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With the evolution of internet, there has been an unprecedented and unlimited growth in volume, velocity, veracity and variety of the data and the complexity of data attributes is on the rise. Further, in the domain of internet, data is not geo-centric any longer and multiple locations are contributing to the data acquisition technologies including but not limited to packet captures, data logs, routing...
State of the art network intrusion detection systems are heavily influenced by signature based techniques for detecting threats which are extracted from raw packet captures and firewall logs. With the recent emergence of cloud computing and big data analytics, supervised machine learning is also being used to detect deviations of the network traffic patterns from already-known normal patterns. Subsequently,...
Contrastive Divergence (CD) has shown success in estimating the parameters of Markov Random Fields. Restricted Boltzmann Machine (RBM) updates weights of RBM architecture using CD to minimize the reconstruction error. Minimization of CD is fundamentally equivalent of finding the self-similarity among the training data and the estimated data. However, this self-similarity is measured at single measurement...
The recent blaze in cyber espionage has posed unprecedented challenges to the cutting edge network intrusion detection systems in terms of accurate and precise classification of dynamically evolving threats. Along with the traditional signature based detection, the supervised and unsupervised machine learning algorithms are also being deployed to detect advance anomalies. However, due to the class...
Polymorphic malware belong to the class of host based threats which defy signature based detection mechanisms. Threat actors use various code obfuscation methods to hide the code details of the polymorphic malware and each dynamic iteration of the malware bears different and new signatures therefore makes its detection harder by signature based antimalware programs. Sandbox based detection systems...
This paper presents a method for detecting anomalous power consumption patterns attacks, using a discrete wavelet transform, as well as the variance fractal dimension (VFD) and an artificial neural network (ANN) for a smart grid. The main procedure of the proposed algorithm consists of the following steps: (i) Finding normal and anomalous patterns of power consumption to train the proposed method,...
Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated in the original AdaBoost machine learning...
This paper presents a cognitive feature extraction model based on scaling and multifractal dimension trajectory to analyze internet traffic time series. DNS (Domain Naming System) traffic time series is considered that contains tagged DNS Denial of Service attacks. The first step of the analysis involves transforming the DNS time series into a multifractal variance dimension trajectory keeping statistical...
Today's evolving cyber security threats demand new, modern, and cognitive computing approaches to network security systems. In the early years of the Internet, a simple packet inspection firewall was adequate to stop the then-contemporary attacks, such as Denial of Service (DoS), ports scans, and phishing. Since then, DoS has evolved to include Distributed Denial of Service (DDoS) attacks, especially...
Two different variations of chaotic simulated annealing were applied to combinatorial optimization problems in multiprocessor task allocation. Chaotic walks in the solution space were taken to search for the global optimum or “good enough” task-to-processor allocation solutions. Chaotic variables were generated to set the number of perturbations made in each iteration of a chaotic simulated annealing...
This paper reports on the impact of using custom animation software to teach second/third year computer/electrical engineering students in the microprocessing systems course at the University of Manitoba, Winnipeg, MB, Canada. Over the span of 13 years' experience with teaching the course, the difficulties and limitations with conventional lectures and visual aids led to the development of custom...
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