Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems

The rapid development of internet of things (IoT) is to be the next generation of the IoT devices are a simple target for attackers due to the lack of security. Attackers can easily hack the IoT devices that can be used to form botnets, which can be used to launch distributed denial of service (DDoS) attack against networks. Botnets are the most dangerous threat to the security systems. Software-defined networking (SDN) is one of the developing filed, which introduce the capacity of dynamic program to the network. Use the flexibility and multidimensional characteristics of SDN used to prevent DDoS attacks. The DDoS attack is the major attack to the network, which makes the entire network down, so that normal users might not avail the services from the server. In this article, we proposed the DDoS attack detection model based on SDN environment by combining support vector machine classification algorithm is used to collect flow table values in sampling time periods. From the flow table values, the five-tuple characteristic values extracted and based on it the DDoS attack can be detected. Based on the experimental results, we found the average accuracy rate is 96.23% with a normal amount of traffic flow. Proposed research offers a better DDoS detection rate on SDN.     

一种防御DDoS攻击的软件定义安全网络机制

软件定义网络的出现为防御DDoS攻击提供了新的思路.首先,从网络体系结构角度建模分析了DDoS攻击所需的3个必要条件：连通性、隐蔽性与攻击性;然后,从破坏或限制这些必要条件的角度出发,提出了一种能够对抗DDoS攻击的软件定义安全网络机制SDSNM（software defined security networking mechanism）.该机制主要在边缘SDN网络实现,同时继承了核心IP网络体系架构,具有增量部署特性.利用云计算与Chord技术设计实现了原型系统,基于原型系统的测量结果表明,SDSNM具有很好的扩展性和可用性.     

基于软件定义物联网的分布式拒绝服务攻击检测方法

由于物联网（IoT）设备众多、分布广泛且所处环境复杂,相较于传统网络更容易遭受分布式拒绝服务（DDoS）攻击,针对这一问题提出了一种在软件定义物联网（SD-IoT）架构下基于均分取值区间长度-K均值（ELVR-Kmeans）算法的DDoS攻击检测方法。首先,利用SD-IoT控制器的集中控制特性通过获取OpenFlow交换机的流表,分析SD-IoT环境下DDoS攻击流量的特性,提取出与DDoS攻击相关的七元组特征;然后,使用ELVR-Kmeans算法对所获取的流表进行分类,以检测是否有DDoS攻击发生;最后,搭建仿真实验环境,对该方法的检测率、准确率和错误率进行测试。实验结果表明,该方法能够较好地检测SD-IoT环境中的DDoS攻击,检测率和准确率分别达到96.43%和98.71%,错误率为1.29%。     

基于SDN的DDoS攻击防御系统

软件定义网络（SDN）是一种新兴网络架构,通过将转发层和控制层分离,实现网络的集中管控。控制器作为SDN网络的核心,容易成为被攻击的目标,分布式拒绝服务（DDoS）攻击是SDN网络面临的最具威胁的攻击之一。针对这一问题,本文提出一种基于机器学习的DDoS攻击检测模型。首先基于信息熵监控交换机端口流量来判断是否存在异常流量,检测到异常后提取流量特征,使用SVM+K-Means的复合算法检测DDoS攻击,最后控制器下发丢弃流表处理攻击流量。实验结果表明,本文算法在误报率、检测率和准确率指标上均优于SVM算法和K-Means算法。     

SDN环境下基于DBN的DDoS攻击检测

软件定义网络(SDN)作为新型网络架构模式,其安全威胁主要来自DDoS攻击,建立高效的DDoS攻击检测系统是网络安全管理的重要内容.在SDN环境下,针对DDoS的入侵检测算法具有支持协议少、实用性差等缺陷,为此,提出一种基于深度信念网络(DBN)的DDoS攻击检测算法.分析SDN环境下DDoS攻击的机制,通过Mininet模拟SDN的网络拓扑结构,并使用Wireshark完成DDoS流量数据包的收集和检测.实验结果表明,与XGBoost、随机森林、支持向量机算法相比,该算法具有攻击检测准确性高、误报率低、检测速率快和易于扩展等优势,综合性能较好.     

