基于源代码的缓冲区溢出预防技术

基于缓冲区溢出的攻击是一种常见的安全攻击手段,也是目前惟一最重要最常见的安全威胁。在所有的软件安全漏洞中,缓冲区溢出漏洞大约占一半。该文从编程的角度分析了缓冲区溢出攻击,并提出在源代码阶段尽量避免缓冲区溢出的方法。     

DDoS攻击防御机制综述

DDoS攻击是目前威胁网络安全的主要因素之一。本文分类介绍了现有的DDoS攻击防御机制,阐明了各种机制之间的关系,给出了每种防御机制的应用实例,全面分析了各种防御机制的优缺点。对正确认识DDoS攻击及其防御有很大的参考价值。     

EMDR Therapy Following the 9/11 Terrorist Attacks: A Community-Based Intervention Project in New York City.

This article presents the results of a time-limited psychological relief effort using eye movement desensitization and reprocessing (EMDR) following the attacks on the World Trade Center on September 11, 2001. Clients made highly significant positive gains on a range of outcome variables, including validated psychometrics and self-report scales. Analyses of the data suggest 2 broad conclusions: EMDR is a useful treatment intervention both in the immediate aftermath of disaster as well as later; the longer treatment is delayed, the greater the level of disturbance experienced by clients. Also discussed are problems in conducting research during mass disaster response situations. A demonstration of an analog to a wait-list control group is provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于移动代理、关键主机隐藏技术的IDS体系结构

本文介绍了一种安全灵活的入侵检测系统体系结构，通过运用关键主机隐藏技术，使关键主机对于主动探测，被动监听均不可见，提高了系统自身的安全性，同时，通过引入移动代理，限制入侵检测系统各部分之间的通信等机制，增强本体系结构对于拒绝服务攻击的抵抗力，系统通过使用智能移动代理在网络节点上收集处理信息，提高了入侵检检测系统的灵活性，减少了网络负载。     

一种基于多点传送树的拒绝服务攻击的安全方案

拒绝服务攻击严重威胁着Internet的安全,而且其入侵工具的发展是一种长期存在的趋势。文章介绍了一种新的拒绝服务攻击的安全方案,通过使用多点传送树作为基本的认证机制来进行IP多点传送认证,从而对拒绝服务攻击免疫。     

Memory and Emotions for the September 11, 2001, Terrorist Attacks in Patients With Alzheimer's Disease, Patients With Mild Cognitive Impairment, and Healthy Older Adults.

National traumatic events can produce extremely vivid memories. Using a questionnaire administered during telephone interviews, the authors investigated emotional responses to, and memory for. the September 11, 2001, terrorist attacks in patients with Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and healthy older adults in the initial weeks following the event and again 3-4 months later. There were several notable findings. First, patients with AD showed less memory than patients with MCI and older adults. Second, patients with AD, but not patients with MCI or older adults, appeared to retain more memory for personal versus factual information. Third, patients with AD and older adults did not differ in the intensity of their reported emotional responses to the attacks, whereas patients with MCI reported relatively less intense emotional responses. Last, distortions of memory for personal information were frequent for all participants but were more common in patients with AD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Experimental validation of a resilient monitoring and control system

Complex, high performance, engineering systems have to be closely monitored and controlled to ensure safe operation and protect public from potential hazards. One of the main challenges in designing monitoring and control algorithms for these systems is that sensors and actuators may be malfunctioning due to malicious or natural causes. To address this challenge, this paper addresses a resilient monitoring and control (ReMAC) system by expanding previously developed resilient condition assessment monitoring systems and Kalman filter-based diagnostic methods and integrating them with a supervisory controller developed here. While the monitoring and diagnostic algorithms assess plant cyber and physical health conditions, the supervisory controller selects, from a set of candidates, the best controller based on the current plant health assessments. To experimentally demonstrate its enhanced performance, the developed ReMAC system is then used for monitoring and control of a chemical reactor with a water cooling system in a hardware-in-the-loop setting, where the reactor is computer simulated and the water cooling system is implemented by a machine condition monitoring testbed at Idaho National Laboratory. Results show that the ReMAC system is able to make correct plant health assessments despite sensor malfunctioning due to cyber attacks and make decisions that achieve best control actions despite possible actuator malfunctioning. Monitoring challenges caused by mismatches between assumed system component models and actual measurements are also identified for future work.     

Unpaired image to image transformation via informative coupled generative adversarial networks

We consider image transformation problems, and the objective is to translate images from a source domain to a target one. The problem is challenging since it is difficult to preserve the key properties of the source images, and to make the details of target being as distinguishable as possible. To solve this problem, we propose an informative coupled generative adversarial networks (ICoGAN). For each domain, an adversarial generator-and-discriminator network is constructed. Basically, we make an approximately-shared latent space assumption by a mutual information mechanism, which enables the algorithm to learn representations of both domains in unsupervised setting, and to transform the key properties of images from source to target.Moreover, to further enhance the performance, a weightsharing constraint between two subnetworks, and different level perceptual losses extracted from the intermediate layers of the networks are combined. With quantitative and visual results presented on the tasks of edge to photo transformation, face attribute transfer, and image inpainting, we demonstrate the ICo- GAN’s effectiveness, as compared with other state-of-the-art algorithms.     

Protection issues for supply systems involving random attacks

This paper focuses on the protection issues for supply systems involving random attacks, which are described as attacks whose targets cannot be predicted. We present the random-attack median fortification problem (RAMF) to identify the fortification strategy that minimizes the expected operation cost after random attacks. RAMF is formulated as an integer-linear program and solved directly using general-purpose MIP solver. Moreover, a more complex problem, the fortification median problem for disruptions caused by mixed types of attacks (FMMA), is introduced to find a balance between defending the worst-case attacks and random attacks. Solving FMMA can achieve good protection results, which are more practical in dealing with systems with mixed types of attacks, if the proportion between the types is properly estimated. We formulate FMMA as a non-linear bilevel program and extend a typical implicit enumeration (IE) algorithm to solve the problem. Finally, computational experiments demonstrate the effectiveness of both RAMF and FMMA in dealing with protective affairs involving random attacks. The efficiency of solving the formulations of RAMF and FMMA is also testified.     

一种半监督对抗鲁棒模型无关元学习方法

元学习期望训练所得的元模型在学习到的“元知识”基础上利用来自新任务的少量标注样本，仅通过较少的梯度下降步骤微调模型就能够快速适应该任务。但是，由于缺乏训练样本，元学习算法在元训练期间对现有任务过度训练时所得的分类器决策边界不够准确，不合理的决策边界使得元模型更容易受到微小对抗扰动的影响，导致元模型在新任务上的鲁棒性能降低。提出一种半监督对抗鲁棒模型无关元学习（semi-ARMAML）方法，在目标函数中分别引入半监督的对抗鲁棒正则项和基于信息熵的任务无偏正则项，以此优化决策边界，其中对抗鲁棒正则项的计算允许未标注样本包含未见过类样本，从而使得元模型能更好地适应真实应用场景，降低对输入扰动的敏感性，提高对抗鲁棒性。实验结果表明，相比ADML、R-MAML-TRADES等当下主流的对抗元学习方法，semi-ARMAML方法在干净样本上准确率较高，在MiniImageNet数据集的5-way 1-shot与5-way 5-shot任务上对抗鲁棒性能分别约提升1.8%和2.7%，在CIFAR-FS数据集上分别约提升5.2%和8.1%。