RPL-Based IoT Networks under Decreased Rank Attack:Performance Analysis in Static and Mobile Environments

The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

Deep-learning-based ciphertext-only attack on optical double random phase encryption

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems.Learning-based at-tack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be at-tacked.Here,we propose a two-step deep learning strategy for ciphertext-only attack(COA)on the classical double ran-dom phase encryption(DRPE).Specifically,we construct a virtual DRPE system to gather the training data.Besides,we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks(DNNs)to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image.With these two trained DNNs at hand,we show that the plaintext can be predicted in real-time from an unknown ciphertext alone.The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system.Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

Attack on Optical Double Random Phase Encryption Based on the Principle of Ptychographical Imaging

The principle of ptychography is applied in known plain text attack on the double random phase encoding （DRPE） system. We find that with several pairs of plain texts and cipher texts, the model of attack on DRPE can be converted to the model of ptyehographical imaging. Owing to the inherent merits of the ptyehographical imaging, the DRPE system can be breached totally in a fast and nearly perfect way, which is unavailable for currently existing attack methods. Further, since the decryption keys can be seen as an object to be imaged from the perspective of imaging, the ptychographical technique may be a kind of new direction to further analysis of the security of other encryption systems based on double random keys.

Modeling and Analysis of OFDMA-NOMA-RA Protocol Considering Imperfect SIC in Multi-User Uplink WLANs

To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios,this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access(OFDMA)and non-orthogonal multiple access(NOMA).The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units(RUs),and NOMA is used on each RU to enable more users to access the channel and improve spectrum efficiency.Based on the protocol designed in this paper,in the case of imperfect successive interference cancellation(SIC),the probability of successful competition subchannels and the outage probability are derived for two scenarios:Users occupy the subchannel individually and users share the subchannel.Moreover,when two users share the channel,the decoding order of the users and the corresponding probabilities are considered.Then,the system throughput is obtained.To achieve better outage performance in the system,the optimal power allocation algorithm is proposed in this paper,which enables the optimal power allocation strategy to be obtained.Numerical results show that the larger the imperfect SIC coefficient,the worse the outage performance of weak users.Compared with pure OFDMA and NOMA,OFDMA-NOMA-RA always maintains an advantage when the imperfect SIC coefficient is less than a specific value.

A Robustness Model of Complex Networks with Tunable Attack Information Parameter

We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study the robustness of complex networks under random information and preferential information, respectively. Using the generating function method, we derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component. We show that hiding just a small fraction of nodes randomly can prevent a scale-free network from collapsing and detecting just a small fraction of nodes preferentially can destroy a scale-free network.

Randomized Stream Ciphers with Enhanced Security Based on Nonlinear Random Coding

We propose a framework for designing randomized stream ciphers with enhanced security. The key attribute of this framework is using of nonlinear bijective mappings or keyless hash functions for random coding. We investigate the computational security of the proposed ciphers against chosen-plaintext-chosen-initialization-vector attacks and show that it is based on the hardness of solving some systems of random nonlinear Boolean equations. We also provide guidelines for choosing components to design randomizers for specified ciphers.

Social Engineering Attack Classifications on Social Media Using Deep Learning

In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

硫酸盐对混凝土侵蚀过程的细观数值分析

硫酸盐侵蚀作为混凝土耐久性损伤的一个重要方面,日益受到人们的普遍关注。根据Fick第二定律和化学反应动力方程,建立了细观层面硫酸盐对混凝土侵蚀过程的数值分析方法。以混凝土随机凸多边形骨料模型为基础,利用Matlab程序对有限元软件进行二次开发,建立了基于扩散-化学反应的硫酸盐对混凝土侵蚀过程的细观数值分析方法,并与试验结果进行了对比。结果表明:硫酸盐侵蚀过程细观数值方法得到的模拟结果与试验结果吻合较好,可以有效模拟硫酸盐对混凝土的侵蚀过程;距混凝土表面一定深度范围内,SO_(4)^(2-)浓度随表面SO_(4)^(2-)浓度的提高而增加;砂浆中SO_(4)^(2-)扩散系数是影响混凝土内SO_(4)^(2-)最大侵蚀深度的主要因素;界面过渡区厚度对硫酸盐环境中混凝土损伤的影响较小。

外部干扰和随机DoS攻击下的网联车安全H∞队列控制

针对网联车队列系统易受到干扰和拒绝服务(Denial of service, DoS)攻击问题,提出一种外部干扰和随机DoS攻击作用下的网联车安全H∞队列控制方法.首先,采用马尔科夫随机过程,将网联车随机DoS攻击特性建模为一个随机通信拓扑切换模型,据此设计网联车安全队列控制协议.然后,采用线性矩阵不等式(Linear matrix inequality, LMI)技术计算安全队列控制器参数,并应用Lyapunov-Krasovskii稳定性理论,建立在外部扰动和随机DoS攻击下队列系统稳定性充分条件.在此基础上,分析得到该队列闭环系统的弦稳定性充分条件.最后,通过7辆车组成的队列系统对比仿真实验,验证该方法的优越性.

