38 research outputs found
A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection
Enterprise networks that host valuable assets and services are popular and
frequent targets of distributed network attacks. In order to cope with the
ever-increasing threats, industrial and research communities develop systems
and methods to monitor the behaviors of their assets and protect them from
critical attacks. In this paper, we systematically survey related research
articles and industrial systems to highlight the current status of this arms
race in enterprise network security. First, we discuss the taxonomy of
distributed network attacks on enterprise assets, including distributed
denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing
methods in monitoring and classifying network behavior of enterprise hosts to
verify their benign activities and isolate potential anomalies. Third,
state-of-the-art detection methods for distributed network attacks sourced from
external attackers are elaborated, highlighting their merits and bottlenecks.
Fourth, as programmable networks and machine learning (ML) techniques are
increasingly becoming adopted by the community, their current applications in
network security are discussed. Finally, we highlight several research gaps on
enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive
Robustness of Image-Based Malware Analysis
In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider
Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey
Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio
Mass Removal of Botnet Attacks Using Heterogeneous Ensemble Stacking PROSIMA classifier in IoT
In an Internet of Things (IoT) environment, any object, which is equipped with sensor node and other electronic devices can involve in the communication over wireless network. Hence, this environment is highly vulnerable to Botnet attack. Botnet attack degrades the system performance in a manner difficult to get identified by the IoT network users. The Botnet attack is incredibly difficult to observe and take away in restricted time. there are challenges prevailed in the detection of Botnet attack due to number of reasons such as its unique structurally repetitive nature, performing non uniform and dissimilar activities and invisible nature followed by deleting the record of history. Even though existing mechanisms have taken action against the Botnet attack proactively, it has been observed failing to capture the frequent abnormal activities of Botnet attackers .When number of devices in the IoT environment increases, the existing mechanisms have missed more number of Botnet due to its functional complexity. So this type of attack is very complex in nature and difficult to identify. In order to detect Botnet attack, Heterogeneous Ensemble Stacking PROSIMA classifier is proposed. This takes advantage of cluster sampling in place of conventional random sampling for higher accuracy of prediction. The proposed classifier is tested on an experimental test setup with 20 nodes. The proposed approach enables mass removal of Botnet attack detection with higher accuracy that helps in the IoT environment to maintain the reliability of the entire network
Machine and deep learning techniques for detecting internet protocol version six attacks: a review
The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS