Studies on controllability of directed networks with extremal optimization

Almost all natural, social and man-made-engineered systems can be represented by a complex network to describe their dynamic behaviors. To make a real-world complex network controllable with its desired topology, the study on network controllability has been one of the most critical and attractive subjects for both network and control communities. In this paper, based on a given directed–weighted network with both state and control nodes, a novel optimization tool with extremal dynamics to generate an optimal network topology with minimum control nodes and complete controllability under Kalman’s rank condition has been developed. The experimental results on a number of popular benchmark networks show the proposed tool is effective to identify the minimum control nodes which are sufficient to guide the whole network’s dynamics and provide the evolution of network topology during the optimization process. We also find the conclusion: “the sparse networks need more control nodes than the dense, and the homogeneous networks need fewer control nodes compared to the heterogeneous” (Liu et al., 2011  [18]), is also applicable to network complete controllability. These findings help us to understand the network dynamics and make a real-world network under the desired control. Moreover, compared with the relevant research results on structural controllability with minimum driver nodes, the proposed solution methodology may also be applied to other constrained network optimization problems beyond complete controllability with minimum control nodes.     

Interference Cancellation and Avoidance for Secondary Users Co-existing with TDD-based Primary Systems

This paper proposes a new interference cancellation/avoidance scheme for secondary spectrum usage, which takes advantage of the features of Time Division Duplex (TDD) based primary systems. We focus on the co-existence between the heterogeneous systems, where the primary and the secondary are using the same spectrum simultaneously. The secondary systems have to accurately find the possible interference from/to the primary systems for discreet and efficient secondary transmission. In order to judge the arrival of interference from/to the primary system, the secondary access point (AP) compares the difference in power between the up and downlink signals in TDD-based primary systems. This scheme enables the secondary AP to know the level of interference from the primary systems that arrives at the AP without employing complex signal detection. The proposed scheme also determines the level of interference induced to the primary system by measuring the difference in power. To cancel and avoid interference in the proposed scheme, we introduce an adaptive array at only the AP that creates a null toward the interference with a higher power level between the up and downlink signals. Moreover, new algorithms by using scheduling information of the multiple primary nodes are introduced in the proposed method, in order to enhance the transmission quality of a secondary mobile station (MS) while maintaining a simple control scheme for the MS. Finally, we verify the effectiveness of the proposed method based on computer simulations.

基于Android的手机恶意代码检测与防护技术

随着通信技术以及移动终端的发展,Android系统由于其本身的开源性,滋生了大量的恶意代码。为了满足Android手机用户的安全需求,文中基于Android,采用SVM机器学习思想,构建了恶意代码检测模型,并开发了一套手机恶意代码检测与防护系统,可以对其进行快速检测和深度检测。系统经Android手机测试结果表明,其具有较好的检测精度以及较低的恶意代码漏报率。     

微型水声传感网水下传感器节点设计

针对在海洋环境监测系统中水声传感网高传输速率、高可靠性、长时间工作的要求,文中设计了一种低功耗的微型水下传感器节点。设计采用较高的能量转换率和接收灵敏度的换能器T235,并且在发送单元使用变压器进行阻抗匹配以使信号功率最大化传输。接收单元采用8阶巴特沃斯带通滤波器,可以更好地滤除噪声。软件上,移植并且优化了MAC协议,减少撞包和重发的概率。测试结果显示,在小规模范围内,传输速率在19 200 bit·s ^-1时,系统的总功耗低于20 W,能达到90%以上的通信传输成功率。     

On the resilience of P2P botnet footprints in the presence of legitimate P2P traffic

Botnet is a distributed platform for illegal activities severely threaten the security of the Internet. Fortunately, although their complicated nature, bots leave some footprints during the C&C communication that have been utilized by security researchers to design detection mechanisms. Nevertheless, botnet designers are always trying to evade detection systems by leveraging the legitimate P2P protocol as C&C channel or even mimicking legitimate peer‐to‐peer (P2P) behavior. Consequently, detecting P2P botnet in the presence of normal P2P traffic is one of the most challenging issues in network security. However, the resilience of P2P botnet detection systems in the presence of normal P2P traffic is not investigated in most proposed schemes. In this paper, we focused on the footprint as the most essential part of a detection system and presented a taxonomy of footprints utilized in behavioral P2P botnet detection systems. Then, the resilience of mentioned footprints is analyzed using three evaluation scenarios. Our experimental and analytical investigations indicated that the most P2P botnet footprints are not resilient to the presence of legitimate P2P traffic and there is a pressing need to introduce more resilient footprints.     

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust‐based schemes before they can damage the network. In this paper, we propose an efficient weighted trust‐based malicious node detection (WT‐MND) scheme that can detect malicious nodes in a clustered WSN. The node behaviors are realistically treated by accounting for false‐positive and false‐negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT‐MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trust‐update process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust‐update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network's size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads.     

恶意网址拦截技术的应用研究

在新技术、新应用的不断刺激下,各类网站应用服务如雨后春笋般不断涌现。伴随着互联网+产业的蓬勃发展,网络安全形势日益严峻。钓鱼电子邮件攻击持续高发、仿冒网站窃取用户个人隐私信息及诈骗用户钱财等时刻威胁着用户的合法权益。这种情况下,迫切需要建立一套全网恶意网址综合防范治理体系。因此,提出了一种针对多种应用场景下的恶意网址拦截方法,通过基于DNS Forward的恶意网址拦截、恶意短网址拦截、“拆链”技术在恶意网址拦截中的应用等技术手段,综合治理全网恶意网址访问行为。不仅提升了全网防护效果,而且有效减少了全网恶意访问流量。     

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.     

HTTP malicious traffic detection method based on hybrid structure deep neural network

In response to the HTTP malicious traffic detection problem,a preprocessing method based on cutting mechanism and statistical association was proposed to perform statistical information correlation as well as normalization processing of traffic.Then,a hybrid neural network was proposed based on the combination of raw data and empirical feature engineering.It combined convolutional neural network (CNN) and multilayer perceptron (MLP) to process text and statistical information.The effect of the model was significantly improved compared with traditional machine learning algorithms (e.g.,SVM).The F₁value reached 99.38% and had a lower time complexity.At the same time,a data set consisting of more than 450 000 malicious traffic and more than 20 million non-malicious traffic was created.In addition,prototype system based on model was designed with detection precision of 98.1%~99.99% and recall rate of 97.2%~99.5%.The application is excellent in real network environment.