基于知识图谱的网络攻击预测方法研究及应用

针对网络攻击知识图谱,同时引入了时序信息,提出一种基于知识图谱的网络攻击预测方案,并对其进行应用。通过对网络攻击知识图谱进行规则学习和应用,能够有效地得到网络攻击事件预测结果,为网络安全运维人员提供决策支持。以企业提供的网络安全运维知识图谱为例,将文中研究的方法应用到企业安全检测系统,结果证明该方法具有充分的准确性和可行性,同时为后续研究提供了思路。

基于随机森林算法和K-means算法的网络攻击识别方法

5G网络与核电的深度融合能够提升核电厂生产安全管控水平,减少人为事故,促进核电行业安全和经济发展。但由于网络的接入,为核电安全生产带来了一定的安全风险,恶意攻击者会通过向核电5G网络发起攻击进而破坏核电生产。为了解决核电5G网络场景下面临的网络异常和恶意攻击的问题,提出了一种在核电5G网络场景下基于随机森林算法和K-means算法的实时网络异常检测和网络攻击识别方法,对于提高核电网络安全具有重要的意义。

基于联邦学习的无线通信网络DoS攻击检测方法

无线通信网络受到DoS攻击,会使得网络的负载增加,导致延迟增加。而在无线通信网络中,数据通常分散在多个节点上,这会造成数据泄露和被攻击。为此,提出一种基于联邦学习的无线通信网络DoS攻击检测方法。对初始无线通信网络数据进行预处理和归一化,并采用随机森林算法进行降维处理,去除冗余特征,获得最佳网络数据特征集。将特征集输入到以深度卷积神经网络为通用模型的联邦学习训练模型中,独立训练本地模型并进行模型修正,传输至中心服务器进行聚合,收敛后完成训练。利用训练得到的联邦学习模型检测无线通信网络DoS攻击速率,再与接收者接收的容量最大值进行比较,判断是否有DoS攻击。实验结果表明,所提方法在处理大量数据时具有较高的稳定性和可靠性,能够在短时间内准确地检测出DoS攻击。

面向二进制代码的细粒度软件多样化方法

现有软件多样化方法大多需要源代码,基于编译器生成变体二进制,而对二进制代码直接进行转换时由于缺乏调试信息导致难以正确逆向,且易造成高额的性能开销。为此,提出一种面向二进制代码的细粒度软件多样化方法。通过静态二进制重写技术以函数块为单位进行重排序,随机化函数在代码段中的原始位置,同时使程序的内存片段gadgets位置发生改变,使得攻击者对程序的先验知识失效,以防御大规模代码重用攻击。为了进一步提高攻击者破解难度,对基本块内的指令进行依赖性分析,实现基本块内指令随机化,同时使得随机化后基本块的原始语义不变。性能测试结果表明,函数重排序对gadgets存活率的影响大于基本块内指令重排序,两者同时使用时程序的gadgets平均存活率为5.71%;模糊哈希算法Tlsh比较结果显示,该方法能够有效躲避同源性检测;使用工具Bindiff进行测试的结果表明,多样化后基本块内指令重排序的异构度大于函数重排序,且在基准测试集SPEC CPU2006上函数重排序和指令重排序同时使用时平均运行开销仅为3.1%,具有良好的实用性。

基于随机噪声和自适应步长的快速对抗训练方法

当前对抗训练(AT)及其变体被证明是防御对抗攻击的最有效方法,但生成对抗样本的过程需要庞大的计算资源,导致模型训练效率低、可行性不强;快速AT(Fast-AT)使用单步对抗攻击代替多步对抗攻击加速训练过程,但模型鲁棒性远低于多步AT方法且容易发生灾难性过拟合(CO)。针对这些问题,提出一种基于随机噪声和自适应步长的Fast-AT方法。首先,在生成对抗样本的每次迭代中,通过对原始输入图像添加随机噪声增强数据;其次,累积训练过程中每个对抗样本的梯度,并根据梯度信息自适应地调整对抗样本的扰动步长;最后,根据步长和梯度进行对抗攻击,生成对抗样本用于模型训练。在CIFAR-10、CIFAR-100数据集上进行多种对抗攻击,相较于N-FGSM(Noise Fast Gradient Sign Method),所提方法在鲁棒准确率上取得了至少0.35个百分点的提升。实验结果表明,所提方法能避免Fast-AT中的CO问题,提高深度学习模型的鲁棒性。

