Biblio
A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.
With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.
Port scans are a persistent problem on contemporary communication networks. Typically used as an attack reconnaissance tool, they can also create problems with application performance and throughput. This paper describes an architecture that deploys sequential neural networks (NNs) to classify packets, separate TCP datagrams, determine the type of TCP packet and detect port scans. Sequential networks allow this lengthy task to learn from the current environment and to be broken up into component parts. Following classification, analysis is performed in order to discover scan attempts. We show that neural networks can be used to successfully classify general packetized traffic at recognition rates above 99% and more complex TCP classes at rates that are also above 99%. We demonstrate that this specific communications task can successfully be broken up into smaller work loads. When tested against actual NMAP scan pcap files, this model successfully discovers open ports and the scan attempts with the same high percentage and low false positives.
In this work, we propose a novel approach for decentralized identifier distribution and synchronization in networks. The protocol generates network entity identifiers composed of timestamps and cryptographically secure random values with a significant reduction of collision probability. The distribution is inspired by Unique Universal Identifiers and Timestamp-based Concurrency Control algorithms originating from database applications. We defined fundamental requirements for the distribution, including: uniqueness, accuracy of distribution, optimal timing behavior, scalability, small impact on network load for different operation modes and overall compliance to common network security objectives. An implementation of the proposed approach is evaluated and the results are presented. Originally designed for a domain of proactive defense strategies known as Moving Target Defense, the general architecture of the protocol enables arbitrary applications where identifier distributions in networks have to be decentralized, rapid and secure.
Device management in large networks is of growing importance to network administrators and security analysts alike. The composition of devices on a network can help forecast future traffic demand as well as identify devices that may pose a security risk. However, the sheer number and diversity of devices that comprise most modern networks have vastly increased the management complexity. Motivated by a need for an encryption-invariant device management strategy, we use affiliation graphs to develop a methodology that reveals key insights into the devices acting on a network using only the source and destination IP addresses. Through an empirical analysis of the devices on a university campus network, we provide an example methodology to infer a device's characteristics (e.g., operating system) through the services it communicates with via the Internet.
An attacker's success crucially depends on the reconnaissance phase of Distributed Denial of Service (DDoS) attacks, which is the first step to gather intelligence. Although several solutions have been proposed against network reconnaissance attacks, they fail to address the needs of legitimate users' requests. Thus, we propose a cloud-based deception framework which aims to confuse the attacker with reconnaissance replies while allowing legitimate uses. The deception is based on for-warding the reconnaissance packets to a cloud infrastructure through tunneling and SDN so that the returned IP addresses to the attacker will not be genuine. For handling legitimate requests, we create a reflected virtual topology in the cloud to match any changes in the original physical network to the cloud topology using SDN. Through experimentations on GENI platform, we show that our framework can provide reconnaissance responses with negligible delays to the network clients while also reducing the management costs significantly.
The performance-driven design of SDN architectures leaves many security vulnerabilities, a notable one being the communication bottleneck between the controller and the switches. Functioning as a cache between the controller and the switches, the flow table mitigates this bottleneck by caching flow rules received from the controller at each switch, but is very limited in size due to the high cost and power consumption of the underlying storage medium. It thus presents an easy target for attacks. Observing that many existing defenses are based on simplistic attack models, we develop a model of intelligent attacks that exploit specific cache-like behaviors of the flow table to infer its internal configuration and state, and then design attack parameters accordingly. Our evaluations show that such attacks can accurately expose the internal parameters of the target flow table and cause measurable damage with the minimum effort.
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.
Through time inference attacks, adversaries fingerprint SDN controllers, estimate switches flow-table size, and perform flow state reconnaissance. In fact, timing a SDN and analyzing its results can expose information which later empowers SDN resource-consumption or saturation attacks. In the real world, however, launching such attacks is not easy. This is due to some challenges attackers may encounter while attacking an actual SDN deployment. These challenges, which are not addressed adequately in the related literature, are investigated in this paper. Accordingly, practical solutions to mitigate such attacks are also proposed. Discussed challenges are clarified by means of conducting extensive experiments on an actual cloud data center testbed. Moreover, mitigation schemes have been implemented and examined in details. Experimental results show that proposed countermeasures effectively block time inference attacks.
In this paper we investigate deceptive defense strategies for web servers. Web servers are widely exploited resources in the modern cyber threat landscape. Often these servers are exposed in the Internet and accessible for a broad range of valid as well as malicious users. Common security strategies like firewalls are not sufficient to protect web servers. Deception based Information Security enables a large set of counter measures to decrease the efficiency of intrusions. In this work we depict several techniques out of the reconnaissance process of an attacker. We match these with deceptive counter measures. All proposed measures are implemented in an experimental web server with deceptive counter measure abilities. We also conducted an experiment with honeytokens and evaluated delay strategies against automated scanner tools.
Military reconnaissance in 1999 has paved the way to establish its own, self-reliant and indigenous navigation system. The strategic necessity has been accomplished in 2013 by launching seven satellites in Geo-orbit and underlying Network control center in Bangalore and a new NavIC control center at Lucknow, later in 2016. ISTRAC is one of the premier and amenable center to track the Indian as well as external network satellite launch vehicle and provide house-keeping and inertial navigation (INC) data to launch control center in real time and to project team in off-line. Over the ISTRAC Launch network, Simple Network Management Protocol (SNMP) was disabled due to security and bandwidth reasons. The cons of SNMP comprise security risks that are normal trait whenever applied as an open standard. There is "security through obscurity" linked with any slight-used communications standard in SNMP. Detailed messages are being sent between devices, not just miniature pre-set codes. These cons in the SNMP are found in majority applications and more bandwidth seizure is another contention. Due to the above pros and cones in SNMP in form of open source, available network monitoring system (NMS) could not be employed for link monitoring and immediate decision making in ISTRAC network. The situation has made requisitions to evolve an in-house network monitoring system (NMS). It was evolved for real-time network monitoring as well as communication link performance explication. The evolved system has the feature of Internet control message protocol (ICMP) based link monitoring, 24/7 monitoring of all the nodes, GUI based real-time link status, Summary and individual link statistics on the GUI. It also identifies total downtime and generates summary reports. It does identification for out of order or looped packets, Email and SMS alert to Prime and Redundant system which one is down and repeat alert if the link is failed for more than 30 minutes. It has easy file based configuration and no application restart required. Generation of daily and monthly link status, offline link analysis plot of any day, less consumption of system resources are add-on features. It is fully secured in-house development, calculates total data flow over a network and co-relate data vs link percentage.
We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients over the Internet. We study a proactive moving-target defense to thwart a DDoS attacker's reconnaissance phase and consequently reduce the attack's impact. The defense is effectively a moving-target (motag) technique in which the proxies dynamically change. The system is evaluated using an AWS prototype of HTTP redirection and by numerical evaluations of an “adversarial” coupon-collector mathematical model, the latter allowing larger-scale extrapolations.