Cobalt Strike is a penetration testing software that supports stealthy command and control (C&C). As Cobalt Strike C&C always encrypts its payload and can mimic various normal traffic (e.g., HTTP, HTTPS), it is very difficult to distinguish and detect stealthy Cobalt Strike C&C traffic from normal (encrypted) traffic. As a result, many recent cyber attacks (e.g., 2020 SolarWind data breach) used Cobalt Strike C&C to surreptitiously control compromised systems and exfiltrate sensitive data without being detected. To protect our mission critical systems from sophisticated cyber attacks, it is critically important to develop capabilities to effectively detect real world Cobalt Strike C&C activities from encrypted traffic. In this paper, we investigate how machine learning models can help detect stealthy Cobalt Strike C&C activities from encrypted traffic. Based on analysis of real world Cobalt Strike C&C traffic, we have developed novel machine learning features across multiple packet flows that capture some inherent characteristics of encrypted Cobalt Strike C&C traffic. We have evaluated several machine learning algorithms with our multi-flow based machine learning features and real world Cobalt Strike C&C traffic captured from real world cyber attacks Our experimental results demonstrate that our neural network based detection is able to detect 90.9% real world Cobalt Strike C&C activities with 0.4% false positive rate.

In the past few years, massive data breach attacks on large organizations (e.g., Anthem Inc., Equifax) have compromised sensitive data of tens or even hundreds of millions of people. The 2017 Equifax data breach attack has compromised sensitive data of 148 million people and has costed Equifax $\$ 1.4$ billion as of May 2019. Unfortunately the average time to detect, contain a data breach was 206 days and 73 days respectively in 2019. There is a pressing need to develop practical and deployable capability to detect and block previously unseen, application specific cyberattacks on vulnerable binary applications in real-time. In this paper, we present AppImmu, a practical cyber defense system that can detect and block previously unknown cyber-attacks on vulnerable binary applications in real-time with no false positive. Given a potentially vulnerable ELF binary application, AppImmu can transparently and statically immunize it into an immunized version via binary rewriting. At run-time, AppImmu uses kernel level immunization based anomaly detection techniques to detect and block previously unknown cyberattacks on immunized binary applications without any prior knowledge of the attacks. We have successfully immunized real world large binary applications such as Apache Java execution environment, bash shell, Snort in Linux and have successfully detected and blocked real world data breach attacks (e.g., Apache Strut exploit used in 2017 Equifax data breach attack, Shellshock exploit) in true real-time. Our benchmark experiments show that AppImmu incurs less than 6% run-time overhead in overall system performance, 2.1% run-time overhead for applications under typical workload.

The fast-spreading worm, which immediately propagates itself after a successful infection, is becoming one of the most serious threats to today’s networked information systems. In this article, we present WormTerminator, a host-based solution for fast Internet worm detection and containment with the assistance of virtual machine techniques based on the fast-worm defining characteristic. In WormTerminator, a virtual machine cloning the host OS runs in parallel to the host OS. Thus, the virtual machine has the same set of vulnerabilities as the host. Any outgoing traffic from the host is diverted through the virtual machine. If the outgoing traffic from the host is for fast worm propagation, the virtual machine should be infected and will exhibit worm propagation pattern very quickly because a fast-spreading worm will start to propagate as soon as it successfully infects a host. To prove the concept, we have implemented a prototype of WormTerminator and have examined its effectiveness against the real Internet worm Linux/Slapper. Our empirical results confirm that WormTerminator is able to completely contain worm propagation in real-time without blocking any non-worm traffic. The major performance cost of WormTerminator is a one-time delay to the start of each outgoing normal connection for worm detection. To reduce the performance overhead, caching is utilized, through which WormTerminator will delay no more than 6% normal outgoing traffic for such detection on average.

Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.

In this paper, we present a lightweight defense mechanism against VoIP attacks, called VoIP Shield, that can detect counterfeit or MITM modified messages as well as replay attacks without ever changing the underlying VoIP protocol. We then empirically tested our design in conjunction with deployed Vonage VoIP services against a large body of attacks including billing, call redirection, voice pharming, and complex MITM exploits. The results demonstrate that advanced VoIP protection is practical, lightweight, and can be deployed even in systems where software upgrades are impossible.

