In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

Malicious attackers on the Internet use automated attack programs to disrupt the use of services via mass spamming, unnecessary bulletin boarding, and account creation. Completely automated public turing test to tell computers and humans apart (CAPTCHA) is used as a security solution to prevent such automated attacks. CAPTCHA is a system that determines whether the user is a machine or a person by providing distorted letters, voices, and images that only humans can understand. However, new attack techniques such as optical character recognition (OCR) and deep neural networks (DNN) have been used to bypass CAPTCHA. In this paper, we propose a method to generate CAPTCHA images by using the fast-gradient sign method (FGSM), iterative FGSM (I-FGSM), and the DeepFool method. We used the CAPTCHA image provided by python as the dataset and Tensorflow as the machine learning library. The experimental results show that the CAPTCHA image generated via FGSM, I-FGSM, and DeepFool methods exhibits a 0% recognition rate with ε=0.15 for FGSM, a 0% recognition rate with α=0.1 with 50 iterations for I-FGSM, and a 45% recognition rate with 150 iterations for the DeepFool method.

A Multipath TCP (MPTCP) connection uses multiple subflows (i.e., TCP flows), each of which traverses over a wireless link, enabling throughput and resilience enhancements in mobile wireless networks. However, to achieve the benefits, the subflows are necessarily initialized (i.e., must complete TCP handshakes) and sequentially attached to the MPTCP connection. In the standard (MPTCPST), MPTCP initialization raises several problems. First, the TCP handshake of opening subflow is generally associated with a predetermined network. That leads to degraded MPTCP performance when the network does not have the lowest latency among available ones. Second, the first subflow's initialization needs to be successful before the next subflow can commence its attempt to achieve initialization. Therefore, the resilience of multiple paths fails when the first initialization fails. This paper proposes a novel method for MPTCP initialization, namely MPTCPSD (i.e., MPTCP with SYN duplication), which can solve the problems. MPTCPSD duplicates the first SYN and attempts to establish TCP handshakes for all subflows simultaneously, hence inherently improves the loss-resiliency. The subflow that achieves initialization first, is selected as the first subflow, consequently solving the first problem. We have implemented and extensively evaluated MPTCPSD in comparison to MPTCPST. In an emulated network, the evaluation results show that MPTCPSD has better performance that MPTCPST with the scenarios of medium and short flows. Moreover, MPTCPSD outperforms MPTCPST in the case that the opening subflow fails. Moreover, a real network evaluation proves that MPTCPSD efficiently selects the lowest delay network among three ones for the first subflow regardless of the preconfigured default network. Additionally, we propose and implement a security feature for MPTCPSD, that prevents the malicious subflow from being established by a third party.

We propose new CAPTCHA image generation systems by using generative adversarial network (GAN) techniques to strengthen against CAPTCHA solvers. To verify whether a user is human, CAPTCHA images are widely used on the web industry today. We introduce two different systems for generating CAPTCHA images, namely, the distance GAN (D-GAN) and composite GAN (C-GAN). The D-GAN adds distance values to the original CAPTCHA images to generate new ones, and the C-GAN generates a CAPTCHA image by composing multiple source images. To evaluate the performance of the proposed schemes, we used the CAPTCHA breaker software as CAPTCHA solver. Then, we compared the resistance of the original source images and the generated CAPTCHA images against the CAPTCHA solver. The results show that the proposed schemes improve the resistance to the CAPTCHA solver by over 67.1% and 89.8% depending on the system.

We propose a new construct, the Text-Graphics Character (TGC) CAPTCHA, for preventing dictionary attacks against password authentication systems allowing remote access via dumb terminals. Password authentication is commonly used for computer access control. But password authentication systems are prone to dictionary attacks, in which attackers repeatedly attempt to gain access using the entries in a list of frequently-used passwords. CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) are currently being used to prevent automated "bots" from registering for email accounts. They have also been suggested as a means for preventing dictionary attacks. However, current CAPTCHAs are unsuitable for text-based remote access. TGC CAPTCHAs fill this gap. In this paper, we define two TGC CAPTCHAs and incorporate one of them in a prototype based on the SSH (Secure Shell) protocol suite. We also prove that, if a TGC CAPTCHA is easy for humans and hard for machines, then the resulting CAPTCHA is secure. We provide empirical evidence that our TGC CAPTCHAs are indeed easy for humans and hard for machines through a series of experiments. We believe that a system exploiting a TGC CAPTCHA will not only help improve the security of servers allowing remote terminal access, but also encourage a healthy spirit of competition in the fields of pattern recognition, computer graphics, and psychology.

This paper deals with the use of neural network rule extraction techniques based on the Genetic Programming (GP) to build intelligent and explanatory evaluation systems. Recent development in algorithms that extract rules from trained neural networks enable us to generate classification rules in spite of their intrinsically black-box nature. However, in the original decompositional method looking at the internal structure of the networks, the comprehensive methods combining the output to the inputs using parameters are complicated. Then, in our paper, we utilized the GP to automatize the rule extraction process in the trained neural networks where the statements changed into a binary classification. Even though the production (classification) rule generation based on the GP alone are applicable straightforward to the underlying problems for decision making, but in the original GP method production rules include many statements described by arithmetic expressions as well as basic logical expressions, and it makes the rule generation process very complicated. Therefore, we utilize the neural network and binary classification to obtain simple and relevant classification rules in real applications by avoiding straightforward applications of the GP procedure to the arithmetic expressions. At first, the pruning process of weight among neurons is applied to obtain simple but substantial binary expressions which are used as statements is classification rules. Then, the GP is applied to generate ultimate rules. As applications, we generate rules to prediction of bankruptcy and creditworthiness for binary classifications, and the apply the method to multi-level classification of corporate bonds (rating) by using the financial indicators.

Photoacoustic (PA) spectra on the 3, 4, 9, 10-perylenetetracarboxylic dianhydride (PTCDA) films deposited by the vacuum evaporation were measured. The films have layered structures constructed from the perylene molecule plane structures. The crystal quality depended on the deposited substrate and the photoacoustic spectroscopy (PAS) seems to be the very useful tools to evaluate these properties from the non-radiative features. The films deposited on the three different substrate had the almost same PL spectra, but the films deposited on the glass substrate had the large non-radiative peaks in the PA spectra contrary to the films deposited on the alumina or crystal Si (100) those had the non-radiative peaks only observed at the short wavelength region.

This paper proposes three-phased traffic conditioner (3PTC) to be installed at edge routers in Differentiated Services (DiffServ) networks. 3PTC ensures that Assured Service (AS) flows are supplied with the throughput assurance, which stems from alleviating the impact of the size of TCP reserved rate, UDP/TCP interaction, Round Trip Time (RTT) and number of microflows. 3PTC is composed of token bucket phase, writing probability (WP) calculation phase and queue management phase. Computer simulation results show that 3PTC guarantees throughput assurance and provides end users with expected service levels.

This paper describes the performance evaluation of the Translation Look-aside Buffer (TLB) for highly integrated microprocessors, especially concerning the TLB in the SPARC Reference MMU specification. The analysis covers configurations, the number of entries, and replacement algorithms for the instruction TLB and the data TLB, which are assumed to be practically integrated on one die. We also present performance improvement using a Page Table Cache (PTC). We evaluate some types of TLB configurations with software simulation and excute the Systems Performance Evaluation Cooperative (SPEC) programs.