Social Engineering: Hacking the Wetware!

ABSTRACT

Social engineering is a methodology that allows an attacker to bypass technical controls by attacking the human element in an organization. There are many techniques commonly used in social engineering including but not limited to Trojan and phishing email messages, impersonation, persuasion, bribery, shoulder surfing, and dumpster diving. Hackers rely on social engineering attacks to bypass technical controls by focusing on the human factors. Social engineers often exploit the natural tendency people have toward trusting others who seem likeable or credible, deferring to authority or need to acquiesce to social conformity. Mitigation of social engineering begins with good policy and awareness training, but there are a number of other approaches an organization can take to defend against this type of an attack. Social engineering attacks are likely to increase, and it is becoming increasingly important for organizations to address this issue.     

Detecting Insider Threats: Solutions and Trends

ABSTRACT

Insider threats pose significant challenges to any organization. Many solutions have been proposed in the past to detect insider threats. Unfortunately, given the complexity of the problem and the human factors involved, many solutions which have been proposed face strict constraints and limitations when it comes to the working environment. As a result, many past insider threat solutions have in practice failed in their implementations. In this work, we review some of the recent insider threat detection solutions and explore their benefits and limitations. We also discuss insider threat issues for emerging areas such as cloud computing, virtualization, and social networking.     

Atom probe tomography and transmission electron microscopy characterisation of precipitation in an Al-Cu-Li-Mg-Ag alloy

State-of-the art atom probe tomography (APT) combined with transmission electron microscopy (TEM) were used to investigate the microstructure at different stages of the ageing process of an alloy of composition (at%) Al-1.68%Cu-4.62%Li-0.33%Mg-0.1%Ag. These alloys were shown to exhibit a complex microstructure of T₁ plates and several metastable phases, including θ′ and S. We will highlight the early stages of clustering, precipitate interactions and possible solute segregation at the matrix/precipitate interfaces and detail the chemical composition of the different phases.     

P2P网络中的局域搜索免疫

针对P2P网络的特性,本文提出了一种局域搜索免疫。实验使用SIS病毒传播模型征BA无标度网络和实际网络中测试了局域搜索免疫策略,结果表明与其它免疫策略相比,该策略可以通过免疫相等或较少的节点来消灭病毒。     

基于融合信息的癌症相关基因选择方法

基因表达数据的出现,为人类从分子生物学的角度研究和探索癌症的发病机理提供了广阔的前景,利用基因表达数据发现与癌症相关的基因对于癌症的诊断和治疗具有重要的意义。在过去的十几年里,已经有很多种计算方法被成功地用于从基因表达数据中找出与癌症相关的关键基因,然而,不同的方法从不同的角度刻画基因对不同类型样本的区分能力,它们选择出来的关键基因可能不一致,这将给医学解释和应用带来困扰。现提出一种融合的方法,即将基因在不同方面对样本的判别能力结合起来,首先计算每个基因的信息增益、全局判别能力和局部判别能力,再用它们的识别率进行加权,进而计算每个基因的综合判别能力,最后筛选出判别能力最高的基因子集作为关键基因子集。实验结果表明,此方法得到了比采用单独一种评价标准更好的识别效果。     

局域网环境下利用DHCP欺骗技术实现DNS劫持攻击

利用DHCP协议的缺陷,实现对局域网计算机的DNS劫持攻击,并给出将此类攻击应用于网站钓鱼的实现方法,旨在引起人们对局域网安全问题的重视。     

基于BOSS的移动营业操作系统的设计与实现

为解决移动业务数据日稽核、监控物资发放情况等问题,设计并实现了基于BOSS的移动营业操作系统。通过对BOSS克隆数据库中自动抽取业务数据与营业操作系统中业务数据的对比实现了业务稽核功能,满足了公司精细化管理的要求。实践表明,该系统有效提高了工作效率,减少了员工工作量,节约了大量的人力成本。     

Face recognition using adaptively weighted patch PZM array from a single exemplar image per person

Though numerous approaches have been proposed for face recognition, little attention is given to the moment-based face recognition techniques. In this paper we propose a novel face recognition approach based on adaptively weighted patch pseudo Zernike moment array (AWPPZMA) when only one exemplar image per person is available. In this approach, a face image is represented as an array of patch pseudo Zernike moments (PPZM) extracted from a partitioned face image containing moment information of local areas instead of global information of a face. An adaptively weighting scheme is used to assign proper weights to each PPZM to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains and the likelihood of a patch is occluded. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal conditions, different illumination conditions, different facial expressions and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that moments can be used for face recognition and patch-based moment array provides a novel way for face representation and recognition in single model databases.     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.