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Machine learning algorithms are widely used in cyber forensic biometric systems to analyze a subject's truthfulness in an interrogation. An analytical method (rather than experimental) to evaluate the security strength of these systems under potential cyber attacks is essential. In this paper, we formalize a theoretical method for analyzing the immunity of a machine learning based cyber forensic system...
In the Bitcoin network, lack of class labels tend to cause obscurities in anomalous financial behaviour interpretation. To understand fraud in the latest development of the financial sector, a multifaceted approach is proposed. In this paper, Bitcoin fraud is described from both global and local perspectives using trimmed k-means and kd-trees. The two spheres are investigated further through random...
In spite of significant on-going research, the Border gateway protocol (BGP) still encompasses conceptual vulnerability issues regarding impersonating the ownership of IP prefixes for ASes (Autonomous Systems). In this context, a number of research studies focused on securing BGP through historical-based and statistical-based behavioural models. This paper suggests a novel method based on tracking...
Researchers have previously attempted to apply machine learning techniques to network anomaly detection problems. Due to the staggering amount of variety that can occur in normal networks, as well as the difficulty in capturing realistic data sets for supervised learning or testing, the results have often been underwhelming. These challenges are far less pronounced when considering industrial control...
In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization...
User reported experiences and opinions are used by peers to make decisions about where to go and what to buy. Unfortunately, not all users or opinions are honest. Many opinions are fabricated and may be submitted by automated systems or by people who are recruited by businesses and search engine optimizers to write good reviews. Such reviews and ratings are called spam reviews. These are misleading...
Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites...
In the present world, the web is the firmest and most common medium of communication and business interchange. Every day, millions of data are loaded through various channels on the web by users and user input can be malicious. Therefore, security becomes a very important aspect of web applications. Since they are easily accessible, they are prone to many vulnerabilities which if neglected can cause...
Abstract- Voice over IP (VoIP) technologies such as Skype are becoming increasingly popular and widely used in different organisations, and therefore identifying the usage of this service at the network level becomes very important. Reasons for this include applying Quality of Service (QoS), network planning, prohibiting its use in some networks and lawful interception of communications. Researchers...
Modern communication infrastructure (IP Multimedia Subsystem (IMS) and Voice over IP (VoIP)) are vulnerable to zero day attacks and unknown threats. Anomalous SIP requests can be used to remotely launch malicious activity. Furthermore, anomalous messages are capable of crashing - sometimes with one message only - servers and end points. Recently, it is shown that a malicious SIP message "INVITE...
In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android's Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between...
This paper proposes two-level strategy for building the anomaly detection classifier, namely, macro level and micro level classification. The former intend to classify network data on a broader perspective to predict whether it is normal or a potential attack. The later classifies individual anomalies within each category of known attacks. The paper also investigates various feature selection techniques...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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