An Effective and Scalable VM Migration Strategy to Mitigate Cross-VM Side-Channel Attacks in Cloud

Co-residency of virtual machines(VMs) of different tenants on the same physical platform would possibly lead to cross-VM side-channel attacks in the cloud. While most of current countermeasures fail for real or immediate deployment due to their requirement for modification of virtualization structure, we adopt dynamic migration, an inherent mechanism of the cloud platform, as a general defense against this kind of threats. To this end, we first set up a unified practical information leakage model which shows the factors affecting side channels and describes the way they influence the damage due to side-channel attacks. Since migration is adopted to limit the time duration of co-residency, we envision this defense as an optimization problem by setting up an Integer Linear Programming(ILP) to calculate optimal migration strategy, which is intractable due to high computational complexity. Therefore, we approximate the ILP with a baseline genetic algorithm, which is further improved for its optimality and scalability. Experimental results show that our migration-based defense can not only provide excellent security guarantees and affordable performance cost in both theoretical simulation and practical cloud environment, but also achieve better optimality and scalability than previous countermeasures.

Side-channel attack-resistant AES S-box with hidden subfield inversion and glitch-free masking

A side-channel attack(SCA)-resistant AES S-box implementation is proposed,which is an improvement from the power-aware hiding(PAH)S-box but with higher security and a smaller area.We use the composite field approach and apply the PAH method to the inversion in the nonlinear kernel and a masking method to the other parts.In addition,a delaymatched enable control technique is used to suppress glitches in the masked parts.The evaluation results show that its area is contracted to 63.3%of the full PAH S-box,and its power-delay product is much lower than that of the masking implementation.The leakage assessment using simulation power traces concludes that it has no detectable leakage under t-test and that it at least can thwart the moment-correlation analysis using 665000 noiseless traces.

An Efficient Method against Side-Channel Attacks on ECC

Side-channel attacks （SCA） may exploit leakage information to break cryptosystems. In this paper we present a new SCA resistant Elliptic Curve scalar multiplication algorithm. The proposed algorithm, builds a sequence of bit-strings representing the scalar k, characterized by the fact that all bit-strings are different from zero; this property will ensure a uniform computation behavior for the algorithm, and thus will make it secure against simple power analysis attacks （SPA）. With other randomization techniques, the proposed countermeasures do not penalize the computation time. The proposed scheme is more efficient than MOEller＇s one, its cost being about 5% to 10% smaller than MOEller＇s one.

Side-Channel Attacks Based on Collaborative Learning

Side-channel attacks based on supervised learning require that the attacker have complete control over the cryptographic device and obtain a large number of labeled power traces.However,in real life,this requirement is usually not met.In this paper,an attack algorithm based on collaborative learning is proposed.The algorithm only needs to use a small number of labeled power traces to cooperate with the unlabeled power trace to realize the attack to cryptographic device.By experimenting with the DPA contest V4 dataset,the results show that the algorithm can improve the accuracy by about 20%compared with the pure supervised learning in the case of using only 10 labeled power traces.

The Role of AI in Cyber Security: Safeguarding Digital Identity

This article signals the use of Artificial Intelligence (AI) in information security where its merits, downsides as well as unanticipated negative outcomes are noted. It considers AI based models that can strengthen or undermine infrastructural functions and organize the networks. In addition, the essay delves into AI’s role in Cyber security software development and the need for AI-resilient strategies that could anticipate and thwart AI-created vulnerabilities. The document also touched on the socioeconomic ramifications of the emergence of AI in Cyber security as well. Looking into AI and security literature, the report outlines benefits including made threat detection precision, extended security ops efficiency, and preventive security tasks. At the same time, it emphasizes the positive side of AI, but it also shows potential limitations such as data bias, lack of interpretability, ethical concerns, and security flaws. The work similarly focuses on the characterized of misuse and sophisticated cyberattacks. The research suggests ways to diminish AI-generating maleficence which comprise ethical AI development, robust safety measures and constant audits and updates. With regard to the AI application in Cyber security, there are both pros and cons in terms of socio-economic issues, for example, job displacement, economic growth and the change in the required workforce skills.

