Biblio

One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.

This paper presents an anomalous IP address detection algorithm for network traffic logs. It is based on word embedding techniques derived from natural language processing to extract the representative features of IP addresses. However, the features extracted from vanilla word embeddings are not always compatible with machine learning-based anomaly detection algorithms. Therefore, we developed an algorithm that enables the extraction of more compatible features of IP addresses for anomaly detection than conventional methods. The proposed algorithm optimizes the objective functions of word embedding-based feature extraction and anomaly detection, simultaneously. According to the experimental results, the proposed algorithm outperformed conventional approaches; it improved the detection performance from 0.876 to 0.990 in the area under the curve criterion in a task of detecting the IP addresses of attackers from network traffic logs.

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.

An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.