Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

基于性能驱动的船载稳定平台尺度参数优化设计

为提升船载稳定平台的运动学性能,针对■并联机构的尺度参数优化问题,以机构的工作空间体积和全域力传递率为综合评价指标,采用小生境遗传算法优化得到稳定平台的最佳几何构形。具体地,采用数值法与解析法相结合的方式判断支链长度、关节转角、奇异位形等约束条件的生效情况,求解出并联稳定平台的工作空间;基于力雅可比矩阵逆矩阵的最小奇异值定义机构的局部力传递性能,以工作空间内局部力传递率的平均值作为全域力传递性能评价指标;以工作空间体积和全域力传递率的加权和为优化目标,采用小生境自适应遗传算法完成优化求解,获得最优尺度参数。与初始构形的性能对比分析表明,优化构形在力传递性能方面有35%的提升,具有更好的综合运动学性能。制作试验样机并完成相关实验,验证了所提尺度参数优化方法的有效性。最后探讨了多目标优化过程中不同的权重系数取值对优化结果的作用规律,发现选用均衡的权重可获得更佳的综合性能。     

基于改进的GABP算法优化5G天线阵列

为了加快天线建模速度,针对现有天线设计软件天线参数优化速度过慢问题进行了建模研究。首先通过几种常用的启发式算法优化后的多层前馈（Back propagation,BP）神经网络对天线参数进行优化比较,并对其中最优的算法遗传算法优化BP（Genetic algorithm BP,GABP）神经网络算法进行深度改进。其次采用自适应算法和模拟退火算法优化算法对GABP进行优化。最终通过模拟试验验证出自适应GABP算法对于天线参数优化的误差最小。该研究为天线设计软件中天线优化方法提供了一种误差较小的新方法,拥有更高的预测准确度,拟合速度也大大提升。实验对比证明了该算法的可行性。     

Deadline-aware multi-objective IoT services placement optimization in fog environment using parallel FFD-genetic algorithm

Nowadays, Internet of things has become as an inevitable aspect of humans’ IT-based life. A huge number of geo-distributed IoT enabled devices such as smart phones, smart cameras, health care systems, vehicles, etc. are connected to the Internet and manage users’ applications. The IoT applications are generally time sensitive, so that giving them up to Cloud and receiving the response may violate their required deadline, due to distance between user device and centralized Cloud data center and consequently increasing network latency. Fog environment, as an intermediate layer between Cloud and IoT devices, brings a smaller scales of Cloud capabilities closer to user location. Processing real time applications in Fog layer helps more deadlines to be met. Although Fog computing enhances quality of service parameters, limited resources and power of Fog nodes is a challenge in processing applications. Furthermore, the network latency is still an issue for communications between applications’ services and between user device and Fog node, which seriously threatens deadline condition. Regarding to mentioned points, this paper proposes a 3-partite deadline-aware applications’ services placement optimization model in Fog environment which optimizes total power consumption, total resources wastage, and total network latency, simultaneously. The proposed model prioritizes applications in 3 levels based on their associated deadline, and then the model is solved using a parallel model of first fit decreasing and genetic algorithm combination. Simulations results indicates the superiority of proposed approach against counterpart algorithms in terms of reducing power consumption, resource wastage, network latency, and service rejection rate.     

基于量子重力探测技术的水下匹配导航仿真分析与评估

在电磁信号拒止的海洋环境中,基于海洋重力场辅助的水下匹配导航技术是提高潜航器导航定位精度的有效途径之一。近年来,基于冷原子干涉的量子绝对重力探测技术发展迅速,其探测精度高、可移动性强,在水下重力辅助导航领域具有极大的应用潜力。结合冷原子干涉量子重力仪最新技术发展水平,选取太平洋某典型海域,通过地形正演构建了高精度（<0.1mGal）、高分辨率（500m）海洋重力基准图,基于最近等值线迭代（Iterative Closest Contour Point,ICCP）算法实现了对潜航器水下航行误差的重力场辅助匹配校正,将初始航迹误差从约4km校正到约400m。匹配算法具有指数收敛优势,在阈值收敛误差为10-6下,算法平均收敛迭代次数44次,平均收敛时间0.0678s。通过仿真分析和“探测技术-重力场基准图-匹配算法”系统综合效能评估,论证了现有量子重力探测技术在重力辅助水下匹配导航领域的应用潜能。     

