Machine learning (ML)-based network anomaly detection methods are proven to provide automated network protection from traffic misbehavior and authorized system access through data monitoring and analysis. However, conventional centralized methods present risks for data privacy and breaches. By facilitating distributed model training over a number of network nodes, Federated Learning (FL) emerges as a key enabler for effective anomaly detection yet while preserving the privacy of the data. This paper studies FL-based detection approaches under two different Deep Learning models, CNN and MLP. We use XGBoost for feature selection and the two UNSWNB15 and CICDDoS2019 datasets for assessing the effectiveness of each model through the evaluation of standard performance metric criteria, namely the recall, precision, accuracy, and F1score metrics. Our experimental findings indicate that integrating XGBoost-based feature selection with the CNN model yields superior performance on the UNSW-NB15 dataset, whereas the MLP model benefits more from the same integration when applied to the CICDDoS2019 dataset.

Rising phishing attacks pose serious cybersecurity threats due to their use of fraudulent links to collect confidential user information. In this paper, we evaluate the performance of various Machine Learning (ML) models, including Decision Trees, Random Forest, and Extreme Gradient Boosting, to address this growing threat. Additionally, we assess the effectiveness of different feature selection techniques, such as Analysis of Variance, Correlation-based Selection, Mutual Information, and Recursive Feature Elimination with Cross-Validation. Our findings demonstrate that combining Extreme Gradient Boosting with Recursive Feature Elimination and Cross-Validation outperforms previous methods. The proposed solution achieved an accuracy of 97.33%, a recall of 97.1656%, an F1 score of 97.3%, and a precision of 97.42%, highlighting its potential for effectively identifying phishing attacks

SQL injection attacks are one of the most devastating vulnerabilities on the web, which can be used to leak sensitive information, gain unauthorized access, and result in financial losses. We utilized five distinct Machine Learning (ML) and Deep Learning (DL) algorithms to detect SQL injection attacks. We extracted features from network traffic and SQL queries through tokenization and regular expressions. Our findings show that combining the Term Frequency-Inverse Document Frequency (TF-IDF), Vanilla Generative Adversarial Network (VGAN), and eXtreme Gradient Boosting (XGB) models resulted in remarkably high performance metrics, including 99.95 % accuracy, 99.92 % precision, 99.97 % recall, and a 99.95 % F1-score. The results not only show the combination's effectiveness but also emphasize the importance of using machine learning techniques to detect SQL injection attacks to improve real-world cybersecurity applications significantly.

In an ever-evolving digital landscape, the significance of robust cybersecurity measures continues to grow. This paper explores the efficacy of fortifying organizational defenses through the adoption of open-source Security Information and Event Management (SIEM) solutions. Through a comprehensive analysis, it is revealed that Wazuh emerges as the optimal choice due to its notable attributes including reliability, cost-effectiveness, high endpoint availability, robust file monitoring capabilities, intrusion detection prowess, and scalability. These findings offer valuable insights for organizations seeking to bolster their cybersecurity infrastructure, empowering them to make informed decisions regarding the integration of Wazuh into their security frameworks.

With the progression and sophistication of technology, the frequency and intricacy of cyber-attacks also escalate. Malicious hackers and cybercriminals are perpetually devising novel techniques to infiltrate computer systems and steal vital data illicitly. To tackle these risks, organizations must deploy efficient Intrusion Detection Systems (IDSs) to detect and respond to attacks promptly. In recent times, IDSs have garnered considerable interest due to their efficacy and accuracy in identifying anomalous patterns in network traffic through the utilization of machine learning methodologies. This study aims to develop a model capable of detecting intrusions by utilizing several machine learning algorithms on the selected features derived from the modeling procedure. Multicollinearity is a statistical method employed to identify high relationships between variables. This study uses a Synthetic Minority Oversampling Technique (SMOTE) to tackle the problem of imbalanced datasets. SMOTE is a powerful technique that can successfully balance the distribution of classes and improve the accuracy of a machine learning model in identifying the underrepresented class. A study is done to examine several machine learning approaches and evaluate the efficiency of the proposed methodology. The performance of Machine Learning algorithms on the well-established Intrusion Detection benchmark CSE-CIC-IDS2018 and KDD CUP99 datasets shows great potential.

Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

Speech and music discrimination task is considered among the most important tools in several multimedia applications. In this paper, we propose a twofold approach for speech/music discrimination: in the first side, we consider a relevant and rich set of features then in the second one, we adopt mid-term level statistics. The used set of features involves musical descriptors as well as speech and cepstral descriptors. According to the retrieved results, standard deviation metric was elected as the best mid-term level statistic parameter. The SVM classifier has provided the higher accuracy value among the set of employed classifiers. And when combined to the standard deviation statistical parameter, it has reached a satisfying accuracy percentage higher than 99%. Thus, the proposed scheme has achieved promising classification performance thanks to the discriminating abilities and diversity of the used features besides to the statistics on mid term level.

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The Expectation Maximization algorithm has shown to be an efficient solution to estimate the collusion channel and thus to tune the Tardos accusation functions. In this paper, we explore the impact of this algorithm in a group-based tracing scheme to deal with the computational costs and the invariance of the Tardos accusation performance. The tracing scheme we propose benefits from a twofold accusation process. Indeed, in a first time, it is based on a two-level tracing strategy which consists in tracing guilty groups in a first level with the Boneh Shaw tracing code and in retrieving at least one colluder in accused groups with Tardos code in the second level. This strategy has reduced efficiently the decoding process of the Tardos code. The main shift we propose in the second level is to apply the Expectation Maximization algorithm to be tightly tied to collusion yielded by colluders and hence to find the more accurate Tardos accusation functions. The performance of the resulting tracing scheme is evaluated according to different criteria and promising results have been achieved when compared to the existing tracing schemes proposed in the literature.

According to the ever development of multimedia distribution systems, more than one technique was proposed in the literature to address the copyright protection issue. One key technique was to propose a fingerprinting system based on traitor tracing process to retrieve back the traitorous users who can operate in the mid-way. Some previous works agree upon the fact that users belonging to the same group have more probability to collude together. Several researchers in the tracing traitor field agree upon the fact that constructing a group-based fingerprint should enhance the detection rates of the fingerprinting system. In this paper, we propose to generate a fingerprint having the group property by using a clustering algorithm. We propose to construct a group-based fingerprint according to a DCT-based audio watermarking technique which has proven good robustness and inaudibility results. To show the impact of the classifying algorithm, a set of experimental tests are conducted to check two relevant criteria: the capacity and the security of the group-based fingerprint.

Due to the incessant explosion of the multimedia documents amount, the use of metadata is becoming crucial to facilitate the retrieval and the management of these audiovisual contents. Metadata creation is highly time and resources consuming even if the process is automatically done. Thus, video browsing systems uses existing metadata files generally jointed to the corresponding video for efficient semantic multimedia content retrieval. However, missing the metadata file renders the related video useless. So, in this paper, a new strategy for video characterization is described by embedding the metadata information using a blind watermarking technique. Consequently, browsing systems can use the beforehand indexed content just by extracting the corresponding metadata.

The encroachment of Internet and Peer to Peer networks has really facilitated our daily lives and works but it contributes to another dangerous phenomenon which is copying a digital content without having authorization, called piracy. To handle this phenomenon, several techniques of tracing traitors were proposed, by combining in the same time a fingerprinting technique to a watermarking one. In this paper, we first present basic notions for multimedia traceability framework: Anti Collusion code (ACC) and the watermarking technique. We show a study of available tracing traitors' schemes and we propose a comparison of accusation ability and computational costs of these techniques. Next we describe our future contribution in this target.