x86处理器向量条件访存指令安全脆弱性分析

单指令多数据流(Single Instruction stream,Multiple Data streams,SIMD)是一种利用数据级并行提高处理器性能的技术,旨在利用多个处理器并行执行同一条指令增加数据处理的吞吐量.随着大数据、人工智能等技术的兴起,人们对数据并行化处理的需求不断提高,这使得SIMD技术愈发重要.为了支持SIMD技术,Intel和AMD等x86处理器厂商从1996年开始在其处理器中陆续引入了MMX(MultiMedia Extensions)、SSE(Streaming SIMD Extensions)、AVX(Advanced Vector eXtensions)等SIMD指令集扩展.通过调用SIMD指令,程序员能够无需理解SIMD技术的硬件层实现细节就方便地使用它的功能.然而,随着熔断、幽灵等处理器硬件漏洞的发现,人们逐渐认识到并行优化技术是一柄双刃剑,它在提高性能的同时也能带来安全风险.本文聚焦于x86 SIMD指令集扩展中的VMASKMOV指令,对它的安全脆弱性进行了分析.本文的主要贡献如下:(1)利用时间戳计数器等技术对VMASKMOV指令进行了微架构逆向工程,首次发现VMASKMOV指令与内存页管理和CPU Fill Buffer等安全风险的相关性;(2)披露了一个新的处理器漏洞EvilMask,它广泛存在于Intel和AMD处理器上,并提出了3个EvilMask攻击原语:VMASKMOVL+Time(MAP)、VMASKMOVS+Time(XD)和VMASKMOVL+MDS,可用于实施去地址空间布局随机化攻击和进程数据窃取攻击;(3)给出了2个EvilMask概念验证示例(Proof-of-Concept,PoC)验证了EvilMask对真实世界的信息安全危害;(4)讨论了针对EvilMask的防御方案,指出最根本的解决方法是在硬件层面上重新实现VMASKMOV指令,并给出了初步的实现方案.

基于边缘计算的并行密钥隔离聚合签名方案

无线医疗传感器网络的出现为患者的治疗带来了极大的便利.但是,无线医疗传感器网络中往往都使用不可信的公共信道进行数据通信并且只有唯一的云服务器处理大量的医疗数据,这就导致了通信安全、隐私保护、密钥泄露、云服务器计算负担过大、延迟高等问题.此外,现有的大多数无证书聚合签名方案无法抵抗完全选择密钥攻击.针对上述问题,本文提出一种适用于无线医疗传感器网络基于边缘计算的无证书并行密钥隔离聚合签名方案.方案引入边缘计算的架构使得签名的验证和聚合过程在更靠近终端用户的边缘层进行,在降低中心云服务器计算负担的同时还能有效的保护患者的隐私.本文方案继承了无证书和密钥隔离技术的优点,同时避免了复杂的证书管理、密钥托管以及密钥暴露等问题.在随机预言模型下证明了本文方案可以抵抗完全选择密钥攻击、Type I攻击以及Type II攻击.性能分析表明,与相关无证书签名方案相比,本文方案的计算开销至少可降低74.03%,通信开销至少可降低25%.

基于数据相容压缩的RAS测试方法

提出一种随机存取扫描测试方法,对扫描单元进行相容处理,以形成新的测试集合。结合Random Access Scan结构特性,对该测试集合进行优化,同时解决在测试工作中面临的测试数据量、测试功耗、测试时间等3方面问题。在ISCAS’89基准电路上对该方法进行验证,实验结果表明,该方法是有效可行的。

河南省铁路-航道货运多层复合网络鲁棒性研究

发展铁水联运是优化调整运输结构、促进物流业降本增效的重要手段之一,为深入理解和把握河南省铁水联运网络的网络性能,有针对性地优化调整铁水联运网络布局、提升联运效益,运用复杂网络理论,对河南省铁路-航道货运网络进行了拓扑分析和鲁棒性研究。一方面,对铁路网、航道网以及二者相结合的复合网络进行拓扑分析,得出航道网兼具显著的小世界特性和无标度特性,铁路网则具有显著的无标度特性;另一方面,选取网络效率和最大连通子图相对大小作为评价指标,针对3种网络的有效性和连通性进行鲁棒性研究,得出鲁棒性强弱依次为复合网络、铁路网络、航道网络,表明发展铁水联运的重要性和优越性;最后,针对河南省综合运输网络构建优化,提出具体保障措施和运营建议。

N-ras基因DNA损伤检测方法的建立

目的建立应用依赖随机化末端连接物PCR(RDPCR)技术检测N-ras基因DNA损伤的试验方法。方法采用单引物PCR技术制备N-ras基因外显子1单链探针,酶切构建DNA损伤模型,再经RDPCR扩增后与探针杂交显色。结果单引物PCR技术制备单链探针获得成功,目的基因在相应位置出现了清晰的杂交条带。结论RDPCR技术可定位检测到该酶切DNA损伤位点,表明该方法已成功建立,将有利于其在化学致癌作用机制研究及肿瘤预防领域的应用。