The threat of voice spam, commonly known as Spam over Internet Telephony (SPIT) is a real and contemporary problem. We present two approaches based on the anomaly detection of the distributions of selected call features (i.e., day and time of calling, call durations etc.) to detect and prevent SPITting over the Internet. The first approach uses Mahalanobis Distance as a summarization tool and it is able to reliably detect individual spam VoIP calls at a microscopic level. The second approach is designed to detect groups of (potentially collaborating) VoIP spam calls at a macroscopic level. By computing entropy of call durations of groups of calls, we are able to build profile of normal calls and reliably detect the deviation from normal human call behavior that are caused by bulk spam calls.

Network-based intruders seldom attack their victims directly from their own computer. Often, they stage their attacks through intermediate “stepping stones” in order to conceal their identity and origin. To identify the source of the attack behind the stepping stone(s), it is necessary to correlate the incoming and outgoing flows or connections of a stepping stone. To resist attempts at correlation, the attacker may encrypt or otherwise manipulate the connection traffic. Timing-based correlation approaches have been shown to be quite effective in correlating encrypted connections. However, timing-based correlation approaches are subject to timing perturbations that may be deliberately introduced by the attacker at stepping stones. In this paper, we propose a novel watermark-based-correlation scheme that is designed specifically to be robust against timing perturbations. Unlike most previous timing-based correlation approaches, our watermark-based approach is “active” in that it embeds a unique watermark into the encrypted flows by slightly adjusting the timing of selected packets. The unique watermark that is embedded in the encrypted flow gives us a number of advantages over passive timing-based correlation in resisting timing perturbations by the attacker. In contrast to the existing passive correlation approaches, our active watermark-based correlation does not make any limiting assumptions about the distribution or random process of the original interpacket timing of the packet flow. In theory, our watermark-based correlation can achieve arbitrarily close to 100 percent correlation true positive rate (TPR), and arbitrarily close to 0 percent false positive rate (FPR) at the same time for sufficiently long flows, despite arbitrarily large (but bounded) timing perturbations of any distribution by the attacker. Our paper is the first that identifies 1) accurate quantitative tradeoffs between the achievable correlation effectiveness and the defining characteristics of the timing perturbation; and 2) a provable upper bound on the number of packets needed to achieve a desired correlation effectiveness, given the amount of timing perturbation. Experimental results show that our active watermark-based correlation performs better and requires fewer packets than existing, passive timing-based correlation methods in the presence of random timing perturbations.

With abundant aggregate network bandwidth, continuous data streams are commonly used in scientific and commercial applications. Correspondingly, there is an increasing demand of authenticating these data streams. Existing strategies explore data stream authentication by using message authentication codes (MACs) on a certain number of data packets (a data block) to generate a message digest, then either embedding the digest into the original data, or sending the digest out-of-band to the receiver. Embedding approaches inevitably change the original data, which is not acceptable under some circumstances (e.g., when sensitive information is included in the data). Sending the digest out-of-band incurs additional communication overhead, which consumes more critical resources (e.g., power in wireless devices for receiving information) besides network bandwidth. In this paper, we propose a novel strategy, DaTA, which effectively authenticates data streams by selectively adjusting some interpacket delay. This authentication scheme requires no change to the original data and no additional communication overhead. Modeling-based analysis and experiments conducted on an implemented prototype system in an LAN and over the Internet show that our proposed scheme is efficient and practical.

An alarming trend in recent malware incidents is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based antimalware systems is that they run inside the very hosts they are protecting (“in-the-box”), making them vulnerable to counter detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM (“out-of-the-box”). However, they gain tamper resistance at the cost of losing the internal semantic view of the host, which is enjoyed by “in-the-box” approaches. This poses a technical challenge known as the semantic gap. In this article, we present the design, implementation, and evaluation of VMwatcher —an “out-of-the-box” approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM nonintrusively from the outside. More specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. Furthermore, we extend guest view casting to reconstruct details of system call events (e.g., the process that makes the system call as well as the system call number, parameters, and return value) in the VM, enriching the semantic view. With the semantic gap effectively narrowed, we identify three unique malware detection and monitoring capabilities: (i) view comparison-based malware detection and its demonstration in rootkit detection; (ii) “out-of-the-box” deployment of off-the-shelf anti malware software with improved detection accuracy and tamper-resistance; and (iii) nonintrusive system call monitoring for malware and intrusion behavior observation. We have implemented a proof-of-concept VMwatcher prototype on a number of VMM platforms. Our evaluation experiments with real-world malware, including elusive kernel-level rootkits, demonstrate VMwatcher's practicality and effectiveness.