Detection Technique of Software-Induced Rowhammer Attacks

Side-channel attacks have recently progressed into software-induced attacks.In particular,a rowhammer attack,which exploits the characteristics of dynamic random access memory(DRAM),can quickly and continuously access the cells as the cell density of DRAM increases,thereby generating a disturbance error affecting the neighboring cells,resulting in bit flips.Although a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits,it has been reported that it can be exploited on various platforms such as mobile devices,web browsers,and virtual machines.Furthermore,there have been studies on bypassing the defense measures of DRAM manufacturers and the like to respond to rowhammer attacks.A rowhammer attack can control user access and compromise the integrity of sensitive data with attacks such as a privilege escalation and an alteration of the encryption keys.In an attempt to mitigate a rowhammer attack,various hardware-and software-based mitigation techniques are being studied,but there are limitations in that the research methods do not detect the rowhammer attack in advance,causing overhead or degradation of the system performance.Therefore,in this study,a rowhammer attack detection technique is proposed by extracting common features of rowhammer attack files through a static analysis of rowhammer attack codes.

The Impact of Aluminum- and Iron-Bearing Admixtures on the Resistance of Portland Cement Mortars to Alkali-Silica Reaction and Sulfate Attack

Study of sulfate resistance of mortars with aluminum- and iron-bearing admixtures (Al(OH)3, Al2(SO4)3, FeSO4, Fe2(SO4)3) in conditions close to those established in ASTM C 1012, and the study of the mitigation effect of these admixtures on alkali-silica reaction in accordance with accelerated “mortar bar” test ( GOST 8269.0, ASTM C 1260) were performed. Iron (II) and (III) sulfates show ability for mitigation alkali-silica reaction, while also, in contrast with Al-bearing substances, do not induce the drastic reducing of the initial setting time and do not promote the progress of sulfate corrosion. Compared with FeSO4, iron (III) sulfate has moderate deleterious impact on the early strength of cement paste and can be of interest alone as an inhibitor of ASR. Iron (II) sulfate may be used together with aluminum sulfate to offset the accelerating effect of the latter on the setting of cement paste and to reduce a risk of sulfate corrosion. During prolonged water storage, the mortar elongation and secondary ettringite formation do not occur, even when Al2(SO4)3 is available. Therefore, the investigated admixtures cannot act as agents of internal sulfate attack, however, Al2(SO4)3 can enhance the outer sulfate attack.

Side-Channel Analysis for Detecting Protocol Tunneling

Protocol tunneling is widely used to add security and/or privacy to Internet applications. Recent research has exposed side channel vulnerabilities that leak information about tunneled protocols. We first discuss the timing side channels that have been found in protocol tunneling tools. We then show how to infer Hidden Markov models (HMMs) of network protocols from timing data and use the HMMs to detect when protocols are active. Unlike previous work, the HMM approach we present requires no a priori knowledge of the protocol. To illustrate the utility of this approach, we detect the use of English or Italian in interactive SSH sessions. For this example application, keystroke-timing data associates inter-packet delays with keystrokes. We first use clustering to extract discrete information from continuous timing data. We use discrete symbols to infer a HMM model, and finally use statistical tests to determine if the observed timing is consistent with the language typing statistics. In our tests, if the correct window size is used, fewer than 2% of data windows are incorrectly identified. Experimental verification shows that on-line detection of language use in interactive encrypted protocol tunnels is reliable. We compare maximum likelihood and statistical hypothesis testing for detecting protocol tunneling. We also discuss how this approach is useful in monitoring mix networks like The Onion Router (Tor).

Timing Attack Analysis on AA_βCryptosystem

Timing attack is an attack on the implementation of a cryptographic primitive. The attack collects leaked secret data via certain implementation techniques either on software or hardware. This paper provides an analysis of a theoretical timing attack on the AAβ algorithm. The attack discussed in this paper gives avenues for secure implementation of AAβ against timing attacks. The simulation of the attack is important to provide invulnerability features for the algorithm in order to be implemented and embedded on applications. At the end of the attack, a method to overcome it will be introduced and it is called AAβ blinding.

