Biblio
To protect against misuse of passwords compromised in a breach, consumers should promptly change affected passwords and any similar passwords on other accounts. Ideally, affected companies should strongly encourage this behavior and have mechanisms in place to mitigate harm. In order to make recommendations to companies about how to help their users perform these and other security-enhancing actions after breaches, we must first have some understanding of the current effectiveness of companies’ post-breach practices. To study the effectiveness of password-related breach notifications and practices enforced after a breach, we examine—based on real-world password data from 249 participants—whether and how constructively participants changed their passwords after a breach announcement. Of the 249 participants, 63 had accounts on breached domains; only 33% of the 63 changed their passwords and only 13% (of 63) did so within three months of the announcement. New passwords were on average 1.3× stronger than old passwords (when comparing log10-transformed strength), though most were weaker or of equal strength. Concerningly, new passwords were overall more similar to participants’ other passwords, and participants rarely changed passwords on other sites even when these were the same or similar to their password on the breached domain. Our results highlight the need for more rigorous passwordchanging requirements following a breach and more effective breach notifications that deliver comprehensive advice.
The widespread adoption of social networking and cloud computing has transformed today's Internet to a trove of personal information. As a consequence, data breaches are expected to increase in gravity and occurrence. To counteract unintended data disclosure, a great deal of effort has been dedicated in devising methods for uncovering privacy leaks. Existing solutions, however, have not addressed the time- and data-intensive nature of leak detection. The shift from hardware-specific implementation to software-based solutions is the core idea behind the concept of Network Function Virtualization (NFV). On the other hand, the Software Defined Networking (SDN) paradigm is characterized by the decoupling of the forwarding and control planes. In this paper, an SDN/NFV-enabled architecture is proposed for improving the efficiency of leak detection systems. Employing a previously developed identification strategy, Personally Identifiable Information detector (PIID) and load balancer VNFs are packaged and deployed in OpenStack through an NFV MANO. Meanwhile, SDN controllers permit the load balancer to dynamically redistribute traffic among the PIID instances. In a physical testbed, tests are conducted to evaluate the proposed architecture. Experimental results indicate that the proportions of forwarding and parsing on total overhead is influenced by the traffic intensity. Furthermore, an NFV-enabled system with scalability features was found to outperform a non-virtualized implementation in terms of latency (85.1%), packet loss (98.3%) and throughput (8.41%).
A multitude of leaked data can be purchased through the Dark Web nowadays. Recent reports highlight that the largest footprints of leaked data, which range from employee passwords to intellectual property, are linked to governmental institutions. According to OWL Cybersecurity, the US Navy is most affected. Thinking of leaked data like personal files, this can have a severe impact. For example, it can be the cornerstone for the start of sophisticated social engineering attacks, for getting credentials for illegal system access or installing malicious code in the target network. If personally identifiable information or sensitive data, access plans, strategies or intellectual property are traded on the Dark Web, this could pose a threat to the armed forces. The actual impact, role, and dimension of information treated in the Dark Web are rarely analysed. Is the available data authentic and useful? Can it endanger the capabilities of armed forces? These questions are even more challenging, as several well-known cases of deanonymization have been published over recent years, raising the question whether somebody really would use the Dark Web to sell highly sensitive information. In contrast, fake offers from scammers can be found regularly, only set up to cheat possible buyers. A victim of illegal offers on the Dark Web will typically not go to the police. The paper analyses the technical base of the Dark Web and examines possibilities of deanonymization. After an analysis of Dark Web marketplaces and the articles traded there, a discussion of the potential risks to military operations will be used to identify recommendations on how to minimize the risk. The analysis concludes that surveillance of the Dark Web is necessary to increase the chance of identifying sensitive information early; but actually the `open' internet, the surface web and the Deep Web, poses the more important risk factor, as it is - in practice - more difficult to surveil than the Dark Web, and only a small share of breached information is traded on the latter.
The recently applied General Data Protection Regulation (GDPR) aims to protect all EU citizens from privacy and data breaches in an increasingly data-driven world. Consequently, this deeply affects the factory domain and its human-centric automation paradigm. Especially collaboration of human and machines as well as individual support are enabled and enhanced by processing audio and video data, e.g. by using algorithms which re-identify humans or analyse human behaviour. We introduce most significant impacts of the recent legal regulation change towards the automations domain at a glance. Furthermore, we introduce a representative scenario from production, deduce its legal affections from GDPR resulting in a privacy-aware software architecture. This architecture covers modern virtualization techniques along with authorization and end-to-end encryption to ensure a secure communication between distributes services and databases for distinct purposes.