The man-in-the-middle (MITM) attack has been shown to be one of the most serious threats to the security and trust of existing VoIP protocols and systems. For example, the MITM who is in the VoIP signaling and/or media path can easily wiretap, divert and even hijack selected VoIP calls by tempering with the VoIP signaling and/or media traffic. Since all previously identified MITM attacks on VoIP require the adversary initially in the VoIP signaling and/or media path, there is a common belief that it is infeasible for a remote attacker, who is not initially in the VoIP path, to launch any MITM attack on VoIP. This makes people think that securing all the nodes along the normal path of VoIP traffic is sufficient to prevent MITM attacks on VoIP.

While network-wide anomaly analysis has been well studied, the on-line detection of network traffic anomalies at a vantage point inside the Internet still poses quite a challenge to network administrators. In this paper, we develop a behavioral distance based anomaly detection mechanism with the capability of performing on-line traffic analysis. To construct accurate online traffic profiles, we introduce horizontal and vertical distance metrics between various traffic features (i.e., packet header fields) in the traffic data streams. The significant advantages of the proposed approach lie in four aspects: (1) it is efficient and simple enough to process on-line traffic data; (2) it facilitates protocol behavioral analysis without maintaining per-flow state; (3) it is scalable to high speed traffic links because of the aggregation, and (4) using various combinations of packet features and measuring distances between them, it is capable for accurate on-line anomaly detection. We validate the efficacy of our proposed detection system by using network traffic traces collected at Abilene and MAWI high-speed links.

Voice communication is fundamental to the normal operation of our society. The general public have put a lot of trust in voice communication and they have been relying on it for many critical and sensitive information exchange (e.g., emergency 911 calls, calls to customer service of financial institutions). Now more and more voice calls are carried, at least partially, over the public Internet rather than traditional Public Switched Telephone Network (PSTN). The security ramifications of using VoIP, however, have not been fully recognized. It is not clear how secure and trustworthy the currently deployed VoIP systems are, and there exists a substantial gap in the understanding of the potential impact of VoIP exploits on the VoIP users. In this paper, we seek to fill this gap by investigating the trust issues of currently deployed VoIP systems and their implications to the VoIP users.

An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting ("in the box"), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out of the box"). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the "in the box" approach, thus leading to a technical challenge known as the semantic gap.

Tracing interactive traffic that traverses stepping stones (i.e., intermediate hosts) is challenging, as the packet headers, lengths, and contents can all be changed by the stepping stones. The traffic timing has therefore been studied as a means of tracing traffic. One such technique uses traffic timing as a side channel into which a watermark, or identifying tag, can be embedded to aid with tracing. The effectiveness of such techniques is greatly reduced when repacketization of the traffic occurs at the stepping stones. Repacketization is a natural effect of many applications, including SSH, and therefore poses a serious challenge for traffic tracing. This paper presents a new method of embedding a watermark in traffic timing, for purposes of tracing the traffic in the presence of repacketization. This method uses an invariant characteristic of two traffic flows which are part of the same stepping stone chain, namely, elapsed time of the flows. The duration of each flow is sliced into short fixed-length intervals. Packet timing is adjusted to manipulate the packet count in specific intervals, for purposes of embedding the watermark. A statistical analysis of the method, with no assumptions or limitations concerning the distribution of packet times, proves the effectiveness of the method given a sufficient number of packets, despite natural and/or deliberate repacketization and perturbation of the traffic timing by an adversary. The method has been implemented and tested on a large number of synthetically-generated SSH traffic flows. The results demonstrate that 100% detection rates and less than 1% false positive rates are achievable under conditions of 2 seconds of maximum timing perturbation and 12% repacketization rate, using fewer than 1000 packets.

Many proposed low-latency anonymous communication systems have used various flow transformations such as traffic padding, adding cover traffic (or bogus packets), packet dropping, flow mixing, flow splitting, and flow merging to achieve anonymity. It has long been believed that these flow transformations would effectively disguise net-workflows, thus achieve good anonymity. In this paper, we investigate the fundamental limitations of flow transformations in achieving anonymity, and we show that flow transformations do not necessarily provide the level of anonymity people have expected or believed. By injecting unique watermark into the inter-packet timing domain of a packet flow, we are able to make any sufficiently long flow uniquely identifiable even if I) it is disguised by substantial amount of cover traffic, 2) it is mixed or merged with a number of other flows, 3) it is split into a number subflows, 4) there is a substantial portion of packets dropped, and 5) it is perturbed in timing due to either natural network delay jitter or deliberate timing perturbation. In addition to demonstrating the theoretical limitations of low-latency anonymous communications systems, we develop the first practical attack on the leading commercial low-latency anonymous communication system. Our real-time experiments show that our flow watermarking attack only needs about 10 minutes active Web browsing traffic to "penetrate" the total net shield service provided by www.anonymizer.com. Our analytical and empirical results demonstrate that achieving anonymity in low-latency communication systems is much harder than we have realized, and current flow transformation based low-latency anonymous communication systems need to be revisited.