Enhancing non-profiled side-channel attacks by time-frequency analysis

Side-channel analysis(SCA)has become an increasing important method to assess the physical security of cryptographic systems.In the process of SCA,the number of attack data directly determines the performance of SCA.With sufficient attack data,the adversary can achieve a successful SCA.However,in reality,the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key.In this case,the adversary cannot use casual numbers of data to perform SCA.The performance of SCA will be severely dropped if the attack traces are insufficient.In this paper,we introduce wavelet scatter transform(WST)and short-time fourier transform(STFT)to non-profiled side-channel analysis domains,to improve the performance of side-channel attacks in the context of insufficient data.We design a practical framework to provide suitable parameters for WST/STFT-based SCA.Using the proposed method,the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA.The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance.Compared with the original correlation power analysis(CPA),the number of attack data can be reduced by 50–95%.

A Novel IoT Architecture, Assessment of Threats and Their Classification withMachine Learning Solutions

The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave independently of others.IoT devices,however,are also vulnerable,mostly because they lack the essential built-in security to thwart attackers.It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment.As a result,the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT,such as Cloud computing,machine learning techniques,and lightweight encryption techniques.This paper presents a detailed analysis of the security requirements,attack surfaces,and security solutions available for IoT networks and suggests an innovative IoT architecture.The Seven-Layer Architecture in IoT provides decent attack detection accuracy.According to the level of risk they pose,the security threats in each of these layers have been properly categorized,and the essential evaluation criteria have been developed to evaluate the various threats.Also,Machine Learning algorithms like Random Forest and Support Vector Machines,etc.,and Deep Learning algorithms like Artificial Neural Networks,Q Learning models,etc.,are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.

ISO/IEC 17825-2016《密码模块的非入侵式攻击缓解技术的测试方法》标准解读

从密码模块上保障数据隐私和安全是一种行之有效的手段,因此近年来对密码模块的安全性评估倍受关注。非入侵式攻击缓解技术是密码模块安全指标之一,本文结合GM/T 0083-2020国密标准对IEC 17825-2016进行了学习与解读。主要针对非入侵式攻击方法与安全功能的关联性、基本测试项目和流程、通过/失败测试指标、缓解技术等方面进行了解读。希望能为相关人员对标准及测试方法的理解和密码模块安全设计、应用、评估提供些许参考。

Hardware Security for IoT in the Quantum Era: Survey and Challenges

The Internet of Things (IoT) has become a reality: Healthcare, smart cities, intelligent manufacturing, e-agriculture, real-time traffic controls, environment monitoring, camera security systems, etc. are developing services that rely on an IoT infrastructure. Thus, ensuring the security of devices during operation and information exchange becomes a fundamental requirement inherent in providing safe and reliable IoT services. NIST requires hardware implementations that are protected against SCAs for the lightweight cryptography standardization process. These attacks are powerful and non-invasive and rely on observing the physical properties of IoT hardware devices to obtain secret information. In this paper, we present a survey of research on hardware security for the IoT. In addition, the challenges of IoT in the quantum era with the first results of the NIST standardization process for post-quantum cryptography are discussed.

PCI-E密码卡SM2算法侧信道攻击缓解技术设计与研究

典型的外设组件互联高速总线(Peripheral Component Interconnect Express,PCI-E)密码卡进行SM2运算时,常由可编程逻辑器件将关键安全参数传入专算芯片,专算芯片经过计算后再将结果返回至可编程逻辑器件。上述过程不可避免面临关键安全参数在芯片间的电路上以明文传输,并且专算芯片在运算过程中产生的密码卡功耗波动和电磁辐射也容易被侧信道攻击所利用。因此,在典型架构的PCI-E密码卡上通过对密码算法的分析和改造,以避免关键安全参数在芯片间传输,平滑算法芯片的功耗和电磁辐射,并使用Inspector分析工具测试验证效果,提升密码卡抗侧信道攻击的能力成为业界研究的重点。该研究成果对PCI-E密码卡的安全性提升和商业价值的进一步开发具有重要意义。

基于空间波束不变性的升空平台大规模MIMO导频攻击检测与消除方法

针对升空通信平台大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中在通信接入阶段遭受导频攻击引入的信息泄露问题,提出了一种基于空间波束不变性的导频接入攻击检测与消除方法。该方案基于用户空间波束域信道结构特征,在缺少信道先验信息的条件下使用点排序簇结构(Ordering Points to Identify the Clustering Structure,OPTICS)聚类算法,分离提取接入攻击与合法用户的波束域信道,消除接入攻击导致的信号损伤,实现合法用户信号安全传输。仿真结果表明,所提方案能够有效降低导频接入攻击对系统安全传输的影响。

Driftor: mitigating cloud-based side-channel attacks by switching and migrating multi-executor virtual machines

Co-residency of different tenants’ virtual machines(VMs) in cloud provides a good chance for side-channel attacks, which results in information leakage. However, most of current defense suffers from the generality or compatibility problem, thus failing in immediate real-world deployment. VM migration, an inherit mechanism of cloud systems, envisions a promising countermeasure, which limits co-residency by moving VMs between servers. Therefore, we first set up a unified practical adversary model, where the attacker focuses on effective side channels. Then we propose Driftor, a new cloud system that contains VMs of a multi-executor structure where only one executor is active to provide service through a proxy, thus reducing possible information leakage. Active state is periodically switched between executors to simulate defensive effect of VM migration. To enhance the defense, real VM migration is enabled at the same time. Instead of solving the migration satisfiability problem with intractable CIRCUIT-SAT, a greedy-like heuristic algorithm is proposed to search for a viable solution by gradually expanding an initial has-to-migrate set of VMs. Experimental results show that Driftor can not only defend against practical fast side-channel attack, but also bring about reasonable impacts on real-world cloud applications.

抵御SIP分布式洪泛攻击的入侵防御系统

针对SIP分布式洪泛攻击检测与防御的研究现状,结合基于IP的分布式洪泛攻击和SIP消息的特点,提出了一种面向SIP分布式洪泛攻击的两级防御分布式拒绝服务(DDoS)攻击体系结构(TDASDFA):一级防御子系统(FDS)和二级防御子系统(SDS)。FDS对SIP的信令流进行粗粒度检测与防御,旨在过滤非VoIP消息和丢弃超出指定速率的IP地址的SIP信令,保证服务的可用性;SDS利用一种基于安全级别设定的攻击减弱方法对SIP信令流进行细粒度检测,并过滤具有明显DoS攻击特征的恶意攻击和低流量攻击。FDS和SDS协同工作来实时检测网络状况,减弱SIP分布式洪泛攻击。实验结果表明,TDASDFA能实时地识别和防御SIP分布式洪泛攻击,并且在异常发生时有效地减弱SIP代理服务器/IMS服务器被攻击的可能性。

一种SIP分布式洪泛攻击的减弱方法

对会话初始化协议(SIP)分布式洪泛攻击的原理进行研究,结合SIP协议自身的特点提出一种基于安全级别设定的攻击减弱方法.该方法将SIP消息按照历史记录、协议自身进行安全级别分类,利用流量监控对SIP流量监控.当发生分布式洪泛攻击时,通过设定合适的安全级别减弱攻击造成的影响.仿真实验结果表明基于安全级别的方法能够识别和防御SIP分布式洪泛攻击,有效地减弱SIP代理服务器/IMS服务器被攻击的可能性.

基于OpenFlow的流量监控架构实践方案

为了解决基于Open Flow的软件定义网络(software defined network,SDN)架构存在的潜在安全性问题,缓解特定的网络攻击对Open Flow网络基础设施的威胁,保障Open Flow网络在存在异常流量状态下的网络性能,在分析Open Flow协议的安全缺陷的基础上,提出了一种流量监控方案.该方案使用s Flow流量采样技术,结合Flood Light开源控制器,通过上层应用更改控制器操作模式并对交换机执行端口限速.实验结果证明:提出的方案在网络受到特定攻击时可以降低控制器负载,过滤攻击产生的Packet_In包达99.88%,有效地减小了异常流量对网络中主机及网络本身的影响;能及时监测网络攻击造成的网络异常,并缓解网络攻击对网络整体性能的影响.