Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to...Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.展开更多
To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theor...To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theory has merits of fewer requirements on original data scale, less limitation of the distribution pattern and simpler algorithm in modeling. With these merits GTIDS constructs model according to partial time sequence for rapid detect on intrusive act in secure system. In this detection model rate of false drop and false retrieval are effectively reduced through twice modeling and repeated detect on target data. Furthermore, GTIDS framework and specific process of modeling algorithm are presented. The affectivity of GTIDS is proved through emulated experiments comparing snort and next-generation intrusion detection expert system （NIDES） in SRI international.展开更多
The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- s...The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- sures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions, the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.展开更多
针对高维网络数据存在大量冗余和不相关的特征导致入侵检测准确率低的问题,提出了一种改进的多因子优化蝙蝠算法(IMFBA)用于数据特征选择,筛选出具有最大信息量的特征子集,提高网络入侵检测精度。首先,在多因子优化框架下设计全局特征...针对高维网络数据存在大量冗余和不相关的特征导致入侵检测准确率低的问题,提出了一种改进的多因子优化蝙蝠算法(IMFBA)用于数据特征选择,筛选出具有最大信息量的特征子集,提高网络入侵检测精度。首先,在多因子优化框架下设计全局特征选择任务和局部特征选择任务,并通过基于蝙蝠算法所设计的选型交配和垂直文化传播算子实现不同任务间的信息共享,从而帮助全局特征选择任务更快锁定最优解空间,提高算法收敛速度和稳定性。其次,通过将反向学习策略和差分进化引入蝙蝠算法,重新设计算法初始解选择阶段及个体更新过程,弥补其缺少突变机制的不足,增强解的多样性,帮助算法摆脱局部最优。最后,提出一种自适应参数调整策略,根据潜在最优解质量决定其指导个体更新的权重,避免在多任务特征选择过程中出现知识负迁移现象,实现全局搜索与局部开发之间的平衡。实验结果表明:IMFBA所选特征子集对网络入侵数据集KDD CUP 99和NSL-KDD分类结果的准确率分别为95.37%和85.14%,相较于完整特征集提升了3.01百分点和9.78百分点。IMFBA算法能选择更高质量特征子集并提升网络入侵检测准确率。展开更多
为解决物联网设备资源受限、平衡流量检测精度与时间开销等问题,提出一种FastSplit-RF(random forest with fast split)的轻量化分类算法。针对物联网流量设计一个通用的特征提取流程,在随机森林算法基础上,使用多臂赌博机策略代替节点...为解决物联网设备资源受限、平衡流量检测精度与时间开销等问题,提出一种FastSplit-RF(random forest with fast split)的轻量化分类算法。针对物联网流量设计一个通用的特征提取流程,在随机森林算法基础上,使用多臂赌博机策略代替节点分裂的遍历过程,实现对节点的快速分割,完成高效、轻量化的物联网流量分类。实验验证,FastSplit-RF相较随机森林算法,在准确率提升了2.45%的同时,检测速度增快了62.16%,内存占用减小了48.68%。展开更多
基金the National High Technology Development "863" Program of China (2006AA01Z436, 2007AA01Z452)the National Natural Science Foundation of China(60702042).
文摘Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.
文摘To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theory has merits of fewer requirements on original data scale, less limitation of the distribution pattern and simpler algorithm in modeling. With these merits GTIDS constructs model according to partial time sequence for rapid detect on intrusive act in secure system. In this detection model rate of false drop and false retrieval are effectively reduced through twice modeling and repeated detect on target data. Furthermore, GTIDS framework and specific process of modeling algorithm are presented. The affectivity of GTIDS is proved through emulated experiments comparing snort and next-generation intrusion detection expert system （NIDES） in SRI international.
基金This project was supported by the National Natural Science Foundation of China (60672068)the National High Technology Development 863 Program of China (2006AA01Z436, 2007AA01Z452.)
文摘The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- sures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions, the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.
文摘针对高维网络数据存在大量冗余和不相关的特征导致入侵检测准确率低的问题,提出了一种改进的多因子优化蝙蝠算法(IMFBA)用于数据特征选择,筛选出具有最大信息量的特征子集,提高网络入侵检测精度。首先,在多因子优化框架下设计全局特征选择任务和局部特征选择任务,并通过基于蝙蝠算法所设计的选型交配和垂直文化传播算子实现不同任务间的信息共享,从而帮助全局特征选择任务更快锁定最优解空间,提高算法收敛速度和稳定性。其次,通过将反向学习策略和差分进化引入蝙蝠算法,重新设计算法初始解选择阶段及个体更新过程,弥补其缺少突变机制的不足,增强解的多样性,帮助算法摆脱局部最优。最后,提出一种自适应参数调整策略,根据潜在最优解质量决定其指导个体更新的权重,避免在多任务特征选择过程中出现知识负迁移现象,实现全局搜索与局部开发之间的平衡。实验结果表明:IMFBA所选特征子集对网络入侵数据集KDD CUP 99和NSL-KDD分类结果的准确率分别为95.37%和85.14%,相较于完整特征集提升了3.01百分点和9.78百分点。IMFBA算法能选择更高质量特征子集并提升网络入侵检测准确率。
文摘为解决物联网设备资源受限、平衡流量检测精度与时间开销等问题,提出一种FastSplit-RF(random forest with fast split)的轻量化分类算法。针对物联网流量设计一个通用的特征提取流程,在随机森林算法基础上,使用多臂赌博机策略代替节点分裂的遍历过程,实现对节点的快速分割,完成高效、轻量化的物联网流量分类。实验验证,FastSplit-RF相较随机森林算法,在准确率提升了2.45%的同时,检测速度增快了62.16%,内存占用减小了48.68%。