软件定义网络:安全模型、机制及研究进展

软件定义网络(software defined networking,简称SDN)初步实现了网络控制面与数据面分离的思想,然而在提供高度开放性和可编程性的同时,网络自身也面临着诸多安全问题,从而限制了SDN在很多场景下的大规模部署和应用.首先对SDN的架构和安全模型进行分析;其次,从"SDN特有/非特有的典型安全问题"和"SDN各层/接口面临的安全威胁"两方面,对SDN中存在的典型安全威胁和安全问题进行分析和归纳;随后从6个方面对现有SDN安全问题的主要解决思路及其最新研究进展分别进行探讨,包括SDN安全控制器的开发、控制器可组合安全模块库的开发和部署、控制器Do S/DDo S攻击防御方法、流规则的合法性和一致性检测、北向接口的安全性和应用程序安全性;最后对SDN安全方面的标准化工作进行了简要分析,并对SDN安全方面未来的研究趋势进行了展望.     

Enhancing the security of blockchain-based software defined networking through trust-based traffic fusion and filtration

With the rapid development of Internet-of-Things (IoT), more smart devices can be connected to the Internet, resulting in a dramatic increase of data transmission and communication. Software-Defined Networking (SDN), which separates the control planes and data planes, is considered as a promising solution to provide the scale and versatility necessary for IoT. However, SDN still suffers from several challenges, i.e., the centralized control plane would be a single point of failure. With the wide adoption of blockchain applications, such technologies can have a positive impact on SDN’s performance, i.e., blockchains allow non-confident individuals to interact with each other without the need for a central authority. However, attackers can still inject traffic to influence blockchain nodes from normal operations. Motivated by the recent development of blockchains and SDN, in this work, we focus on blockchain-based SDN and develop BSDNFilter, an IDS-based security mechanism that builds a trust-based filtration by using traffic fusion and aggregation to handle and reduce malicious traffic. Through collaborating with an IT organization, our evaluation in a real blockchain-based SDN environment demonstrates that our BSDNFilter is able to achieve better filtration performance against flooding attacks than similar approaches.     

IoTGuardEye:一种面向物联网服务的Web攻击检测方法

在包括物联网(Internet of Things,IoT)设备的绝大部分边缘计算应用中,基于互联网应用技术(通常被称为Web技术)开发的应用程序接口(Application Programming Interface,API)是设备与远程服务器进行信息交互的核心。相比传统的Web应用,大部分用户无法直接接触到边缘设备使用的API,使得其遭受的攻击相对较少。但随着物联网设备的普及,针对API的攻击逐渐成为热点。因此,文中提出了一种面向物联网服务的Web攻击向量检测方法,用于对物联网服务收到的Web流量进行检测,并挖掘出其中的恶意流量,从而为安全运营中心(Security Operation Center,SOC)提供安全情报。该方法在对超文本传输协议(Hypertext Transfer Protocol,HTTP)请求的文本序列进行特征抽取的基础上,针对API请求的报文格式相对固定的特点,结合双向长短期记忆网络(Bidirectional Long Short-Term Memory,BLSTM)实现对Web流量的攻击向量检测。实验结果表明,相比基于规则的Web应用防火墙(Web Application Firewall,WAF)和传统的机器学习方法,所提方法针对面向物联网服务API的攻击具有更好的识别能力。     

Robust and efficient detection of DDoS attacks for large-scale internet

In recent years, distributed denial of service (DDoS) attacks have become a major security threat to Internet services. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. In this paper, we propose a novel framework to robustly and efficiently detect DDoS attacks and identify attack packets. The key idea of our framework is to exploit spatial and temporal correlation of DDoS attack traffic. In this framework, we design a perimeter-based anti-DDoS system, in which traffic is analyzed only at the edge routers of an internet service provider (ISP) network. Our framework is able to detect any source-address-spoofed DDoS attack, no matter whether it is a low-volume attack or a high-volume attack. The novelties of our framework are (1) temporal-correlation based feature extraction and (2) spatial-correlation based detection. With these techniques, our scheme can accurately detect DDoS attacks and identify attack packets without modifying existing IP forwarding mechanisms at routers. Our simulation results show that the proposed framework can detect DDoS attacks even if the volume of attack traffic on each link is extremely small. Especially, for the same false alarm probability, our scheme has a detection probability of 0.97, while the existing scheme has a detection probability of 0.17, which demonstrates the superior performance of our scheme.     

