Biblio

Frame detection is an important part of the reconnaissance satellite receiver to identify the terrestrial application specific messages (ASM) / VHF data exchange (VDE) signal, and has been challenged by Doppler shift and message collision. A constant false alarm rate (CFAR) frame detection strategy insensitive to Doppler shift has been proposed in this paper. Based on the double Barker sequence, a periodical sequence has been constructed, and differential operations have been adopted to eliminate the Doppler shift. Moreover, amplitude normalization is helpful for suppressing the interference introduced by message collision. Simulations prove that the proposed CFAR frame detection strategy is very attractive for the reconnaissance satellite to identify the terrestrial ASM/VDE signal.

Internet technology has made surveillance widespread and access to resources at greater ease than ever before. This implied boon has countless advantages. It however makes protecting privacy more challenging for the greater masses, and for the few hacktivists, supplies anonymity. The ever-increasing frequency and scale of cyber-attacks has not only crippled private organizations but has also left Law Enforcement Agencies(LEA's) in a fix: as data depicts a surge in cases relating to cyber-bullying, ransomware attacks; and the force not having adequate manpower to tackle such cases on a more microscopic level. The need is for a tool, an automated assistant which will help the security officers cut down precious time needed in the very first phase of information gathering: reconnaissance. Confronting the surface web along with the deep and dark web is not only a tedious job but which requires documenting the digital footprint of the perpetrator and identifying any Indicators of Compromise(IOC's). TORSION which automates web reconnaissance using the Open Source Intelligence paradigm, extracts the metadata from popular indexed social sites and un-indexed dark web onion sites, provided it has some relating Intel on the target. TORSION's workflow allows account matching from various top indexed sites, generating a dossier on the target, and exporting the collected metadata to a PDF file which can later be referenced.

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

Exploring the efficient vulnerability scanning and detection technology of various tools is one fundamental aim of network security. This network security technique ameliorates the tremendous number of IoT security challenges and the threats they face daily. However, among various tools, Shodan Eye scanning technology has proven to be very helpful for network administrators and security personnel to scan, detect and analyze vulnerable ports and traffic in organizations' networks. This work presents a simulated network scanning activity and manual vulnerability analysis of an internet-connected industrial equipment of two chosen industrial networks (Industry A and B) by running Shodan on a virtually hosted (Oracle Virtual Box)-Linux-based operating system (Kali Linux). The result shows that the shodan eye is a a promising tool for network security and efficient vulnerability research.

The military operations in low communications infrastructure scenarios employ flexible solutions to optimize the data processing cycle using situational awareness systems, guaranteeing interoperability and assisting in all processes of decision-making. This paper presents an architecture for the integration of Command, Control, Computing, Communication, Intelligence, Surveillance and Reconnaissance Systems (C4ISR), developed within the scope of the Brazilian Ministry of Defense, in the context of operations with Unmanned Aerial Vehicles (UAV) - swarm drones - and the Internet-to-the-battlefield (IoBT) concept. This solution comprises the following intelligent subsystems embedded in UAV: STFANET, an SDN-Based Topology Management for Flying Ad Hoc Network focusing drone swarms operations, developed by University of Rio Grande do Sul; Interoperability of Command and Control (INTERC2), an intelligent communication middleware developed by Brazilian Navy; A Mission-Oriented Sensors Array (MOSA), which provides the automatization of data acquisition, data fusion, and data sharing, developed by Brazilian Army; The In-Flight Awareness Augmentation System (IFA2S), which was developed to increase the safety navigation of Unmanned Aerial Vehicles (UAV), developed by Brazilian Air Force; Data Mining Techniques to optimize the MOSA with data patterns; and an adaptive-collaborative system, composed of a Software Defined Radio (SDR), to solve the identification of electromagnetic signals and a Geographical Information System (GIS) to organize the information processed. This research proposes, as a main contribution in this conceptual phase, an application that describes the premises for increasing the capacity of sensing threats in the low structured zones, such as the Amazon rainforest, using existing communications solutions of Brazilian defense monitoring systems.

Conpot is a low-interaction SCADA honeypot system that mimics a Siemens S7-200 proprietary device on default deployments. Honeypots operating using standard configurations can be easily detected by adversaries using scanning tools such as Shodan. This study focuses on the capabilities of the Conpot honeypot, and how these competences can be used to lure attackers. In addition, the presented research establishes a framework that enables for the customized configuration, thereby enhancing its functionality to achieve a high degree of deceptiveness and realism when presented to the Shodan scanners. A comparison between the default and configured deployments is further conducted to prove the modified deployments' effectiveness. The resulting annotations can assist cybersecurity personnel to better acknowledge the effectiveness of the honeypot's artifacts and how they can be used deceptively. Lastly, it informs and educates cybersecurity audiences on how important it is to deploy honeypots with advanced deceptive configurations to bait cybercriminals.

Many organizations process and store classified data within their computer networks. Owing to the value of data that they hold; such organizations are more vulnerable to targets from adversaries. Accordingly, the sensitive organizations resort to an ‘air-gap’ approach on their networks, to ensure better protection. However, despite the physical and logical isolation, the attackers have successfully manifested their capabilities by compromising such networks; examples of Stuxnet and Agent.btz in view. Such attacks were possible due to the successful manipulation of human beings. It has been observed that to build up such attacks, persistent reconnaissance of the employees, and their data collection often forms the first step. With the rapid integration of social media into our daily lives, the prospects for data-seekers through that platform are higher. The inherent risks and vulnerabilities of social networking sites/apps have cultivated a rich environment for foreign adversaries to cherry-pick personal information and carry out successful profiling of employees assigned with sensitive appointments. With further targeted social engineering techniques against the identified employees and their families, attackers extract more and more relevant data to make an intelligent picture. Finally, all the information is fused to design their further sophisticated attacks against the air-gapped facility for data pilferage. In this regard, the success of the adversaries in harvesting the personal information of the victims largely depends upon the common errors committed by legitimate users while on duty, in transit, and after their retreat. Such errors would keep on repeating unless these are aligned with their underlying human behaviors and weaknesses, and the requisite mitigation framework is worked out.

Zero-day attack is a critical network attack. The zero-day attack period (ZDAP) is the period from the release of malware/exploit until a patch becomes available. IDS/IPS cannot effectively block zero-day attacks because they use pattern-based signatures in general. This paper proposes a Prophetic Defender (PD) by which ZDAP can be minimized. Prior to actual attack, hackers scan networks to identify hosts with vulnerable ports. If this port scanning can be detected early, zero-day attacks will become detectable. PD architecture makes use of a honeypot-based pseudo server deployed to detect malicious port scans. A port-scanning honeypot was operated by us in 6 years from 2009 to 2015. By analyzing the 6-year port-scanning log data, we understand that PD is effective for detecting and blocking zero-day attacks. The block rate of the proposed architecture is 98.5%.

Many critical industrial control systems integrate a mixture of state-of-the-art and legacy equipment. Legacy installations lack advanced, and often even basic security features, risking entire system security. Existing research primarily focuses on the development of secure protocols for emerging devices or protocol translation proxies for legacy equipment. However, a robust security framework not only needs encryption but also mechanisms to prevent reconnaissance and unauthorized access to industrial devices. This paper proposes a novel Edge Security Gateway (ESG) that provides both, communication and endpoint security. The ESG is based on double ratchet algorithm and encrypts every message with a different key. It manages the ongoing renewal of short-lived session keys and provides localized firewall protection to individual devices. The ESG is easily customizable for a wide range of industrial application. As a use case, this paper presents the design and validation for synchrophasor technology in smart grid. The ESG effectiveness is practically validated in detecting reconnaissance, manipulation, replay, and command injection attacks due to its perfect forward and backward secrecy properties.

For the intra-pulse modulation classification of radar signal, traditional deep learning algorithms have poor recognition performance without numerous training samples. Meanwhile, the receiver may intercept few pulse radar signals in the real scenes of electronic reconnaissance. To solve this problem, a structure which is made up of signal pretreatment by Smooth Pseudo Wigner-Ville (SPWVD) analysis algorithm, convolution neural network (CNN) and relation network (RN) is proposed in this study. The experimental results show that its classification accuracy is 94.24% under 20 samples per class training and the signal-to-noise ratio (SNR) is -4dB. Moreover, it can classify the novel types without further updating the network.

Modulated signal identification plays a crucial role in both military reconnaissance and civilian signal regulation. Traditionally, modulated signal identification is based on high-order statistics, but this approach has many drawbacks. With the development of deep learning, its advantages are fully exploited by combining it with modulated signals to avoid the complex process of computing a priori knowledge while having good fault tolerance. In this paper, ten digital modulated signals are classified and recognized, and improvements are made on the basis of convolutional neural networks, using feature reuse to increase the depth of the convolutional layer and extract signal features with better results. After experimental analysis, the recognition accuracy increases with the rise of the signal-to-noise ratio, and can reach 90% and above when the signal-to-noise ratio is 30dB.

The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our ``6 Agree'' technique.

In electronic warfare, communication may not counter reconnaissance and jamming without the help of network-station selection of frequency hopping. The competition in the field of electromagnetic spectrum is becoming more and more fierce with the increasingly complex electromagnetic environment of modern battlefield. The research on detection, identification, parameter estimation and network station selection of frequency hopping communication network has aroused the interest of scholars both at home and abroad, which has been summarized in this paper. Firstly, the working mode and characteristics of two kinds of FH communication networking modes synchronous orthogonal network and asynchronous non orthogonal network are introduced. Then, through the analysis of FH signals time hopping, frequency hopping, bandwidth, frequency, direction of arrival, bad time-frequency analysis, clustering analysis and machine learning method, the feature-based method is adopted Parameter selection technology is used to sort FH network stations. Finally, the key and difficult points of current research on FH communication network separation technology and the research status of blind source separation technology are introduced in details in this paper.

With the rapid development of mobile communication technology, the reconnaissance on terminal capability and identity information is not only an important guarantee to maintain the normal order of mobile communication, but also an essential means to ensure the electromagnetic space security. According to the characteristics of 5G mobile communication terminal's transporting capability and identity information, the smart jamming is first used to make the target terminal away from the 5G network, and then the jamming is turned off at once. Next the terminal will return to the 5G network. Through the time-frequency matching detection method, interactive signals of random access process and network registration between the terminal and the base station are quickly captured in this process, and the scheduling information in Physical Downlink Control Channel (PDCCH) and the capability and identity information in Physical Uplink Shared Channel (PUSCH) are demodulated and decoded under non-cooperative conditions. Finally, the experiment is carried out on the actual 5G communication terminal of China Telecom. The capability and identity information of this terminal are extracted successfully in the Stand Alone (SA) mode, which verifies the effectiveness and correctness of the method. This is a significant technical foundation for the subsequent development on the 5G terminal control equipment.

Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions.

Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR).

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%.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

Web application services have gradually become an important support of Internet services, but are also facing increasingly serious security problems. It is extremely necessary to evaluate the security of Web application services to deal with attacks against them effectively. In this paper, in view of the characteristics of the current attack of Web application services, a Web security analysis model based on the kill chain is established, and the possible attacks against Web application services are analyzed in depth from the perspective of the kill chain. Then, the security of Web application services is evaluated in a quantitative manner. In this way, it can make up the defects of insufficient inspection by the existing security vulnerability model and the security specification of the tracking of Web application services, so as to realize the objective and scientific evaluation of the security state of Web application services.

Manufacturing limitations, configuration and maintenance flaws associated with the Internet of Things (IoT) devices have resulted in an ever-expanding attack surface. Attackers exploit IoT devices to steal private information, take part in botnets, perform Denial of Service (DoS) attacks and use their resources for the mining of cryptocurrency. In this paper, we experimentally evaluate a hypothesis that attacks on IoT devices follow the generalised Cyber Kill Chain (CKC) model. We used a medium-interaction honeypot to capture and analyse more than 30,000 attacks targeting IoT devices. We classified the steps taken by the attackers using the CKC model and extended CKC to an IoT Kill Chain (IoTKC) model. The IoTKC provides details about IoT-specific attack characteristics and attackers' activities in the exploitation of IoT devices.

Multiple techniques for modeling cybersecurity attacks and defense have been developed. The use of tree- structures as well as techniques proposed by several firms (such as Lockheed Martin's Cyber Kill Chain, Microsoft's STRIDE and the MITRE ATT&CK frameworks) have all been demonstrated. These approaches model actions that can be taken to attack or stopped to secure infrastructure and other resources, at different levels of detail.This paper builds on prior work on using the Blackboard Architecture for cyberwarfare and proposes a generalized solution for modeling framework/paradigm-based attacks that go beyond the deployment of a single exploit against a single identified target. The Blackboard Architecture Cyber Command Entity attack Route (BACCER) identification system combines rules and facts that implement attack type determination and attack decision making logic with actions that implement reconnaissance techniques and attack and defense actions. BACCER's efficacy to model examples of tree-structures and other models is demonstrated herein.

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.

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.

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.