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.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways’ roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Cloud-Based User Behavior Emulation Approach for Space-Ground Integrated Networks

Cyber-physical systems (CPSs) based on space-ground integrated networks (SGINs) enable CPSs to break through geographical restrictions in space. Therefore, providing a test platform is necessary for new technical verification and network security strategy evaluations of SGINs. User behavior emulation technology can effectively support the construction of a test platform. Given the inherent dynamic changes, diverse behaviors, and large-scale characteristics of SGIN users, we propose user behavior emulation technology based on a cloud platform. First, the dynamic emulation architecture for user behavior for SGINs is designed. Then, normal user behavior emulation strategy driven by the group user behavior model in real time is proposed, which can improve the fidelity of emulation. Moreover, rogue user behavior emulation technology is adopted, based on traffic replay, to perform the security evaluation. Specifically, virtual Internet Protocol (IP) technology and the epoll model are effectively integrated in this investigation to resolve the contradiction between large-scale emulation and computational overhead. The experimental results demonstrate that the strategy meets the requirement of a diverse and high-fidelity dynamic user behavior emulation and reaches the emulation scale of 100,000-level concurrent communication for normal users and 100,000-level concurrent attacks for rogue users.

Implementation of Centralized Networking System and Integrated Marketing Communication for MSMEs in Buah Batu Bandung

Network security is very important, especially for community service units because a damaged system can affect other systems. Therefore, it is necessary to design and develop a computer network system and security that’s more centralized. Promotional activities are very important to be carried out by every business from the smallest scope to even large companies, because no matter how good the quality of the products the company has, it will not be successful for sale without proper promotion. Referring to the situation and problems faced by partners, our group took the initiative to contribute by providing several alternative solutions whose goals are expected to be useful for partners, by providing training on the implementation of integrated networks and MSME database management, then providing an integrated marketing communication training, workshop on communication marketing tools that can provide a competitive advantage for MSMEs so that MSME marketing tools in Buah Batu District have superior value. With some of the solutions that we offer, the expected final results from this community service activity are network problems and database management can be resolved properly. Furthermore, MSMEs in Buah Batu District have standardized and attractive printed and digital marketing tools.

LB-DDQN for Handover Decision in Satellite-Terrestrial Integrated Networks

The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.

Huṇḍī and Nirakh Huṇḍāwan: Indic Mercantile Instruments in the Persianate Bazaar

The huṇḍī or Indic mercantile instrument integrated networks of merchants and bankers across Persianate bazaars from South Asia to Central Asia, Iran and East Africa. Merchants performed long-distance financial transactions by means of this instrument, catering to both private individuals and the state. While much has been written about the commercial use of huṇḍīs, this paper turns to the working of huṇḍīs at the interface of mercantile and state institutions, looking in particular at the Jaipur state’s collection of nirakh huṇḍāwan registers—which tracked the rate of discounting of huṇḍīs on a daily basis. Produced at the mercantile centre of Sanganer, and open to inspection by the Jaipur state, these registers lie at the intersection of commerce and governance, and of corporation and state. They reveal an ‘economically curious’ state, which accessed and used data collected and maintained by mercantile entities to make significant economic decisions.

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.