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With the continuous advancements of technology, cyber-criminals accordingly develop sophisticated tactics to exploit vulnerabilities in individual systems, organization networks, and nation-states. Enterprises routinely collect huge amount of securityrelevant data such as log events of people, networks, and software applications for future forensic analysis. Existing traditional security analysis tools fail to work well with large scales of data and usually produces high false alarms especially when the enterprises moves to cloud architecture and collect more data. Moreover, the detection of recent and more sophisticated attacks, like advanced persistent threats (APTs), requires continuous monitoring and analysis of huge security related data, accurately and rapidly. Big Data analytics has been in active use in several fields such as financial transactions, healthcare and industrial applications among others. Recently, it has attracted the attention of information security audience due to its promised ability in correlating security related data and draw insights efficiently at unprecedented scale. In this paper, we analyze the traditional technology/systems and Security Information and Event Management (SIEM) tools and show their shortcomings in dealing with huge data scales and advanced sophisticated threats. We then explore the requirements for Big Data analytics to be successfully adopted in cyber threat intelligence and cyber-security landscape to deal with high data scales and sophisticated threats. Finally, we highlight the challenges resulted from such adoption, and suggest some recommendations to overcome adoption challenges in future research. [ABSTRACT FROM AUTHOR]

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. [ABSTRACT FROM AUTHOR]

In order to deal with security of Mobile Cloud Computing (MCC) authentication plays an important role. It is aimed to verify user's identity when they wish to request services via the Internet through Cloud Service Provider (CSP). Personal identification number is the most common mechanism for authentication in mobile devices; however, it is not secure way for authenticating users. This work presents a new behavioral biometric authentication method which enable identify users based on Keystroke Dynamic Authentication (KDA). Furthermore, keystrokes duration is considered as an attribute for measuring keystrokes of mobile. Elliptic Curve Cryptography (ECC) is used as cryptography technique to improve the security of proposed method. Simulation results using JUnit package revealed that, even if an unauthorized person knows the username and password of legal user they cannot gain access rights on 97.33% of efforts. This is due to the keystroke duration of each user which depends on their behavioral characteristic. Therefore, applying this method, it becomes very difficult for an attacker to pretend as the owner. Hence, this method offers the potential to enhance the security of authentication in MCC. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept "Internet of Things" has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed. [ABSTRACT FROM AUTHOR]

In this paper, an improved web page classification method (IWPCM) using neural networks to identify the illicit contents of web pages is proposed. The proposed IWPCM approach is based on the improvement of feature selection of the web pages using class based feature vectors (CPBF). The CPBF feature selection approach has been calculated by considering the important term's weight for illicit web documents and reduce the dependency of the less important term's weight for normal web documents. The IWPCM approach has been examined using the modified term-weighting scheme by comparing it with several traditional term-weighting schemes for non-illicit and illicit web contents available from the web. The precision, recall, and F1 measures have been used to evaluate the effectiveness of the proposed IWPCM approach. The experimental results have shown that the proposed improved term-weighting scheme has been able to identify the non-illicit and illicit web contents available from the experimental datasets. [ABSTRACT FROM AUTHOR]

Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. [ABSTRACT FROM AUTHOR]