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At present, there are many techniques for cyber security defense such as firewall, intrusion detection and cryptography. Despite decades of studies and experiences on this issue, there still exists a problem that we always pay great attention to technology while overlooking strategy. In the traditional warfare, the level of decision-making and the formulation of optimal strategies have a great effect on the warfare result. Similarly, the timeliness and quality of decision-making in cyber attack-defense also make great significance. Since the attackers and defenders are oppositional, the selection of optimal defense strategy with the maximum payoff is difficult. To solve this problem, the stochastic evolutionary game model is utilized to simulate the dynamic adversary of cyber attack-defense. We add the parameter $\lambda $ to the Logit Quantal Response Dynamics (LQRD) equation to quantify the cognitive differences of real-world players. By calculating the evolutionary stable equilibrium, the best decision-making approach is proposed, which makes a balance between defense cost and benefit. Cases studies on ransomware indicate that the proposed approach can help the defender predict possible attack action, select the related optimal defense strategy over time, and gain the maximum defense payoff. [ABSTRACT FROM AUTHOR]

To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.

Attack test and defense verification are important ways to effectively evaluate the cybersecurity performance of Intelligent Connected Vehicle (ICV). This paper investigates the problem of attack-defense visualization in ICV cybersecurity. For the purpose of promoting cybersecurity research capabilities, a novel Cybersecurity Attack-Defense Visualization method based on Intelligent Connected Vehicle (CADV-ICV) is proposed. In this scheme, an Attack-Defense Game model (ADG) is designed so that the logical relationship between the attack and defense can be studied through a system architecture. Then, the CADV-ICV method is implemented through three layers that are hardware layer, software layer and visualization layer. Finally, through an Intelligent Connected Vehicle, two TV monitors, a computer and a server, a real experimental environment is built to test the CADV-ICV method. The experimental results show that CADV-ICV can realize the visual display of attack-defense process, attack messages, defense state, real-time message monitoring, and attack-defense principle for 10 car’s components.

Multimedia communication network has gained remarkable popularity by a wide spectrum of users nowadays. It is easier that the potential threats conceal within the large-scale net flow of multimedia communication traffic. Once vulnerability exploitation occurs, the latent risk will be brought to the surface, causing a series of safety problems. Thus, the vulnerability analysis and threat prediction are becoming critical issues. Recently years, many investigations have been made. However, they are not sufficient. To provide a comprehensive view of the threat scenario and present a quantitative risk-aware approach, we propose a novel method for threat identification, and further we build a quantitative security risk model with it. Actually, two algorithms are proposed, namely dynamic Bayesian attack graph based threat prediction algorithm, and threat prediction based security risk quantification algorithm. The first algorithm aims to provide full prediction information with threat scenario. The second algorithm quantifies the threat in the first algorithm into the security risk from two levels: host and network. The examples indicate that our method is feasible and scalable, which enables a manager to quantify the risks of any identified threat or ongoing attack and to recognize the vulnerable multimedia devices to keep secure multimedia communication. [ABSTRACT FROM AUTHOR]

Mice (BALB/C or NIH, C57 BR/cdJ and A/HeJ) were isolated for 2 weeks then exposed to another mouse for 10 min daily for 1 week. Isolated, unexposed and grouped controls were also studied. Aggression, defined as stereotyped attack behavior, was produced only in the NIH mice which were subsequently used as aggressors. When attacked, the victims, C57 BR/cdJ mice defended themselves whereas A/HeJ mice remained submissive. Isolation alone did not alter adrenal medullary levels of tyrosine hydroxylase (TOH), phenylethanolamine-N-methyltransferase (PNMT) or catecholamines. NIH mice showed an increase in TOH but not in PNMT when they fought with NIH or C57 BR/cdJ mice, whereas both victims showed increases in TOH and PNMT. It was concluded therefore that fright or active fighting induces increases in adrenal medullary enzymes. Drugs were injected into C57 BR/cdJ mice at doses which did not impair their defense behavior, using untreated NIH mice as aggressors. Phenobarbital fully inhibited both TOH and PNMT rise while chlorpromazine only partly suppressed PNMT increase. Methamphetamine was less effective than phenobarbital. Tranylcypromine increased both enzymes in control animals and partly suppressed TOH rise induced by defense. Thus, a reliable model of mild stress was produced which appears suitable for screening psychoactive drugs. [ABSTRACT FROM AUTHOR]