Biblio

Currently the use and production of multimedia data such as digital images have increased due to its wide use within smart devices and open networks. Although this has some advantages, it has generated several issues related to the infraction of intellectual property. Digital image watermarking is a promissory solution to solve these issues. Considering the need to develop mechanisms to improve the information security as well as protect the intellectual property of the digital images, in this paper we propose a novel visible-imperceptible watermarking based on reversible data hiding with contrast enhancement. In this way, a watermark logo is embedded in the spatial domain of the original image imperceptibly, so that the logo is revealed applying reversible data hiding increasing the contrast of the watermarked image and the same time concealing a great amount of data bits, which are extracted and the watermarked image restored to its original conditions using the reversible functionality. Experimental results show the effectiveness of the proposed algorithm. A performance comparison with the current state-of-the-art is provided.

The purpose of this work is to implement a universal system for collecting and analyzing event logs from sources that use the Windows operating system. The authors use event-forwarding technology to collect data from logs. Security information and event management detects incidents from received events. The authors analyze existing methods for transmitting event log entries from sources running the Windows operating system. This article describes in detail how to connect event sources running on the Windows operating system to the event collector without connecting to a domain controller. Event sources are authenticated using certificates created by the event collector. The authors suggest a scheme for connecting the event collector to security information and event management. Security information and event management must meet the requirements for use in conjunction with event forwarding technology. The authors of the article demonstrate the scheme of the test stand and the result of testing the event forwarding technology.

In this paper, an agent-based cross-layer secure service discovery scheme has been presented. Service discovery in MANET is a critical task and it presents numerous security challenges. These threats can compromise the availability, privacy and integrity of service discovery process and infrastructure. This paper highlights various security challenges prevalent to service discovery in MANET. Then, in order to address these security challenges, the paper proposes a cross-layer, agent based secure service discovery scheme for MANET based on deep neural network. The software agents will monitor the intrusive activities in the network based on an Intrusion Detection System (IDS). The service discovery operation is performed based on periodic dissemination of service, routing and security information. The QoS provisioning is achieved by encapsulating QoS information in the periodic advertisements done by service providers. The proposed approach has been implemented in JIST/ SWANS simulator. The results show that proposed approach provides improved security, scalability, latency, packet delivery ratio and service discovery success ratio, for various simulation scenarios.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

The timely exchange of information on new threats and vulnerabilities has become a cornerstone of effective cyber defence in recent years. Especially national authorities increasingly assume their role as information brokers through national cyber security centres and distribute warnings on new attack vectors and vital recommendations on how to mitigate them. Although many of these initiatives are effective to some degree, they also suffer from severe limitations. Many steps in the exchange process require extensive human involvement to manually review, vet, enrich, analyse and distribute security information. Some countries have therefore started to adopt distributed cyber security sensor networks to enable the automatic collection, analysis and preparation of security data and thus effectively overcome limiting scalability factors. The basic idea of IoC-centric cyber security sensor networks is that the national authorities distribute Indicators of Compromise (IoCs) to organizations and receive sightings in return. This effectively helps them to estimate the spreading of malware, anticipate further trends of spreading and derive vital findings for decision makers. While this application case seems quite simple, there are some tough questions to be answered in advance, which steer the further design decisions: How much can the monitored organization be trusted to be a partner in the search for malware? How much control of the scanning process should be delegated to the organization? What is the right level of search depth? How to deal with confidential indicators? What can be derived from encrypted traffic? How are new indicators distributed, prioritized, and scan targets selected in a scalable manner? What is a good strategy to re-schedule scans to derive meaningful data on trends, such as rate of spreading? This paper suggests a blueprint for a sensor network and raises related questions, outlines design principles, and discusses lessons learned from small-scale pilots.

It is challenging in Security information and Platforms of Communication by Visible Light (VLC), solutions are made to manage the right Security problems. Several solutions have been developed and evolved constantly to meet complex and ever-changing business needs in the world. In the business context, people who are responsible for a project or an organization undergo professional and emotional stress. This research project has developed a new model which can help decision makers evaluating these alternative methods in relation to articulating different types of Security problems, formulating Security criteria, and simulating expectations of adopting the chosen method for Platforms of Communication by Visible Light (VLC).

Sharing cyber security data across organizational boundaries brings both privacy risks in the exposure of personal information and data, and organizational risk in disclosing internal information. These risks occur as information leaks in network traffic or logs, and also in queries made across organizations. They are also complicated by the trade-offs in privacy preservation and utility present in anonymization to manage disclosure. In this paper, we define three principles that guide sharing security information across organizations: Least Disclosure, Qualitative Evaluation, and Forward Progress. We then discuss engineering approaches that apply these principles to a distributed security system. Application of these principles can reduce the risk of data exposure and help manage trust requirements for data sharing, helping to meet our goal of balancing privacy, organizational risk, and the ability to better respond to security with shared information.

Wireless Sensor Networks (WSN) is vulnerable to node capture attacks in which an attacker can capture one or more sensor nodes and reveal all stored security information which enables him to compromise a part of the WSN communications. Due to large number of sensor nodes and lack of information about deployment and hardware capabilities of sensor node, key management in wireless sensor networks has become a complex task. Limited memory resources and energy constraints are the other issues of key management in WSN. Hence an efficient key management scheme is necessary which reduces the impact of node capture attacks and consume less energy. By simulation results, we show that our proposed technique efficiently increases packet delivery ratio with reduced energy consumption.