DDoS attack protection in the era of cloud computing and Software-Defined Networking

Cloud computing has become the real trend of enterprise IT service model that offers cost-effective and scalable processing. Meanwhile, Software-Defined Networking (SDN) is gaining popularity in enterprise networks for flexibility in network management service and reduced operational cost. There seems a trend for the two technologies to go hand-in-hand in providing an enterprise’s IT services. However, the new challenges brought by the marriage of cloud computing and SDN, particularly the implications on enterprise network security, have not been well understood. This paper sets to address this important problem.We start by examining the security impact, in particular, the impact on DDoS attack defense mechanisms, in an enterprise network where both technologies are adopted. We find that SDN technology can actually help enterprises to defend against DDoS attacks if the defense architecture is designed properly. To that end, we propose a DDoS attack mitigation architecture that integrates a highly programmable network monitoring to enable attack detection and a flexible control structure to allow fast and specific attack reaction. To cope with the new architecture, we propose a graphic model based attack detection system that can deal with the dataset shift problem. The simulation results show that our architecture can effectively and efficiently address the security challenges brought by the new network paradigm and our attack detection system can effectively report various attacks using real-world network traffic.     

基于可编程协议无关报文处理的分布式拒绝服务攻击检测

传统软件定义网络（SDN）中的分布式拒绝服务（DDoS）攻击检测方法需要控制平面与数据平面进行频繁通信,这会导致显著的开销和延迟,而目前可编程数据平面由于语法无法实现复杂检测算法,难以保证较高检测效率。针对上述问题,提出了一种基于可编程协议无关报文处理（P4）可编程数据平面的DDoS攻击检测方法。首先,利用基于P4改进的信息熵进行初检,判断是否有可疑流量发生;然后再利用P4提取特征只需微秒级时长的优势,提取可疑流量的六元组特征导入数据标准化—深度神经网络（data standardization-deep neural network,DS-DNN）复检模块,判断其是否为DDoS攻击流量;最后,模拟真实环境对该方法的各项评估指标进行测试。实验结果表明,该方法能够较好地检测SDN环境下的DDoS攻击,在保证较高检测率与准确率的同时,有效降低了误报率,并将检测时长缩短至毫秒级别。     

一种基于客户端网络的DDoS攻击防卫模型的研究

分布式拒绝服务攻击(DDoS)严重威胁着因特网的安全，但目前没有一种有效的方法来对付这种攻击。我们提出了一种基于客户端网络的DDoS攻击防卫模型--E-GUARD，它能够自动监测和停止源于本地网络的DDoS攻击。     

A comprehensive review on blockchains for Internet of Vehicles: Challenges and directions

Internet of Vehicles (IoVs) consists of smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate wirelessly to provide enhanced transportation services such as improved traffic efficiency and reduced traffic congestion and accidents. Unfortunately, current IoV networks suffer from security, privacy, and trust issues. Blockchain technology emerged as a decentralized approach for enhanced security without depending on trusted third parties to run services. Blockchain offers the benefits of trustworthiness and immutability and mitigates the problem of a single point of failure and other attacks. In this work, we present the state-of-the-art Blockchain-enabled IoVs (BIoVs) with a particular focus on their applications, such as crowdsourcing-based applications, energy trading, traffic congestion reduction, collision, accident avoidance, infotainment, and content caching. We also present in-depth applications of federated learning (FL) for BIoVs. The key challenges of integrating Blockchain with IoV are investigated in several domains, such as edge computing, machine learning, and Federated Learning (FL). Lastly, we present several open issues, challenges, and future opportunities in AI-enabled BIoV, hardware-assisted security for BIoV, and quantum computing attacks on BIoV applications.     

Design and implementation of the secure compiler and virtual machine for developing secure IoT services

Recent years have seen the development of computing environments for IoT (Internet of Things) services, which exchange large amounts of information using various heterogeneous devices that are always connected to networks. Since the data communication and services occur on a variety of devices, which not only include traditional computing environments and mobile devices such as smartphones, but also household appliances, embedded devices, and sensor nodes, the security requirements are becoming increasingly important at this point in time. Already, in the case of mobile applications, security has emerged as a new issue, as the dissemination and use of mobile applications have been rapidly expanding. This software, including IoT services and mobile applications, is continuously exposed to malicious attacks by hackers, because it exchanges data in the open Internet environment. The security weaknesses of this software are the direct cause of software breaches causing serious economic loss. In recent years, the awareness that developing secure software is intrinsically the most effective way to eliminate the software vulnerability, rather than strengthening the security system of the external environment, has increased. Therefore, methodology based on the use of secure coding rules and checking tools is attracting attention to prevent software breaches in the coding stage to eliminate the above vulnerabilities. This paper proposes a compiler and a virtual machine with secure software concepts for developing secure and trustworthy services for IoT environments. By using a compiler and virtual machine, we approach the problem in two stages: a prevention stage, in which the secure compiler removes the security weaknesses from the source code during the application development phase, and a monitoring stage, in which the secure virtual machine monitors abnormal behavior such as buffer overflow attacks or untrusted input data handling while applications are running.     

Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol

The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote server. This necessitates the need for designing and fabricating IoT based smart containers. Due to constrained nature of IoT devices, these are prone to different cyber security attacks such as Denial of Service (DoS), Man in Middle (MITM) and Replay. Therefore, designing efficient cyber security framework are required for smart container. The Datagram Transport Layer Security (DTLS) Protocol has emerged as the de facto standard for securing communication in IoT devices. However, it is unable to minimize cyber security attacks such as Denial of Service and Distributed Denial of Service (DDoS) during the handshake process. The main contribution of this paper is to design a cyber secure framework by implementing novel hybrid DTLS protocol in smart container which can efficiently minimize the effects of cyber attacks during handshake process. The performance of our proposed framework is evaluated in terms of energy efficiency, handshake time, throughput and packet delivery ratio. Moreover, the proposed framework is tested in IoT based smart containers. The proposed framework decreases handshake time more than 9% and saves 11% of energy efficiency for transmission in compare of the standard DTLS, while increases packet delivery ratio and throughput by 83% and 87% respectively.     

基于SDS架构的多级DDoS防护机制

随着互联网的高速发展,网络安全的问题越来越严峻。软件定义网络（SDN）的出现为解决网络安全问题提供了全新的解决方案,如软件定义安全（SDS）。在SDS架构的基础上,针对分布式拒绝服务（DDoS）攻击的特点,提出一种新的DDoS防护机制SDS for DDoS。这种防护机制结合了以往检测方式和防护方式的优点,将安全服务原子化,并实现安全策略盒的多级防护策略。在受到DDoS攻击时,机制可以根据检测到的攻击力度进行动态决策,还能先验式地对攻击流量进行阻隔,不仅增加了决策的可信度,还解决了以往所采用的静态防护和后验式防护的不足。实验验证了机制的可行性,能有效地避免服务器受到DDoS攻击,更突出了它在决策时的灵活性和在遭受攻击时的先验性。     

云环境下SDN的流量异常检测性能分析

随着复杂的混合云网络逐渐成为云计算发展的瓶颈,软件定义网络(SDN)技术近年来成为学术界和工业界关注的热点。在网络安全领域,对于应用SDN来解决网络攻击的研究尚处于起步阶段,SDN是否能够高效检测来自内部的网络攻击尚无定论。针对该问题,在分析SDN技术框架的基础上,设计基于OpenStack的云环境实验方案。在传统云环境网络和SDN环境下同时测试2种流量异常检测算法,模拟Flood攻击和端口扫描攻击,分析SDN在检测攻击时的精确度和资源使用率。结果表明,在云环境下利用SDN检测内部威胁时比传统网络环境占用更少的物理内存而不影响精确度,但直接在SDN控制器上部署安全应用的方式也存在性能瓶颈。     

An intelligent optimization-based traffic information acquirement approach to software-defined networking

Internet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine-grained and accurate traffic information is important for IoT network management. Software-defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine-grained traffic information in SDN-based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non-deterministic polynomial-hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.     

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).     

Distributed Denial of Service Attacks: A Threat or Challenge

In today’s cyber world, the Internet has become a vital resource for providing a plethora of services. Unavailability of these services due to any reason leads to huge financial implications or even consequences on society. Distributed Denial of Service (DDoS) attacks have emerged as one of the most serious threats to the Internet whose aim is to completely deny the availability of different Internet based services to legitimate users. The attackers compromise a large number of Internet enabled devices and gain malicious control over them by exploiting their vulnerabilities. Simplicity of launching, traffic variety, IP spoofing, high volume traffic, involvement of numerous agent machines, and weak spots in Internet topology are important characteristics of DDoS attacks and makes its defense very challenging. This article provides a survey with the enhanced taxonomies of DDoS attacks and defense mechanisms. Additionally, we describe the timeline of DDoS attacks to date and attempt to discuss its impact according to various motivations. We highlighted the general issues, challenges, and current trends of DDoS attack technology. The aim of the article is to provide complete knowledge of DDoS attacks and defense mechanisms to the research community. This will, in turn, help to develop a powerful, effective, and efficient defense mechanism by filling the various research gaps addressed in already proposed defense mechanisms.