The ignition and extinction characteristics of perchlorate-based electrically controlled solid propellants (ECSPs) that can achieve multiple ignition and produce controllable thrusts were investigated. We examined the multiple-ignition and combustion behaviors of ECSPs and identified the basic ignition-model parameters for these materials. Single-frame photography and a schlieren imaging system were used to determine the ignition, combustion, and extinction properties of two different ECSP formulations at various voltages, pressures, and initial temperatures. The results revealed that increasing the voltage, pressure, and initial temperature reduced the ignition delay; whereas increasing the number of ignitions decreased the ignition delay and ignition energy but increased the extinction delay and smoldering time. Furthermore, under the same experimental conditions, the voltage, pressure, and initial temperature produced a larger effect on a nonmetallic ECSP than on an ECSP containing 5% aluminum (Al) powder. The nonmetallic ECSP exhibited a longer ignition delay, higher ignition energy, and better extinguishing performance than the Al-containing ECSP. Moreover, four distinct stages of the ECSP ignition process were identified. The findings of this study demonstrated that the electrical energy consumed during condensed phase heating plays a critical role in propellant ignition.

A honeypot is a decoy tool for luring an attacker and interacting with it, further consuming its resources. Due to its fake property, a honeypot can be recognized by the adversary and loses its value. Honeypots equipped with dynamic characteristics are capable of deceiving intruders. However, most of their dynamic properties are reflected in the system configuration, rather than the location. Dynamic honeypots are faced with the risk of being identified and avoided. In this paper, we focus on the dynamic locations of honeypots and propose a distributed honeypot scheme. By periodically changing the services, the attacker cannot distinguish the real services from honeypots, and the illegal attack flow can be recognized. We adopt game theory to illustrate the effectiveness of our system. Gambit simulations are conducted to validate our proposed scheme. The game-theoretic reasoning shows that our system comprises an innovative system defense. Further simulation results prove that the proposed scheme improves the server’s payoff and that the attacker tends to abandon launching attacks. Therefore, the proposed distributed honeypot scheme is effective for network security.

As one of the active defense technologies, the honeypot deceives the latent intruders to interact with the imitated systems or networks deployed with security mechanisms. Its modeling and performance analysis have not been well studied. In this paper, we propose a honeypot performance evaluation scheme based on Stochastic Petri Nets (SPN). We firstly set up performance evaluation models for three types of defense scenarios (i.e., firewall; firewall and Intrusion Detection System (IDS); firewall, IDS and honeypot) based on SPN. We then theoretically analyze the SPN models by constructing Markov Chains (MC), which are isomorphic to the models. With the steady state probabilities based on the MC, the system performance evaluation is done with theoretical inference. Finally, we implement the proposed three SPN models on the PIPE platform. Five parameters are applied to compare and evaluate the performance of the proposed SPN models. The analysis of the probability and delay of three scenarios shows that the simulation results validate the effectiveness in security enhancement of the honeypot under the SPN models.

Multi-access edge computing (MEC) presents computing services at the edge of networks to address the enormous processing requirements of intelligent applic

Spam filtering is a great problem nowadays. The conventional spam filtering techniques still result in high false positives and false negatives. This paper proposes a novel online spam filter based on URLs and maximum entropy model. The filter identifies spam by classifying the e-mails with the pre-trained classifier based on the maximum entropy model and filters the spam online in terms of the characteristics of SMTP. Experimental results demonstrate it can significantly raise the filtering accuracy, effectively reduce false positives and can be applied to online processing environment by reducing the computational cost than the state-of-the-art techniques

This paper presents our preliminary work in network anomaly detection. The experimental results demonstrate an inspiring and promising trend for lightweight on-line network anomaly detection, which is rather meaningful for the ever-increasing network traffic and the accompanied network threats. In our future work, we will further verify and optimize our methods in terms of the concrete applications, as well as deploying it in our national backbone network to detect anomalies such as DoS, DDoS, probe, spam, etc.