The TCP protocol’s inherent lack of built-in security mechanisms has rendered it susceptible to various network attacks. Conventional defense approaches face dual challenges: insufficient line-rate processing capacity and impractical online deployment requirements. The emergence of P4-based programmable data planes now enables line-speed traffic processing at the hardware level, creating new opportunities for protocol protection. In this context, we present MARS -a data plane-native TCP abuse detection and mitigation system that synergistically combines the Beaucoup traffic monitoring algorithm with artificial neural network (ANN) based anomaly detection, enhanced by adaptive heuristic mitigation rules. Through comprehensive benchmarking against existing TCP defense mechanisms, our solution demonstrates 12.95% higher throughput maintenance and 25.93% improved congestion window recovery ratio during attack scenarios. Furthermore, the proposed framework establishes several novel evaluation metrics specifically for TCP protocol protection systems.

Software-Defined Networking (SDN) is a new paradigm that facilitates network management by enabling programmability and disassociating the control plane from the data plane. SDN places the control plane into one or more controllers that take charge of the entire network. However, the logically centralized controller of SDN makes it subject to some security issues. Denial-of-Service (DoS) attacks are the main threat to SDN that can lead to impaired performance of the entire network. Low-rate Denial-of-Service (LDoS) attack is a variant of DoS attacks with a lower average attack rate and high concealability which is difficult to identify with traditional DDoS/DoS attack detection mechanisms. Additionally, existing LDoS attack detection and defense mechanisms often have weak real-time performance. To address this issue, we propose in this paper PeakSAX, a novel framework that can protect SDN against LDoS attacks in real-time by (1) Attack monitoring, (2) Traffic symbolization, (3) Malicious traffic identifying, (4) Attacker location, and (5) Mitigation strategy deployment. Simulation results show that PeakSAX can quickly identify and mitigate the impact of LDoS attacks about 4s, which improves over 70% compared to existing solutions.

With the large-scale application of cloud storage, how to ensure cloud data integrity has become an important issue. Although many methods have been proposed, they still have their limitations. This paper improves some defects of the previous methods and proposes an efficient cloud data integrity verification scheme based on blockchain. In this paper, we proposed a lattice signature algorithm to resist quantum computing and introduced cuckoo filter to simplify the computational overhead of the user verification phase. Finally, the decentralized blockchain network is introduced to replace traditional centralized audit to publicize and authenticate the verification results, which improves the transparency and the security of this scheme. Security analysis shows that our scheme can resist malicious attacks and experimental results show that our scheme has high efficiency, especially in the user verification phase.

Network intrusion detection based on SVM is the hot topic of network security research, and the existing researches have low detection rate, high false positive rate and other issues. Optimizing particle swarm optimization parameters of SVM is an effective solution, but the PSO algorithm is easy to fall into local optimum and results premature convergence.We propose an improved particle swarm optimization algorithm ICPSO, which use chaos operator ergodicity, randomness, sensitivity to initial conditions and other characteristics and the ICPSO is used to make the chaos into the inertia weight factor parameters and The chaos is applied to the optimization of the RBF kernel function parameter g and the penalty factor C, and to improve the convergence speed and precision of the particle swarm optimization. The experimental results show that: relative to the PSO-SVM algorithm and GA-SVM algorithm, ICPSO-SVM improves the efficiency of intrusion detection, and is an effective intrusion detection model.

With widespread pervasion of Location-Based Service (LBS), location privacy has attracted more and more attention. In the traditional LBSs model, users need to repeatedly send their location information to the provider's servers or an intermediate Anonymizing Server (AS) for service, which has a lot of disadvantages, such as the low reutilization ratio of the information, the high threat of un-trusted service providers and the excessive reliance on AS. The latest cache method gives a new idea, but it does not take users' mobile behavior into account. To address these issues, this paper presents a multi-level location privacy protection method, RuleCache, which combines the users' mobility patterns with cache and utilize the cache content of distributed neighbors to protect location privacy. Moreover, when the user has to send query to the LBS server, we propose a Cloaking Region Generating Algorithm (CRGA) to achieve protection which considers the factor of query probability and data timeliness to increase cache contribution rate and update outdated data in time, respectively. We carry out extensive simulation and evaluation show that our RuleCache have a higher performance than many other methods on communication cost, cache hit ratio and storage cost.