National Institute of Standards and Technology における超伝導研究及び生活

Conventional attacks of insider employees and emerging APT are both major threats for the organizational information system. Existing detections mainly concentrate on users' behavior and usually analyze logs recording their operations in an information system. In general, most of these methods consider sequential relationship among log entries and model users' sequential behavior. However, they ignore other relationships, inevitably leading to an unsatisfactory performance on various attack scenarios. We propose log2vec, a heterogeneous graph embedding based modularized method. First, it involves a heuristic approach that converts log entries into a heterogeneous graph in the light of diverse relationships among them. Next, it utilizes an improved graph embedding appropriate to the above heterogeneous graph, which can automatically represent each log entry into a low-dimension vector. The third component of log2vec is a practical detection algorithm capable of separating malicious and benign log entries into different clusters and identifying malicious ones. We implement a prototype of log2vec. Our evaluation demonstrates that log2vec remarkably outperforms state-of-the-art approaches, such as deep learning and hidden markov model (HMM). Besides, log2vec shows its capability to detect malicious events in various attack scenarios.

Log2vec: A Heterogeneous Graph Embedding Based Approach for Detecting Cyber Threats within Enterprise

Social engineering is a growing source of information security concern. Exploits appear to evolve, with increasing levels of sophistication, in order to target multiple victims. Despite increased concern with this risk, there has been little research activity focused upon social engineering in the potentially rich hunting ground of social networks. In this setting, factors that influence users' proficiency in threat detection need to be understood if we are to build a profile of susceptible users, develop suitable advice and training programs, and generally help address this issue for those individuals most likely to become targets of social engineering in social networks. To this end, the present study proposes and validates a user-centric framework based on four perspectives: socio-psychological, habitual, socio-emotional, and perceptual. Previous research tends to rely on selected aspects of these perspectives and has not combined them into a single model for a more cohesive understanding of user's susceptibility.

/pdf/user-characteristics-that-influence-judgment-of-social-59ztpgqam7.pdf

User characteristics that influence judgment of social engineering attacks in social networks

Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.

An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement

One of the most difficult challenges in information security today is phishing. Phishing is a difficult problem to address because there are many permutations, messages, and value propositions that can be sent to targets. Spear phishing is also associated with social engineering, which can be difficult for even trained or savvy employees to detect. This makes the user the critical point of entry for miscreants seeking to perpetrate cyber crimes such as identity theft and ransomware propagation, which cause billions of dollars in losses each year. Researchers are exploring many avenues to address this problem, including educating users and making them aware of the repercussions of becoming victims of phishing. The purpose of this study was to interview security professionals to gain better insight on preventing users and employees from succumbing to phishing attack. Seven subject-matter experts were interviewed, revealing nine themes describing traits that identify users as vulnerable to attack or strongly resistive to attack, as well as training suggestions to empower users to resist spear phishing attacks. Suggestions are made for practitioners in the field and future research.

/pdf/individual-cyber-security-empowering-employees-to-resist-11ly47s517.pdf

Individual Cyber Security: Empowering Employees to Resist Spear Phishing to Prevent Identity Theft and Ransomware Attacks

This report answers some frequently asked questions about the Target and selected other data breaches, including what is known to have happened in the breach, and what costs may result. It also examines some of the broader issues common to data breaches, including how the payment system works, how cybersecurity costs are shared and allocated within the payment system, who bears the losses in such breaches more generally, what emerging cybersecurity technologies may help prevent them, and what role the government could play in encouraging their adoption. The report addresses policy issues that were discussed in the 113th Congress to deal with these issues.

https://digital.library.unt.edu/ark:/67531/metadc503332/m1/1/high_res_d/R43496_2015Feb04.pdf

The Target and Other Financial Data Breaches: Frequently Asked Questions

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The Target and Other Financial Data Breaches: Frequently Asked Questions