Biblio

Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.

Thanks to advances in network architecture with Software-Defined Networking (SDN) paradigm, there are various approaches for eliminating attack surface in the largescale networks relied on the essence of the SDN principle. They are ranging from intrusion detection to moving target defense, and cyber deception that leverages the network programmability. Therein, cyber deception is considered as a proactive defense strategy for the usual network operation since it makes attackers spend more time and effort to successfully compromise network systems. In this paper, we concentrate on reconnaissance attacks in SDN-enabled networks to collect the sensitive information for hackers to conduct further attacks. In more details, we introduce SDNRecon tool to perform reconnaissance attacks, which can be useful in evaluating cyber deception techniques deployed in SDN-aware networks.

Advanced persistent threat (APT) attackers usually conduct a large number of network reconnaissance before a formal attack to discover exploitable vulnerabilities in the target network and system. The static configuration in traditional network systems provides a great advantage for adversaries to find network targets and launch attacks. To reduce the effectiveness of adversaries' continuous reconnaissance attacks, this paper develops a moving target defense (MTD) enhanced cyber deception defense system based on software-defined networks (SDN). The system uses virtual network topology to confuse the target network and system information collected by adversaries. Also Besides, it uses IP address randomization to increase the dynamics of network deception to enhance its defense effectiveness. Finally, we implemented the system prototype and evaluated it. In a configuration where the virtual network topology scale is three network segments, and the address conversion cycle is 30 seconds, this system delayed the adversaries' discovery of vulnerable hosts by an average of seven times, reducing the probability of adversaries successfully attacking vulnerable hosts by 83%. At the same time, the increased system overhead is basically within 10%.

Due to the constrained resource and computational limitation of many Internet of Things (IoT) devices, conventional security protections, which require high computational overhead are not suitable to be deployed. Thus, vulnerable IoT devices could be easily exploited by attackers to break into networks. In this paper, we employ cyber deception and moving target defense (MTD) techniques to proactively change the network topology with both real and decoy nodes with the support of software-defined networking (SDN) technology and investigate the impact of single-target and multi-target attacks on the effectiveness of the integrated mechanism via a hierarchical graphical security model with security metrics. We also implement a web-based visualization interface to show topology changes with highlighted attack paths. Finally, the qualitative security analysis is performed for a small-scale and SDN-supported IoT network with different combinations of decoy types and levels of attack intelligence. Simulation results show the integrated defense mechanism can introduce longer mean-time-to-security-failure and larger attack impact under the multi-target attack, compared with the single-target attack model. In addition, adaptive shuffling has better performance than fixed interval shuffling in terms of a higher proportion of decoy paths, longer mean-time-to-security-failure and largely reduced defense cost.

In recent years, persistent cyber adversaries have developed increasingly sophisticated techniques to evade detection. Once adversaries have established a foothold within the target network, using seemingly-limited passive reconnaissance techniques, they can develop significant network reconnaissance capabilities. Cyber deception has been recognized as a critical capability to defend against such adversaries, but, without an accurate model of the adversary's reconnaissance behavior, current approaches are ineffective against advanced adversaries. To address this gap, we propose a novel model to capture how advanced, stealthy adversaries acquire knowledge about the target network and establish and expand their foothold within the system. This model quantifies the cost and reward, from the adversary's perspective, of compromising and maintaining control over target nodes. We evaluate our model through simulations in the CyberVAN testbed, and indicate how it can guide the development and deployment of future defensive capabilities, including high-interaction honeypots, so as to influence the behavior of adversaries and steer them away from critical resources.

Internet of Battlefield Things (IoBT) devices such as actuators, sensors, wearable devises, robots, drones, and autonomous vehicles, facilitate the Intelligence, Surveillance and Reconnaissance (ISR) to Command and Control and battlefield services. IoBT devices have the ability to collect operational field data, to compute on the data, and to upload its information to the network. Securing the IoBT presents additional challenges compared with traditional information technology (IT) systems. First, IoBT devices are mass produced rapidly to be low-cost commodity items without security protection in their original design. Second, IoBT devices are highly dynamic, mobile, and heterogeneous without common standards. Third, it is imperative to understand the natural world, the physical process(es) under IoBT control, and how these real-world processes can be compromised before recommending any relevant security counter measure. Moreover, unprotected IoBT devices can be used as “stepping stones” by attackers to launch more sophisticated attacks such as advanced persistent threats (APTs). As a result of these challenges, IoBT systems are the frequent targets of sophisticated cyber attack that aim to disrupt mission effectiveness.

As evidenced by numerous high-profile security incidents such as the Target data breach and the Equifax hack, APTs (Advanced Persistent Threats) can significantly compromise the trustworthiness of cyber space. This work explores how to improve the effectiveness of cyber deception in hardening FTP (File Transfer Protocol) services against APTs. The main objective of our work is to ensure deception consistency: when the attackers are trapped, they can only make observations that are consistent with what they have seen already so that they cannot recognize the deceptive environment. To achieve deception consistency, we use logic constraints to characterize an attacker's best knowledge (either positive, negative, or uncertain). When migrating the attacker's FTP connection into a contained environment, we use these logic constraints to instantiate a new FTP file system that is guaranteed free of inconsistency. We performed deception experiments with student participants who just completed a computer security course. Following the design of Turing tests, we find that the participants' chances of recognizing deceptive environments are close to random guesses. Our experiments also confirm the importance of observation consistency in identifying deception.

A filesystem capable of curtailing data theft and ensuring file integrity protection through deception is introduced and evaluated. The deceptive filesystem transparently creates multiple levels of stacking to protect the base filesystem and monitor file accesses, hide and redact sensitive files with baits, and inject decoys onto fake system views purveyed to untrusted subjects, all while maintaining a pristine state to legitimate processes. Our prototype implementation leverages a kernel hot-patch to seamlessly integrate the new filesystem module into live and existing environments. We demonstrate the utility of our approach with a use case on the nefarious Erebus ransomware. We also show that the filesystem adds no I/O overhead for legitimate users.

Active defense is a popular defense technique based on systems that hinder an attacker's progress by design, rather than reactively responding to an attack only after its detection. Well-known active defense systems are honeypots. Honeypots are fake systems, designed to look like real production systems, aimed at trapping an attacker, and analyzing his attack strategy and goals. These types of systems suffer from a major weakness: it is extremely hard to design them in such a way that an attacker cannot distinguish them from a real production system. In this paper, we advocate that, instead of adding additional fake systems in the corporate network, the production systems themselves should be instrumented to provide active defense capabilities. This perspective to active defense allows containing costs and complexity, while at the same time provides the attacker with a more realistic-looking target, and gives the Incident Response Team more time to identify the attacker. The proposed proof-of-concept prototype system can be used to implement active defense in any corporate production network, with little upfront work, and little maintenance.

Modern military forces are enabled by networked command and control systems, which provide an important interface between the cyber environment, electronic sensors and decision makers. However these systems are vulnerable to cyber attack. A successful cyber attack could compromise data within the system, leading to incorrect information being utilized for decisions with potentially catastrophic results on the battlefield. Degrading the utility of a system or the trust a decision maker has in their virtual display may not be the most effective means of employing offensive cyber effects. The coordination of cyber and kinetic effects is proposed as the optimal strategy for neutralizing an adversary's C4ISR advantage. However, such an approach is an opportunity cost and resource intensive. The adversary's cyber dependence can be leveraged as a means of gaining tactical and operational advantage in combat, if a military force is sufficiently trained and prepared to attack the entire information network. This paper proposes a research approach intended to broaden the understanding of the relationship between command and control systems and the human decision maker, as an interface for both cyber and kinetic deception activity.