Adaptive optimization of agile organization of command and control resource

Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put forward by analyzing the interrelating concept and research. The model takes the adaptive process as a multi-stage decision making problem. The 2-phases method is presented to calculate the model, which obtains the related parameters by running the colored Petri net （CPN） model of AOC2R and then searches for the result by ant colony optimization （ACO） algorithm integrated with genetic optimization techniques. The simulation results demonstrate that the proposed algorithm greatly improves the performance of AOC2R.

Command and Control Cities in Global Space-economy Before and after 2008 Geo-economic Transition

The global economic downturn caused primarily by the US sub-prime mortgage crisis in 2007 engendered revenue loss of the multinational corporations. Existing studies have yet to depict the detrimental impacts on city＇s command and control functions induced by the sub-prime mortgage crisis together with its residual wave of global economic recession on the global spatial economy. Recent and previous studies have produced an ＇instant history＇ of the global spatial economy before the global economic downturn undermines the global economy in late 2008. How- ever, the waxes and wanes of major cities＇ command and control functions on the global economic arena before and after the outbreak of financial crisis and its associated geo-economic transitions are still poorly understood. This paper attempts to contribute a new set of customized data to update and fill in the gap in the literature with the investigation of the command and control functions of cities arotmd the world from 2005 to 2009. Particular attentions are paid to the time-space relationship of the geo-economic transition that can capture the recent historical images of the com- mand and control situation of different cities in the world.

One-to-Any Command and Control Model:Precisely Coordinated Operation on Uncooperative Controlled Nodes

New precisely cooperative attacks, such as the coordi- nated cross plane session termination （CXPST） attack, need thou- sands upon thousands machines to attack diverse selected links simultaneously with the given rate. However, almost all command and control（C＆C） mechanisms only provide publishing one com- mand to the whole once, so-called one-to-all C＆C model, and are not productive to support CXPST-alike attacks. In this paper, we present one-to-any C＆C model on coordination among the unco- operative controlled nodes. As an instance of one-to-any C＆C model, directional command publishing （DCP） mechanism lever- aging on Kademlia is provided with a range-mapping key creating algorithm for commands to compute the publishing range and a statistically stochastic node querying scheme to obtain the com- mands immediately. With theoretical analysis and simulation, it is indicated that one-to-any C＆C model fits for precisely coordi- nated operation on uncooperative controlled nodes with least complexity, better accuracy and efficiency. Furthermore, DCP mechanism can support one-to-all command publishing at the same time. As an example of future C＆C model, studying on one-to-any C＆C model may help to promote the development of more efficient countermeasures.

Modeling of combined Bayesian networks and cognitive framework for decision-making in C2

The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.

Analysis of the relationship between environmental policies and air quality during major social events

Based on an analysis of major recent social events in China,such as the 2008 Beijing Olympic Games,2010 Shanghai World Expo,2014 Nanjing Youth Olympic Games,and 2014 Beijing Asia Pacific Economic Cooperation Conference,mandatory,temporary,and indemnificatory quantitative policies aimed at protecting air quality,which were carried out by both central and local governments,made substantial contributions to changes and improvements in air quality.To some extent,the findings show that command and control measures play a significant role in protecting air quality,while the law and economic and voluntary environmental safeguard measures do not during major social events.Therefore,it reminds us the air quality could and will improve not only during major social events but also in the regular days if we implement appropriate environmental policies and safeguard measures.

A Learning Evasive Email-Based P2P-Like Botnet

Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.

无线通信“N+1维”内生抗干扰理论与技术

The existing theory and techniques of wireless communication anti-jamming have reached their performance limit recently.With this focus,by leveraging the inherent characteristics of wireless communication and referring to the principle of cyberspace endogenous security,this paper investigates the core issues of endogenous security in the electromagnetic space,namely,endogenous anti-jamming(EAJ),which can defend against unknown electromagnetic attacks effectively.Specifically,the subspace method is first adopted to establish the unified framework for the conventional spread-spectrum,intelligent,and endogenous antijamming,in which both the intrinsic development law of each technique and the internal logic between them are revealed.Then,the fundamental concept,key techniques,and development suggestions of wireless communication“N+1 dimensionality”endogenous anti-jamming are proposed to seek a disruptive breakthrough.

Evolutionary decision-makings for the dynamic weapon-target assignment problem

The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.