5 research outputs found

    Hybrid Approach for Botnet Detection Using K-Means and K-Medoids with Hopfield Neural Network

    Get PDF
    In the last few years, a number of attacks and malicious activities have been attributed to common channels between users. A botnet is considered as an important carrier of malicious and undesirable briskness. In this paper, we propose a support vector machine to classify botnet activities according to k-means, k-medoids, and neural network clusters. The proposed approach is based on the features of transfer control protocol packets. System performance and accuracy are evaluated using a predefined data set. Results show the ability of the proposed approach to detect botnet activities with high accuracy and performance in a short execution time. The proposed system provides 95.7% accuracy rate with a false positive rate less than or equal to 3%

    Novel Approach for Intrusion Detection Using Simulated Annealing Algorithm Combined with Hopfield Neural Network

    Get PDF
    With the continued increase in Internet usage, the risk of encountering online threats remains high. This study proposes a new approach for intrusion detection to produce better outcomes than similar approaches with high accuracy rates. The proposed approach uses Simulated Annealing algorithms [1] combined with Hopfield Neural network [2] for supervised learning to improve performance by increasing the correctness of true detection and reducing the error rates as a result of false detection. The proposed approach is evaluated on an intrusion detection data set called KDD99[3]. Experimental tests demonstrate the potential of the proposed approach to rapidly detect high precision and efficiency intrusion behaviors. The proposed approach offers a 99.16% accuracy rate and a 0.3% false-positive rate.Department of Information Technology

    Mobile Malware Behaviour through Opcode Analysis

    Get PDF
    As the popularity of mobile devices are on the rise, millions of users are now exposed to mobile malware threats. Malware is known for its ability in causing damage to mobile devices. Attackers often use it as a way to use the resources available and for other cybercriminal benefits such stealing users’ data, credentials and credit card number. Various detection techniques have been introduced in mitigating mobile malware, yet the malware author has its own method to overcome the detection method. This paper presents mobile malware analysis approaches through opcode analysis. Opcode analysis on mobile malware reveals the behaviour of malicious application in the binary level. The comparison made between the numbers of opcode occurrence from a malicious application and benign shows a significance traits. These differences can be used in classifying the malicious and benign mobile application

    Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

    Get PDF
    While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications in various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

    Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

    Get PDF
    While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances
    corecore