Network-aware Active Wardens in IPv6

Every day the world grows more and more dependent on digital communication. Technologies like e-mail or the World Wide Web that not so long ago were considered experimental, have first become accepted and then indispensable tools of everyday life. New communication technologies built on top of the existing ones continuously race to provide newer and better functionality. Even established communication media like books, radio, or television have become digital in an effort to avoid extinction. In this torrent of digital communication a constant struggle takes place. On one hand, people, organizations, companies and countries attempt to control the ongoing communications and subject them to their policies and laws. On the other hand, there oftentimes is a need to ensure and protect the anonymity and privacy of the very same communications. Neither side in this struggle is necessarily noble or malicious. We can easily imagine that in presence of oppressive censorship two parties might have a legitimate reason to communicate covertly. And at the same time, the use of digital communications for business, military, and also criminal purposes gives equally compelling reasons for monitoring them thoroughly. Covert channels are communication mechanisms that were never intended nor designed to carry information. As such, they are often able to act ``below\u27\u27 the notice of mechanisms designed to enforce security policies. Therefore, using covert channels it might be possible to establish a covert communication that escapes notice of the enforcement mechanism in place. Any covert channel present in digital communications offers a possibility of achieving a secret, and therefore unmonitored, communication. There have been numerous studies investigating possibilities of hiding information in digital images, audio streams, videos, etc. We turn our attention to the covert channels that exist in the digital networks themselves, that is in the digital communication protocols. Currently, one of the most ubiquitous protocols in deployment is the Internet Protocol version 4 (IPv4). Its universal presence and range make it an ideal candidate for covert channel investigation. However, IPv4 is approaching the end of its dominance as its address space nears exhaustion. This imminent exhaustion of IPv4 address space will soon force a mass migration towards Internet Protocol version 6 (IPv6) expressly designed as its successor. While the protocol itself is already over a decade old, its adoption is still in its infancy. The low acceptance of IPv6 results in an insufficient understanding of its security properties. We investigated the protocols forming the foundation of the next generation Internet, Internet Protocol version 6 (IPv6) and Internet Control Message Protocol (ICMPv6) and found numerous covert channels. In order to properly assess their capabilities and performance, we built cctool, a comprehensive covert channel tool. Finally, we considered countermeasures capable of defeating discovered covert channels. For this purpose we extended the previously existing notions of active wardens to equip them with the knowledge of the surrounding network and allow them to more effectively fulfill their role

An ensemble model to detect packet length covert channels

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors

PadSteg: Introducing Inter-Protocol Steganography

Hiding information in network traffic may lead to leakage of confidential information. In this paper we introduce a new steganographic system: the PadSteg (Padding Steganography). To authors' best knowledge it is the first information hiding solution which represents inter-protocol steganography i.e. usage of relation between two or more protocols from the TCP/IP stack to enable secret communication. PadSteg utilizes ARP and TCP protocols together with an Etherleak vulnerability (improper Ethernet frame padding) to facilitate secret communication for hidden groups in LANs (Local Area Networks). Basing on real network traces we confirm that PadSteg is feasible in today's networks and we estimate what steganographic bandwidth is achievable while limiting the chance of disclosure. We also point at possible countermeasures against PadSteg.Comment: 9 pages, 12 figures, 5 table

"The Good, The Bad And The Ugly": Evaluation of Wi-Fi Steganography

In this paper we propose a new method for the evaluation of network steganography algorithms based on the new concept of "the moving observer". We considered three levels of undetectability named: "good", "bad", and "ugly". To illustrate this method we chose Wi-Fi steganography as a solid family of information hiding protocols. We present the state of the art in this area covering well-known hiding techniques for 802.11 networks. "The moving observer" approach could help not only in the evaluation of steganographic algorithms, but also might be a starting point for a new detection system of network steganography. The concept of a new detection system, called MoveSteg, is explained in detail.Comment: 6 pages, 6 figures, to appear in Proc. of: ICNIT 2015 - 6th International Conference on Networking and Information Technology, Tokyo, Japan, November 5-6, 201

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection

Hijacking User Uploads to Online Persistent Data Repositories for Covert Data Exfiltration

As malware has evolved over the years, it has gone from harmless programs that copy themselves into other executables to modern day botnets that perform bank fraud and identity theft. Modern malware often has a need to communicate back to the author, or other machines that are also infected. Several techniques for transmitting this data covertly have been developed over the years which vary significantly in their level of sophistication. This research creates a new covert channel technique for stealing information from a network by piggybacking on user-generated network traffic. Specifically, steganography drop boxes and passive covert channels are merged to create a novel covert data exfiltration technique. This technique revolves around altering user supplied data being uploaded to online repositories such as image hosting websites. It specifically targets devices that are often used to generate and upload content to the Internet, such as smartphones. The reliability of this technique is tested by creating a simulated version of Flickr as well as simulating how smartphone users interact with the service. Two different algorithms for recovering the exfiltrated data are compared. The results show a clear improvement for algorithms that are user-aware. The results continue on to compare performance for varying rates of infection of mobile devices and show that performance is proportional to the infection rate

A Deep Learning Based Approach To Detect Covert Channels Attacks and Anomaly In New Generation Internet Protocol IPv6

The increased dependence of internet-based technologies in all facets of life challenges the government and policymakers with the need for effective shield mechanism against passive and active violations. Following up with the Qatar national vision 2030 activities and its goals for “Achieving Security, stability and maintaining public safety” objectives, the present paper aims to propose a model for safeguarding the information and monitor internet communications effectively. The current study utilizes a deep learning based approach for detecting malicious communications in the network traffic. Considering the efficiency of deep learning in data analysis and classification, a convolutional neural network model was proposed. The suggested model is equipped for detecting attacks in IPv6. The performance of the proposed detection algorithm was validated using a number of datasets, including a newly created dataset. The performance of the model was evaluated for covert channel, DDoS attacks detection in IPv6 and for anomaly detection. The performance assessment produced an accuracy of 100%, 85% and 98% for covert channel detection, DDoS detection and anomaly detection respectively. The project put forward a novel approach for detecting suspicious communications in the network traffic