DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

Network Security Defense Decision-Making Method Based on Stochastic Game and Deep Reinforcement Learning

Aiming at the existing network attack and defense stochastic game models, most of them are based on the assumption of complete information, which causes the problem of poor applicability of the model. Based on the actual modeling requirements of the network attack and defense process, a network defense decision-making model combining incomplete information stochastic game and deep reinforcement learning is proposed. This model regards the incomplete information of the attacker and the defender as the defender’s uncertainty about the attacker’s type and uses the Double Deep Q-Network algorithm to solve the problem of the difficulty of determining the network state transition probability, so that the network system can dynamically adjust the defense strategy. Finally, a simulation experiment was performed on the proposed model. The results show that, under the same experimental conditions, the proposed method in this paper has a better convergence speed than other methods in solving the defense equilibrium strategy. This model is a fusion of traditional methods and artificial intelligence technology and provides new research ideas for the application of artificial intelligence in the field of cyberspace security.

Attack and Defense Strategy of Distribution Network Cyber-Physical System Considering EV Source-Charge Bidirectionality

In the last few years, there has been an exponential increase in the penetration of electric vehicles (EVs) due to their eco-friendly nature and ability to support bidirectional energy exchanges with the power cyber-physical system. However, the existing research only proposes energy management in terms of vehicle-to-grid (V2G) support using fleets of EVs, which lacks research on EV attacks. Motivated by these facts, this paper first introduces a new data integrity attack strategy for a consistent energy management algorithm which considers electric vehicles as energy storage. In particular, we consider EV aggregators as energy storage with source-charge bidirectional characteristics. The attacker carefully constructs false information to manipulate aggregators to participate in scheduling and obtaining additional benefits on the premise of meeting the constraints of microgrid and various devices by attacking the consistent algorithm. Then, we propose a disturbance rejection control strategy combining privacy protection protocols and an isolation mechanism. We analyze the effectiveness of the proposed encryption mechanism and verify the feasibility of the isolation control algorithm by simulation and comparison.

Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics

We formalize the adversarial process between defender and attackers as a game and study the non-cooperative evolutionary game mechanism under bounded rationality. We analyze the long-term dynamic process between the attacking and defending parties using the evolutionary stable strategies derived from the evolutionary game model. First, we construct a multi-player evolutionary game model consisting of a defender and multiple attackers, formally describe the strategies, and construct a three-player game payoff matrix. Then, we propose two punishment schemes, i.e., static and dynamic ones. Moreover, through the combination of mathematical derivation with simulation, we obtain the evolutionary stable strategies of each player. Different from previous work, in this paper, we consider the influence of strategies among different attackers. The simulation shows that (1) in the static punishment scheme, increasing the penalty can quickly control the occurrence of network attacks in the short term; (2) in the dynamic punishment scheme, the game can be stabilized effectively, and the stable state and equilibrium values are not affected by the change of the initial values.

Chronic exposure to ∆9-tetrahydrocannabinol in adolescence decreases social play behaviours

Background: Cannabis use remains a major public health concern, and its use typically begins in adolescence. Chronic administration of ∆9-tetrahydrocannabinol (THC), the main psychoactive compound in cannabis, during adolescence can produce deficits in adult learning and memory, stress reactivity and anxiety. One possible mechanism behind the disruptions in adulthood from adolescent exposure to THC includes changes in social behaviours, such as social play, which has been shown to be critical to socio-cognitive development. Methods: Here, using an established animal model of adolescent THC exposure in male and female Long–Evans rats, we explored the effects of THC on play behaviour during the chronic administration period. Following puberty onset, as indicated by external changes to the genitalia, THC (5mg/kg) was administered for 14 days. Play behaviour was assessed seven days following the onset of the injection period at approximately 1 hour post treatment. The frequency of nape attacks, the likelihood and tactics of defensive behaviour, and pins were scored and analyzed. Results: THC exposure decreased playfulness in adolescent rats including the number of attacks, likelihood of defense and pins compared to control and vehicle treated rats. Conclusion: This suggests that THC suppresses both the attack and defense components of social play. This is an important finding because there is evidence that attack and defense may be mediated by different mechanisms. Furthermore, the effect of THC exposure decreasing playfulness occurred similarly in males and females.

Lipid-Binding Aegerolysin from Biocontrol Fungus Beauveria bassiana

Fungi are the most common pathogens of insects and thus important regulators of their populations. Lipid-binding aegerolysin proteins, which are commonly found in the fungal kingdom, may be involved in several biologically relevant processes including attack and defense against other organisms. Aegerolysins act alone or together with membrane-attack-complex/perforin (MACPF)-like proteins to form transmembrane pores that lead to cell lysis. We performed an in-depth bioinformatics analysis of aegerolysins in entomopathogenic fungi and selected a candidate aegerolysin, beauveriolysin A (BlyA) from Beauveria bassiana. BlyA was expressed as a recombinant protein in Escherichia coli, and purified to further determine its functional and structural properties, including lipid-binding ability. Aegerolysins were found to be encoded in genomes of entomopathogenic fungi, such as Beauveria, Cordyceps, Metarhizium and Ophiocordyceps. Detailed bioinformatics analysis revealed that they are linked to MACPF-like genes in most genomes. We also show that BlyA interacts with an insect-specific membrane lipid. These results were placed in the context of other fungal and bacterial aegerolysins and their partner proteins. We believe that aegerolysins play a role in promoting the entomopathogenic and antagonistic activity of B. bassiana, which is an active ingredient of bioinsecticides.

Spears and shields: attacking and defending deep model co-inference in vehicular crowdsensing networks

Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge co-inference paradigm to improve the inference efficiency in the VCS network. Specifically, the vehicular device and the edge server keep a part of the deep model separately, but work together to perform the inference through sharing intermediate results. Although vehicles keep the raw data locally, privacy issues still exist once attackers obtain the shared intermediate results and recover the raw data in some way. In this paper, we validate the possibility by conducting a systematic study on the privacy attack and defense in the co-inference of VCS network. The main contributions are threefold: (1) We take the road sign classification task as an example to demonstrate how an attacker reconstructs the raw data without any knowledge of deep models. (2) We propose a model-perturbation defense to defend against such attacks by injecting some random Laplace noise into the deep model. A theoretical analysis is given to show that the proposed defense mechanism achieves $$\epsilon$$ ϵ -differential privacy. (3) We further propose a Stackelberg game-based incentive mechanism to attract the vehicles to participate in the co-inference by compensating their privacy loss in a satisfactory way. The simulation results show that our proposed defense mechanism can significantly reduce the effects of the attacks and the proposed incentive mechanism is very effective.