Enhancing Network Visibility and Security with Advanced Port Scanning Techniques.

Network security is paramount in today's digital landscape, where cyberthreats continue to evolve and pose significant risks. We propose a DPDK-based scanner based on a study on advanced port scanning techniques to improve network visibility and security. The traditional port scanning methods suffer from speed, accuracy, and efficiency limitations, hindering effective threat detection and mitigation. In this paper, we develop and implement advanced techniques such as protocol-specific probes and evasive scan techniques to enhance the visibility and security of networks. We also evaluate network scanning performance and scalability using programmable hardware, including smart NICs and DPDK-based frameworks, along with in-network processing, data parallelization, and hardware acceleration. Additionally, we leverage application-level protocol parsing to accelerate network discovery and mapping, analyzing protocol-specific information. In our experimental evaluation, our proposed DPDK-based scanner demonstrated a significant improvement in target scanning speed, achieving a 2× speedup compared to other scanners in a target scanning environment. Furthermore, our scanner achieved a high accuracy rate of 99.5% in identifying open ports. Notably, our solution also exhibited a lower CPU and memory utilization, with an approximately 40% reduction compared to alternative scanners. These results highlight the effectiveness and efficiency of our proposed scanning techniques in enhancing network visibility and security. The outcomes of this research contribute to the field by providing insights and innovations to improve network security, identify vulnerabilities, and optimize network performance.

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection.

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway.

As a result of vehicle platooning, advantages including decreased traffic congestion and improved fuel economy are expected. Vehicles in a platoon move in a single line, closely spaced, and at a constant speed. Vehicle-to-vehicle communications and sensor data help keep the platoon formation in place, and the CACC system is responsible for maintaining it. In reality, V2V transmissions are essential for reducing platooning distances while still ensuring their safety and security. It is far more difficult to confirm the veracity of a V2V message's content than it is to verify its integrity and source authentication. Only platoon members can send and receive V2V communications by implementing a practical access control mechanism. The goal is to link a prospective platoon member's digital identification to their actual location inside the unit. A physical challenge-response interaction is used in the CAVVPM process to verify that a prospective platoon member respects the rules. The applicant is asked to perform a series of random longitudinal movements, thus, the protocol's name. Remote attackers cannot join the platoon or send bogus CACC communications because CAVVPM blocks them. CAVVPM is more resistant to pre-recording assaults than previous work, and it can validate that the candidate is precisely behind the verifier in the same lane compared to previous studies.