Impersonating users’ identity in Online Social Networks (OSNs) is one of the open dilemmas from security and privacy point of view. Scammers and adversaries seek to create set of fake profiles to carry out malicious behaviors and online social crimes in social media. Recognizing the identity of Fake Profiles is an urgent issue of concern to the attention of researchers. In this paper, we propose a detection technique called Fake Profile Recognizer (FPR) for verifying the identity of profiles, and detecting the fake profiles in OSNs. The detection method in our proposed technique is based on utilizing Regular Expression (RE) and Deterministic Finite Automaton (DFA) approaches. We evaluated our proposed detection technique on three datasets types of OSNs: Facebook, Google+, and Twitter. The results explored high Precision, Recall, accuracy, and low False Positive Rates (FPR) of detecting Fake Profiles in the three datasets.

Steganography is the art or science that is used in secret communication. It means that there is a secret message that is hidden within another cover media. The cover media may be image, video or audio and the secret message may be any type of digital message. The hidden message doesn't have any relationship with the cover media where the cover media is just to protect the secret message from hacking by unauthorized receiver. The audio cover is used in this paper because of the higher sensitivity of the human auditory system (HAS) than the human visual system (HVS). In this paper, we proposed a hybrid technique to audio steganography. This technique is based on a hybrid between two techniques of audio steganography. These techniques are Least Significant Bit (LSB) technique and modification of phase coding. The hybrid between them is for improving the performance of the phase coding where the performance of it is very low. Audio steganography performance is measured by several factors, the most important one of them is Signal to noise ratio (SNR) which is used to compare the performance of our technique with some known techniques.

This paper presents a dataflow model to control the flow of data in each phase of a customer-oriented software process model. In addition, we suggest a workflow model to describe the transaction between the model phases, and a role model to govern the personnel participation and roles. Our goal is to develop models that involve the customer frequently and effectively during project development. Testing the models using CHAOS Report and shows that our models are capable of achieving this goal. 

Steganography is the art of amalgamating the secret message into another public message which may be text, audio or video file in a way that no one can know or imperceptible the existence of message. So, the secret message can send in a secret and obscure way using steganography techniques. In this paper, we use the audio steganography where the secret message conceal in audio file. We use audio rather than image because the human auditory system (HAS) is more sensitive than human visual system (HVS). We propose an audio steganography algorithm, for embedding text, audio or image based on Lifting Wavelet Transform (LWT) transform with modification of Least Significant Bit (LSB) technique and three random keys where these key is used to increase the robustness of the LSB technique and without it no one can know the sort of secret message type, the length of the secret message and the initial position of the embedded secret message in LSB. The performance of our algorithm is calculated using SNR and we compare the values of our proposed method with some known algorithms.

Online Social Networks (OSN) are considering one of the most popular internet applications which attract millions of users around the world to build several social relationships. Emerging the Web 2.0 technology allowed OSN users to create, share, or exchange types of contents in a popular fashion. The other hand, OSN are considering one of the most popular platforms for the intruders to spread several types of OSN attacks. Creating fake profiles for launching cloning attacks is one of the most risky attacks which target Users' profiles in Online Social Networks, the attacker seek to impersonate user's identity through duplicating user's online presence in the same or across several social networks, therefore, he can deceive OSN users into forming trusting social relations with his created fake profiles. These malicious profiles aim to harvest sensitive user's information or misuse the reputation of the legitimate profile's owner, as well as it may be used as a spy profiles for other criminal parties. Detecting these fake profiles still represent a major problem from OSN Security and Privacy point of view. In this paper we introduced a theoretical framework which depends on a novel topology of a social graph called Trusted Social Graph (TSG) which used to visualize trusted instances of social communications between OSN users. Another contribution is a proposed detection model that based on TSG topology as well as two techniques; Deterministic Finite Automaton (DFA) and Regular Expression. Our proposed detection model used to recognize the stranger instances of communications and social actions that performed using fake profiles in OSN.

Centrality is an important concept in the study of social network analysis (SNA), which is used to measure the importance of a node in a network. While many different centrality measures exist, most of them are proposed and applied to static networks. However, most types of networks are dynamic that their topology changes over time. A popular approach to represent such networks is to construct a sequence of time windows with a single aggregated static graph that aggregates all edges observed over some time period. In this paper, an approach which overcomes the limitation of this representation is proposed based on the notion of the time-ordered graph, to measure the communication centrality of a node in dynamic networks.