KEYWORDS: Social networks, Associative arrays, Steganography, Data hiding, Multimedia, Web 2.0 technologies, Genetic algorithms, Reliability, Image processing, Internet
A new method for information hiding in Open Social Networks named SocialStegDisc was designed as an application of the StegHash method by applying the theory of filesystems. The mechanism of a linked list was added into the design to provide the set of basic operations on files: creation, reading, deletion and modification. It establishes a new kind of mass-storage characterized by unlimited data space. SocialStegDisc optimizes the operation of the original version of StegHash by a trade-off between the memory requirements and computation time. Features, limitations and opportunities were discussed. The proposed system could create a completely new area of threats in social networks.
The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms – Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.
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