95% average accuracy. K-Fold cross-validation ensures consistency.

The second scheme focuses on using the Border-Hunting Optimization-based Deep CNN (BHO-DCNN) for a mobile agent (MA)-based intrusion detection in Wireless Sensor Networks (WSN). This approach aims to accurately identify malicious activities within sensor networks. The BHO-DCNN algorithm resulted in 45 alive nodes, an end-to-end delay of 0.2572 ms, a normalized energy consumption of 0.1622 J, and a throughput of 0.3125 % for 50 nodes at a 100% population rate.

The third scheme focuses on developing a self-configuring mobile agent-based intrusion detection system using a Hybrid Deep LSTM. In wireless networks, sensor nodes send data to cluster heads, which then transmit it to the base station. Cluster head selection is based on a multi-objective function. The data is sent to the sink node through a mobile agent, where a Hybrid Deep LSTM classifier is used. The model will be trained to improve intrusion detection based on the self-configuring concept. Its effectiveness will be evaluated by comparing it with existing techniques using performance metrics such as energy consumption, delay, traffic overhead, and throughput.

The fourth scheme focuses on a comprehensive approach to safeguarding mobile agents from malicious code execution by utilizing a Dynamic Bloom Filter and BlowFish algorithm; it addresses the inherent security challenges in cloud networking. The proposed method aims to detect and prevent malicious code execution. The paper offers a compelling solution to enhance agent security by using Dynamic Bloom Filters, elliptical curve keys, and asymmetric BlowFish encryption.

Implemented on the JADE platform, the results reaffirm the effectiveness of the Dynamic Bloom Filter and BlowFish algorithm in fortifying mobile agents against security threats.