The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.

In NRSC24, Elkamchouchi et al. proposed a new approach for key controlled agreement to provide key control in the Pour public key distribution system. In NRSC25, they further proposed an efficient and confirmed protocol for authenticated key agreement to provide forward secrecy in their previously proposed protocol. This paper, however, will show that Pour's protocol and Elkamchouchi et al.'s two protocols cannot withstand key compromise impersonation resilience, and man-in-the-middle attacks, and do not have perfect forward secrecy resilience. To eliminate the pointed out security leaks, we further propose a new two-pass authenticated key agreement with a key confirmation protocol. The proposed protocol has the following properties: (i) it is proved to be secure against above attacks and stronger adversary attacks, and provides the desirable security properties as a three-pass authenticated key agreement protocol. (ii) It can provide entity authentication and assurance for key reception in an indirect way. (iii) It can withstand denial of service attacks. In addition, we also propose a derivation one-pass protocol from the proposed twopass protocol to fit a one-way communication channel, which is suitable for mobile stations and electronic business transactions. The security and the computational complexities of the proposed two protocols outperform those of previously proposed protocols.

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SUMMARYPacket classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW + s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

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Abstract:Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.

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