The fast spreading worm is becoming one of the most serious threats to today's networked information systems. A fast spreading worm could infect hundreds of thousands of hosts within a few minutes. In order to stop a fast spreading worm, we need the capability to detect and contain worms automatically in real-time. While signature based worm detection and containment are effective in detecting and containing known worms, they are inherently ineffective against previously unknown worms and polymorphic worms. Existing traffic anomaly pattern based approaches have the potential to detect and/or contain previously unknown and polymorphic worms, but they either impose too much constraint on normal traffic or allow too much infectious worm traffic to go out to the Internet before an unknown or polymorphic worm can be detected.In this paper, we present WormTerminator, which can detect and completely contain, at least in theory, almost all fast spreading worms in real-time while blocking virtually no normal traffic. WormTerminator detects and contains the fast spreading worm based on its defining characteristic -- a fast spreading worm will start to infect others as soon as it successfully infects one host. WormTerminator also exploits the observation that a fast spreading worm keeps exploiting the same set of vulnerabilities when infecting new machines. To prove the concept, we have implemented a prototype of WormTerminator and have examined its effectiveness against the real Internet worm Linux/Slapper.

Peer-to-peer VoIP calls are becoming increasingly popular due to their advantages in cost and convenience. When these calls are encrypted from end to end and anonymized by low latency anonymizing network, they are considered by many people to be both secure and anonymous.In this paper, we present a watermark technique that could be used for effectively identifying and correlating encrypted, peer-to-peer VoIP calls even if they are anonymized by low latency anonymizing networks. This result is in contrast to many people's perception. The key idea is to embed a unique watermark into the encrypted VoIP flow by slightly adjusting the timing of selected packets. Our analysis shows that it only takes several milliseconds time adjustment to make normal VoIP flows highly unique and the embedded watermark could be preserved across the low latency anonymizing network if appropriate redundancy is applied. Our analytical results are backed up by the real-time experiments performed on leading peer-to-peer VoIP client and on a commercially deployed anonymizing network. Our results demonstrate that (1) tracking anonymous peer-to-peer VoIP calls on the Internet is feasible and (2) low latency anonymizing networks are susceptible to timing attacks.

Network based intruders seldom attack directly from their own hosts, but rather stage their attacks through intermediate "stepping stones" to conceal their identity and origin. To identify attackers behind stepping stones, it is necessary to be able to correlate connections through stepping stones, even if those connections are encrypted or perturbed by the intruder to prevent traceability.The timing-based approach is the most capable and promising current method for correlating encrypted connections. However, previous timing-based approaches are vulnerable to packet timing perturbations introduced by the attacker at stepping stones. In this paper, we propose a novel watermark-based correlation scheme that is designed specifically to be robust against timing perturbations. The watermark is introduced by slightly adjusting the timing of selected packets of the flow. By utilizing redundancy techniques, we have developed a robust watermark correlation framework that reveals a rather surprising result on the inherent limits of independent and identically distributed (iid) random timing perturbations over sufficiently long flows. We also identify the tradeoffs between timing perturbation characteristics and achievable correlation effectiveness. Experiments show that the new method performs significantly better than existing, passive, timing-based correlation in the presence of random packet timing perturbations.

DECIDUOUS is a security management framework for identifying the sources of network-based intrusions. The first key concept in DECIDUOUS is dynamic security associations, which efficiently and collectively provide location information for attack sources. DECIDUOUS is built on top of the IETF's IPSEC/ISAKMP infrastructure, and it does not introduce any new network protocol for source identification in a single administrative domain. It defines a collaborative protocol for inter-domain attack source identification. The second key concept in DECIDUOUS is the management information integration of the intrusion detection system (IDS) and attack source identification system (ASIS) across different protocol layers. For example, in DECIDUOUS, it is possible for a network-layer security control protocol (e.g., IPSEC) to collaborate with an application-layer intrusion detection system module (e.g., IDS for the SNMP engine). In this paper, we present the motivations, design, and prototype implementation of the DECIDUOUS framework.