基于云模型和余弦跳跃权重的改进蛙跳算法

标准蛙跳优化算法(SFLA)有寻优精度低和易陷入局部收敛区域的缺点, 为提高其性能, 提出一种基于云模型局部搜索和余弦跳跃权重更新位置的改进蛙跳算法(CSFLA). 首先通过Tent混沌映射和反向学习生成种群, 使种群的分布更均匀, 利用云模型的正态特性对子群中的优秀个体的所在区域进行探索. 同时, 对种群中其他个体引入基于余弦函数的跳跃步长权重, 使该权重在整个迭代过程中由高以不同的速率下降, 提高种群的全局搜索能力. 最后, 将CSFLA与多个优化算法在不同类型的测试函数上进行了比较. 结果表明, CSFLA具有更好的收敛速度和精度, 能有效找出全局最优解. 并且将改进算法应用至旅行商问题, 该算法可以找到总路程更短的路线.     

混合遗传蚁群算法优化BP神经网络预测空气质量

为了进一步提高空气质量指数预测精度,提出一种混合遗传蚁群算法优化BP神经网络的方式对空气质量指数进行预测.首先初始化蚁群算法的信息素分布,对不满足适应度条件的进行遗传算法的交叉、变异操作,进而计算蚁群的状态转移概率和信息素浓度,当适应度值满足条件要求时,将寻优结果作为BP神经网络的最优权值和阈值,来改善单一BP神经网络的不足.最后通过运用西安市的空气质量指数日历史数据进行验证,实验表明,本文所提模型的各个评价指标相对其他对比模型误差更小,在预测精度方面具有更高的说服力,因此能够有效地预测空气质量指数.     

基于SSA-SVM的网络入侵检测研究

互联网快速发展使得网络空间越来越复杂,网络入侵导致网络安全问题备受关注。为提升网络入侵的检测效率和精度,构建了基于支持向量机的网络入侵检测模型。支持向量机模型的惩罚系数和核函数参数直接影响入侵模型的检测精度,采用麻雀搜索算法对惩罚系数和核函数参数进行优化,提出了基于麻雀搜索算法和支持向量机的网络入侵检测模型。将提出的网络入侵检测模型应用于实际的网络入侵检测中,并与PSO-SVM和SVM模型进行对比。结果表明,所提出的网络入侵检测模型能够有效降低网络入侵的误报率,这对确保网络安全具有一定的现实意义。     

多智能体强化学习的机械臂运动控制决策研究

针对传统运动控算法存在环境适应性较差,效率低的问题。可以利用强化学习在环境中不断去探索试错,并通过奖励函数对神经网络参数进行调节的方法对机械臂的运动进行控制。但是在现实中无法提供机械臂试错的环境,采用Unity引擎平台来构建机械臂的数字孪生仿真环境,设置观察状态变量和设置奖励函数机制,并提出在该模型环境中对PPO(proximal policy optimization)与多智能体（agents）结合的M-PPO算法来加快训练速度,实现通过强化学习算法对机械臂进行智能运动控制,完成机械臂执行末端有效避障快速到达目标物体位置,并通过该算法与M-SAC（多智能体与Soft Actor-Critic结合）和PPO算法的实验结果进行分析,验证M-PPO算法在不同环境下机械臂运动控制决策调试上的有效性与先进性。实现孪生体自主规划决策,反向控制物理体同步运动的目的。     

基于共享近邻和优化关联策略的边界剥离聚类

边界剥离聚类算法(BP)是一种基于密度的聚类算法,它通过逐渐剥离边界点来揭示聚类的潜在核心,已经被证明是一种十分有效的聚类手段.然而, BP算法仍存在一些不足之处:一方面,数据点的局部密度仅考虑了距离特征,使得边界点的确定不够合理;另一方面, BP算法中的关联策略容易误判异常值,并且在分配边界点时容易产生连带错误.为此,本文提出了一种基于共享近邻和优化关联策略的边界剥离聚类算法(SOBP).该算法使用了基于共享近邻的局部密度函数来更好地探索数据点之间的相似性,同时优化了BP算法中的关联策略,使得每次迭代中边界点不再仅与一个非边界点进行关联,并进一步采用了边界点与非边界点、已剥离边界点之间的双重关联准则.在一些数据集上的测试表明,相较于其他6种经典算法,该算法在评估指标上表现更佳.