Focusing on the issue that only one instruction substitution with 5 operators and 13 substitution schemes is supported in Obfuscator Low Level Virtual Machine (OLLVM) at the instruction obfuscation level, an improved instruction obfuscation framework InsObf was proposed. InsObf, including junk code insertion and instruction substitution, was able to enhance the obfuscation effect at the instruction level based on OLLVM. For junk code insertion, firstly, the dependency of the instruction inside the basic block was analyzed, and then two kinds of junk code, multiple jump and bogus loop, were inserted to disrupt the structure of the basic block. For instruction substitution, based on OLLVM, it was expanded to 13 operators, with 52 instruction substitution schemes. The framework prototype was implemented on Low Level Virtual Machine (LLVM). Experimental results show that compared to OLLVM, InsObf has the cyclomatic complexity and resilience increased by almost four times, with a time cost of about 10 percentage points and a space cost of about 20 percentage points higher. Moreover, InsObf can provide higher code complexity compared to Armariris and Hikari, which are also improved on the basis of OLLVM, at the same order of magnitude of time and space costs. Therefore, InsObf can provide effective protection at the instruction level.
The use of Unmanned Aerial Vehicle (UAV) to continuously monitor designated areas can play a role in deterring invasion and damage as well as discovering abnormalities in time, but the fixed monitoring rules are easy to be discovered by the invaders. Therefore, it is necessary to design a random algorithm for UAV flight path. In view of the above problem, a UAV persistent monitoring path planning algorithm based on Value Function Iteration (VFI) was proposed. Firstly, the state of the monitoring target point was selected reasonably, and the remaining time of each monitoring node was analyzed. Secondly, the value function of the corresponding state of this monitoring target point was constructed by combining the reward/penalty benefit and the path security constraint. In the process of the VFI algorithm, the next node was selected randomly based on ε principle and roulette selection. Finally, with the goal that the growth of the value function of all states tends to be saturated, the UAV persistent monitoring path was solved. Simulation results show that the proposed algorithm has the obtained information entropy of 0.905 0, and the VFI running time of 0.363 7 s. Compared with the traditional Ant Colony Optimization (ACO), the proposed algorithm has the information entropy increased by 216%, and the running time decreased by 59%,both randomness and rapidity have been improved. It is verified that random UAV flight path is of great significance to improve the efficiency of persistent monitoring.
In the Service Oriented Architecture (SOA), an improved Krill Herd algorithm PRKH with adaptive crossover and random perturbation operator was proposed to solve the problem of easily falling into local optimum and high time cost in the process of service composition optimization. Firstly, a service composition optimization model was established based on Quality of Service (QoS), and the QoS calculation formulas and normalization methods under different structures were given. Then, based on the Krill Herd (KH) algorithm, the adaptive crossover probability and the random disturbance based on the actual offset were added to achieve a good balance between the global search ability and the local search ability of krill herd. Finally, through simulation, the proposed algorithm was compared with KH algorithm, Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm and Flower Pollination Algorithm (FPA). Experimental results show that the PRKH algorithm can find better QoS composite services faster.
Focusing on the fading and shadowing effect in satellite channel, a Hybrid Satellite-Terrestrial Cooperative System (HSTCS) was presented, and the closed-form expression of the outage probability was evaluated using the Land Mobile Satellite (LMS) channel. A selective Decode-and-Forward (DF) scheme was implemented between a source node (the satellite) and a destination node (a terrestrial station), and signals from the satellite and terrestrial relay were combined at destination. The analytical expression of the outage probability was verified with the Matlab simulation. The results show that the system can improve the outage performance through the diversity gain, compared with the direct transmission.