Enabling non-expert analysis of large volumes of intercepted network traffic

The Fourteenth Annual IFIP WG 11.9 International Conference on Digital Forensics (Digital Forensics 2018), New Delhi, India, 3-5 January 2018Telecommunications wiretaps are commonly used by law enforcement in criminal investigations. While phone-based wiretapping has seen considerable success, the same cannot be said for Internet taps. Large portions of intercepted Internet traffic are often encrypted, making it difficult to obtain useful information. The advent of the Internet of Things further complicates network wiretapping. In fact, the current level of complexity of intercepted network traffic is almost at the point where data cannot be analyzed without the active involvement of experts. Additionally, investigations typically focus on analyzing traffic in chronological order and predominately examine the data content of the intercepted traffic. This approach is overly arduous when the amount of data to be analyzed is very large. This chapter describes a novel approach for analyzing large amounts of intercepted network traffic based on traffic metadata. The approach significantly reduces the analysis time and provides useful insights and information to non-technical investigators. The approach is evaluated using a large sample of network traffic data

Practical Analysis of Encrypted Network Traffic

The growing use of encryption in network communications is an undoubted boon for user privacy. However, the limitations of real-world encryption schemes are still not well understood, and new side-channel attacks against encrypted communications are disclosed every year. Furthermore, encrypted network communications, by preventing inspection of packet contents, represent a significant challenge from a network security perspective: our existing infrastructure relies on such inspection for threat detection. Both problems are exacerbated by the increasing prevalence of encrypted traffic: recent estimates suggest that 65% or more of downstream Internet traffic will be encrypted by the end of 2016. This work addresses these problems by expanding our understanding of the properties and characteristics of encrypted network traffic and exploring new, specialized techniques for the handling of encrypted traffic by network monitoring systems. We first demonstrate that opaque traffic, of which encrypted traffic is a subset, can be identified in real-time and how this ability can be leveraged to improve the capabilities of existing IDS systems. To do so, we evaluate and compare multiple methods for rapid identification of opaque packets, ultimately pinpointing a simple hypothesis test (which can be implemented on an FPGA) as an efficient and effective detector of such traffic. In our experiments, using this technique to “winnow”, or filter, opaque packets from the traffic load presented to an IDS system significantly increased the throughput of the system, allowing the identification of many more potential threats than the same system without winnowing. Second, we show that side channels in encrypted VoIP traffic enable the reconstruction of approximate transcripts of conversations. Our approach leverages techniques from linguistics, machine learning, natural language processing, and machine translation to accomplish this task despite the limited information leaked by such side channels. Our ability to do so underscores both the potential threat to user privacy which such side channels represent and the degree to which this threat has been underestimated. Finally, we propose and demonstrate the effectiveness of a new paradigm for identifying HTTP resources retrieved over encrypted connections. Our experiments demonstrate how the predominant paradigm from prior work fails to accurately represent real-world situations and how our proposed approach offers significant advantages, including the ability to infer partial information, in comparison. We believe these results represent both an enhanced threat to user privacy and an opportunity for network monitors and analysts to improve their own capabilities with respect to encrypted traffic.Doctor of Philosoph

Using Context to Improve Network-based Exploit Kit Detection

Today, our computers are routinely compromised while performing seemingly innocuous activities like reading articles on trusted websites (e.g., the NY Times). These compromises are perpetrated via complex interactions involving the advertising networks that monetize these sites. Web-based compromises such as exploit kits are similar to any other scam -- the attacker wants to lure an unsuspecting client into a trap to steal private information, or resources -- generating 10s of millions of dollars annually. Exploit kits are web-based services specifically designed to capitalize on vulnerabilities in unsuspecting client computers in order to install malware without a user's knowledge. Sadly, it only takes a single successful infection to ruin a user's financial life, or lead to corporate breaches that result in millions of dollars of expense and loss of customer trust. Exploit kits use a myriad of techniques to obfuscate each attack instance, making current network-based defenses such as signature-based network intrusion detection systems far less effective than in years past. Dynamic analysis or honeyclient analysis on these exploits plays a key role in identifying new attacks for signature generation, but provides no means of inspecting end-user traffic on the network to identify attacks in real time. As a result, defenses designed to stop such malfeasance often arrive too late or not at all resulting in high false positive and false negative (error) rates. In order to deal with these drawbacks, three new detection approaches are presented. To deal with the issue of a high number of errors, a new technique for detecting exploit kit interactions on a network is proposed. The technique capitalizes on the fact that an exploit kit leads its potential victim through a process of exploitation by forcing the browser to download multiple web resources from malicious servers. This process has an inherent structure that can be captured in HTTP traffic and used to significantly reduce error rates. The approach organizes HTTP traffic into tree-like data structures, and, using a scalable index of exploit kit traces as samples, models the detection process as a subtree similarity search problem. The technique is evaluated on 3,800 hours of web traffic on a large enterprise network, and results show that it reduces false positive rates by four orders of magnitude over current state-of-the-art approaches. While utilizing structure can vastly improve detection rates over current approaches, it does not go far enough in helping defenders detect new, previously unseen attacks. As a result, a new framework that applies dynamic honeyclient analysis directly on network traffic at scale is proposed. The framework captures and stores a configurable window of reassembled HTTP objects network wide, uses lightweight content rendering to establish the chain of requests leading up to a suspicious event, then serves the initial response content back to the honeyclient in an isolated network. The framework is evaluated on a diverse collection of exploit kits as they evolve over a 1 year period. The empirical evaluation suggests that the approach offers significant operational value, and a single honeyclient can support a campus deployment of thousands of users. While the above approaches attempt to detect exploit kits before they have a chance to infect the client, they cannot protect a client that has already been infected. The final technique detects signs of post infection behavior by intrusions that abuses the domain name system (DNS) to make contact with an attacker. Contemporary detection approaches utilize the structure of a domain name and require hundreds of DNS messages to detect such malware. As a result, these detection mechanisms cannot detect malware in a timely manner and are susceptible to high error rates. The final technique, based on sequential hypothesis testing, uses the DNS message patterns of a subset of DNS traffic to detect malware in as little as four DNS messages, and with orders of magnitude reduction in error rates. The results of this work can make a significant operational impact on network security analysis, and open several exciting future directions for network security research.Doctor of Philosoph

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype

Traffic Monitoring and analysis for source identification

Ph.DDOCTOR OF PHILOSOPH