聊聊session fixation attacks

序 本文主要讲一下session fixation attacks以及spring security对它的防范。 session fixation attacks 会话固定攻击,是利用那些登录前和登录之后sessionId没有变化的漏洞来获取登录态,进而获取用户的相关信息等。 would be to make sure a session exists or to change the session Id to guard * against session-fixation attacks You will" + " not be adequately protected against session-fixation attacks"); 在登录成功之后进行相关session处理,如果servlet3.1+,则使用ChangeSessionIdAuthenticationStrategy来更换sessionId,以防范session fixation attacks


Web Security 之 HTTP Host header attacks

HTTP Host header attacks 在本节中,我们将讨论错误的配置和有缺陷的业务逻辑如何通过 HTTP Host 头使网站遭受各种攻击。 这有时被称为 "Host header SSRF attacks" 。 经典的 SSRF 漏洞通常基于 XXE 或可利用的业务逻辑,该逻辑将 HTTP 请求发送到从用户可控制的输入派生的 URL 。

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    科普哈希长度扩展攻击(Hash Length Extension Attacks)

    Length Extension Attacks, The Simple Explanation 哈希摘要算法,如MD5,SHA1, SHA2等,都是基于Merkle–Damgård结构。


    Generalized Transferability for Evasion and Poisoning Attacks论文笔记

    Generalized Transferability for Evasion and Poisoning Attacks论文笔记 该论文主要是介绍了一个FAIL模型, 即一个通用框架用来分析针对机器学习系统的真实攻击


    Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks 论文笔记(2)

    该文章提出一种利用程序化噪声来生成对抗样本的方法, 所提出的方法和那些通过梯度不断修改以至于到达分类器的边界的方法不一样, 上述方法需要对目标的模型有一定的了解...


    Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks论文笔记(1)

    Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks论文笔记 0. Roli, “Evasion Attacks against Machine Learning at Test Time,” in Joint European Conference on Machine Goodfellow, “Transferability in Machine Learning: From Phenomena to Black-box Attacks using Adversarial Bethge, “Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Hsieh, “Zoo: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training


    论文笔记:Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis(DDLA)

    论文笔记:Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis(DDLA) Benjamin Timon eShard, Singapore 基础概念 Non-profiling attacks: 假设攻击者只能从目标设备获取跟踪。 Profiling attacks: 假定攻击者拥有与目标设备相同的可编程设备。 例如: Template Attacks, Stochastic attacks or Machine-Learning-based attacks. 1.在分析阶段:用收集的侧通道迹,对所有可能密钥值 • For attacks on the unprotected CW and on ASCAD, we used the LSB labeling.



    def attacks(): list_of_attacks = ["lower_body", "lower_body", "upper_body"] print("There are a total of {lenlist_of_attacks)}\ attacks coming!") for attack in list_of_ attacks: yield attack attack = attacks() count = 0 while next(attack) def lazy_return_random_attacks(): """Yield attacks each time""" import random attacks (attack.pop().upper() for attack in \ lazy_return_random_attacks()) next(upper-case_attacks



    (3)一些其他的例子,cleverhans代码库提供了多样性的对抗样本生成方法,具体如下: sample_attacks/ - directory with examples of attacks: sample_attacks/fgsm/ - Fast gradient sign attack. sample_attacks/noop/ - No-op attack, which just copied images unchanged. sample_attacks/random_noise/ - Attack which adds random noise to images. sample_targeted_attacks / - directory with examples of targeted attacks: sample_targeted_attacks/step_target_class/ - one Model is described in Ensemble Adversarial Training: Attacks and Defenses paper. 同时也提供了好几个example。


    研发:What is a DDoS Attack?

    What are common types of DDoS attacks? While nearly all DDoS attacks involve overwhelming a target device or network with traffic, attacks can Protocol Attacks The Goal of the Attack: Protocol attacks, also known as a state-exhaustion attacks, Volumetric Attacks The Goal of the Attack: This category of attacks attempts to create congestion by attacks.



    原文题目:Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability 原文:This work proposes a new class of proactive attacks called the Informational Denial-of-Service (IDoS) attacks that exploit the attentional human vulnerability. to prevent humans from identifying the real attacks hidden among feints. security technologies to mitigate the severity level and risks of IDoS attacks.



    输入 exec audit : 执行检测中间件插件下的所有模块 输入 exec attacks.xss :使用注入插件中的检测xss漏洞模块: 输入 exec attacks.blindsqli : exec attacks才会调用报告赋值(调用子模块的函数是startup_spec_attacks,没有重新赋报告值,所以导致exec子模块,报告数据就会并到前面的插件总模块),参见代码如下: #执行 attacks子模块函数,没有对REPORT进行再赋值 def startup_spec_attacks(attack:str): if attack in attacks_info.keys( '] = strftime("%Y/%m/%d at %H:%M:%S") execmod.append("attacks") plugins = attacks_plugins() #调用总的attacks模块,才对报告结果赋值 这个问题需要引起关注,如果想简单的改,直接在startup_spec_attacks函数里加上REPORT赋值(需要有重复判断)就行: def startup_spec_attacks


    直流微电网中的虚假数据注入攻击及对策(CS SC)

    原文题目:False Data Injection Attacks and Corresponding Countermeasure in DC Microgrid 原文:In this paper, equipped with unknown input observer (UIO) based detectors, and investigate false data injection (FDI) attacks Furthermore, we extend ZTU attacks to nonzero trace undetectable (NTU) attacks by utilizing system noise expressions of the point of common coupling (PCC) voltages are obtained to analyze the impacts of NTU attacks Moreover, we provide a possible countermeasure against ZTU and NTU attacks by observing the average PCC