In the realm of today's real world, information systems are represented by complex networks. Complex networks contain a community structure inherently. Community is a set of members strongly connected within members and loosely connected with the rest of the network. Community detection is the task of revealing inherent community structure. Since the networks can be either static or dynamic, community detection can be done on both static and dynamic networks as well. In this study, we have talked about taxonomy of community detection methods with their shortages. Then we examine and categorize application areas of community detection in the realm of nature of complex networks (i.e., static or dynamic) by including sub areas of criminology such as fraud detection, criminal identification, criminal activity detection and bot detection. This paper provides a hot review and quick start for researchers and developers in community detection area.

One of the major components of the CloudComputing is "Security and Privacy". These concerns about security and privacy directly address the trustworthy and reliability levels of a system. The researches and studies about security and privacy on cloud computing are continuing. The aim of this paper is to analyze the privacy and security requirements and highlight new tools and open research topics for cloud computing systems.

With social networks (SNs) being populated by a still increasing numbers of people who take advantage of the communication and collaboration capabilities that they offer, the probability of the exposure of people's personal moments to a wider than expected audience is also increasing. By studying the functionalities and characteristics that modern SNs offer, along with the people's habits and common behaviors in them, it is easy to understand that several privacy risks may exist, many of which people may be unaware of. In this paper, we focus on users' interactions with posts in a social network (SN), using Facebook as our research domain, and we emphasize some privacy leakages currently existing in Facebook's privacy policy. We also propose a solution to detected privacy issues, featuring a reference implementation of a tool based on a simulation, which visualizes the effect of potential privacy risks on Facebook and directs users to control their privacy. The proposed and simulated tool allows a post owner to observe the spreading area of his or her post depending on the selected privacy settings. Moreover, it provides preliminary feedback for all Facebook users that have interacted with this post, to make them aware of the possible privacy changes, aiming to give them a chance to protect the privacy of their interaction on this post by deleting it when an unwanted privacy change takes place. Finally, an online survey to increase privacy awareness in Facebook usage with over 500 volunteer participants has illuminated the need for such a tool or solution.

Facebook, Twitter, LinkedIn gibi çevrimiçi sosyal ağların (OSN) popülerliği ve web servislerinin yaygınlığı, bu alanlarda sosyal bot olarak nitelendirdiğimiz yazılımsal sosyal aktörlerin ortaya çıkmasına ve yaygınlaşmasına neden olmuştur. Ancak çoğunlukla bu aktörler kötü rollerde karşımıza çıkmaktadırlar. Örneğin, sosyal botlar insanmış gibi sohbetlere katılma, başka hesapları çalarak üzerinden dolandırıcılık yapma, yanlış bilgi yayma, borsayı manipüle etme, sahte halk tabakası oluşturarak propaganda yapma gibi ciddi problemlerde karşımıza çıkmaktadırlar. Bununla beraber, istenmeyen postaları ve zararlı yazılımları yaymanın en etkin araçları haline gelmişlerdir. Dahası, botlar gerçek hesapları ele geçirerek "zombi bilgisayar ağı" (botnet attack) saldırıları düzenlemekte de kullanılmaktadırlar. Öte yandan; sosyal botların, sosyal paylaşım ağları üzerindeki yaygınlığı ve önemi inkâr edilemez bir gerçekliktir. Bu çalışmada, kötü niyetli sosyal botların potansiyel tehlikeleri vurgulanmış, literatürdeki bot tespit yaklaşımları metodolojik bir sınıflandırma içerisinde gözden geçirilmiş, bu yaklaşımların sınırları ve açık problemler sunulmuş ve bu problemleri çözmeye yönelik iki yeni yaklaşım önerilmiştir.

Tracking community evolution can provide insights into significant changes in community interaction patterns, promote the understanding of structural changes, and predict the evolutionary behavior of networks. Therefore, it is a fundamental component of decision-making mechanisms in many fields such as marketing, public health, criminology, etc. However, in this problem domain, it is an open challenge to capture all possible events with high accuracy, memory efficiency, and reasonable execution times under a single solution. To address this gap, we propose a novel method for tracking the evolution of communities (TREC). TREC efficiently detects similar communities through a combination of Locality Sensitive Hashing and Minhashing. We provide experimental evidence on four benchmark datasets and real dynamic datasets such as AS, DBLP, Yelp, and Digg and compare them with the baseline work. The results show that TREC achieves an accuracy of about 98%, has a minimal space requirement, and is very close to the best performing work in terms of time complexity. Moreover, it can track all event types in a single solution.