Biblio

This paper provides a Common Vulnerability Scoring System (CVSS) metric-based technique for classifying and analysing the prevailing Computer Network Security Vulnerabilities and Threats (CNSVT). The problem that is addressed in this paper, is that, at the time of writing this paper, there existed no effective approaches for analysing and classifying CNSVT for purposes of assessments based on CVSS metrics. The authors of this paper have achieved this by generating a CVSS metric-based dynamic Vulnerability Analysis Classification Countermeasure (VACC) criterion that is able to rank vulnerabilities. The CVSS metric-based VACC has allowed the computation of vulnerability Similarity Measure (VSM) using the Hamming and Euclidean distance metric functions. Nevertheless, the CVSS-metric based on VACC also enabled the random measuring of the VSM for a selected number of vulnerabilities based on the [Ma-Ma], [Ma-Mi], [Mi-Ci], [Ma-Ci] ranking score. This is a technique that is aimed at allowing security experts to be able to conduct proper vulnerability detection and assessments across computer-based networks based on the perceived occurrence by checking the probability that given threats will occur or not. The authors have also proposed high-level countermeasures of the vulnerabilities that have been listed. The authors have evaluated the CVSS-metric based VACC and the results are promising. Based on this technique, it is worth noting that these propositions can help in the development of stronger computer and network security tools.

Advent of Cyber has converted the entire World into a Global village. But, due to vurneabilites in SCADA architecture [1] national assests are more prone to cyber attacks.. Cyber invasions have a catastrophic effect in the minds of the civilian population, in terms of states security system. A robust cyber security is need of the hour to protect the critical information infastructrue & critical infrastructure of a country. Here, in this paper we scrutinize cyber terrorism, vurneabilites in SCADA network systems [1], [2] and concept of cyber resilience to combat cyber attacks.

Malware authors attempt to obfuscate and hide their code in its static and dynamic states. This paper provides a novel approach to aid analysis by intercepting and capturing malware artifacts and providing dynamic control of process flow. Capturing malware artifacts allows an analyst to more quickly and comprehensively understand malware behavior and obfuscation techniques and doing so interactively allows multiple code paths to be explored. The faster that malware can be analyzed the quicker the systems and data compromised by it can be determined and its infection stopped. This research proposes an instantiation of an interactive malware analysis and artifact capture tool.

Security has always been a major issue in cloud. Data sources are the most valuable and vulnerable information which is aimed by attackers to steal. If data is lost, then the privacy and security of every cloud user are compromised. Even though a cloud network is secured externally, the threat of an internal attacker exists. Internal attackers compromise a vulnerable user node and get access to a system. They are connected to the cloud network internally and launch attacks pretending to be trusted users. Machine learning approaches are widely used for cloud security issues. The existing machine learning based security approaches classify a node as a misbehaving node based on short-term behavioral data. These systems do not differentiate whether a misbehaving node is a malicious node or a broken node. To address this problem, this paper proposes an Improvised Long Short-Term Memory (ILSTM) model which learns the behavior of a user and automatically trains itself and stores the behavioral data. The model can easily classify the user behavior as normal or abnormal. The proposed ILSTM not only identifies an anomaly node but also finds whether a misbehaving node is a broken node or a new user node or a compromised node using the calculated trust factor. The proposed model not only detects the attack accurately but also reduces the false alarm in the cloud network.

A Mobile ad hoc Network (MANET) is a self-configure, dynamic, and non-fixed infrastructure that consists of many nodes. These nodes communicate with each other without an administrative point. However, due to its nature MANET becomes prone to many attacks such as DoS attacks. DoS attack is a severe as it prevents legitimate users from accessing to their authorised services. Monitoring, Detection, and rehabilitation (MrDR) method is proposed to detect DoS attacks. MrDR method is based on calculating different trust values as nodes can be trusted or not. In this paper, we evaluate the MrDR method which detect DoS attacks in MANET and compare it with existing method Trust Enhanced Anonymous on-demand routing Protocol (TEAP) which is also based on trust concept. We consider two factors to compare the performance of the proposed method to TEAP method: packet delivery ratio and network overhead. The results confirm that the MrDR method performs better in network performance compared to TEAP method.

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.

Traditional security practices focus on negative incentives that attempt to force compliance through constraints, monitoring, and punishment. This paper describes a missing dimension of most organizations' insider threat defense-one that explicitly considers positive incentives for attracting individuals to act in the interests of the organization. Positive incentives focus on properties of the organizational context of workforce management practices - including those relating to organizational supportiveness, coworker connectedness, and job engagement. Without due attention to the organizational context in which insider threats occur, insider misbehaviors may simply reoccur as a natural response to counterproductive or dysfunctional management practices. A balanced combination of positive and negative incentives can improve employees' relationships with the organization and provide a means for employees to better cope with personal and professional stressors. An insider threat program that balances organizational incentives can become an advocate for the workforce and a means for improving employee work life - a welcome message to employees who feel threatened by programs focused on discovering insider wrongdoing.

Monitoring systems are essential to understand and control the behaviour of systems and networks. Cyber-physical systems (CPS) are particularly delicate under that perspective since they involve real-time constraints and physical phenomena that are not usually considered in common IT solutions. Therefore, there is a need for publicly available monitoring tools able to contemplate these aspects. In this poster/demo, we present our initiative, called CPS-MT, towards a versatile, real-time CPS monitoring tool, with a particular focus on security research. We first present its architecture and main components, followed by a MiniCPS-based case study. We also describe a performance analysis and preliminary results. During the demo, we will discuss CPS-MT's capabilities and limitations for security applications.

Supervisory Control and Data Acquisition (SCADA) systems play a critical role in the operation of large-scale distributed industrial systems. There are many vulnerabilities in SCADA systems and inadvertent events or malicious attacks from outside as well as inside could lead to catastrophic consequences. Network-based intrusion detection is a preferred approach to provide security analysis for SCADA systems due to its less intrusive nature. Data in SCADA network traffic can be generally divided into transport, operation, and content levels. Most existing solutions only focus on monitoring and event detection of one or two levels of data, which is not enough to detect and reason about attacks in all three levels. In this paper, we develop a novel edge-based multi-level anomaly detection framework for SCADA networks named EDMAND. EDMAND monitors all three levels of network traffic data and applies appropriate anomaly detection methods based on the distinct characteristics of data. Alerts are generated, aggregated, prioritized before sent back to control centers. A prototype of the framework is built to evaluate the detection ability and time overhead of it.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Cisco Adaptive Security Appliance (ASA) 5500 Series Firewall is amongst the most popular and technically advanced for securing organisational networks and systems. One of its most valuable features is its threat detection function which is available on every version of the firewall running a software version of 8.0(2) or higher. Threat detection operates at layers 3 and 4 to determine a baseline for network traffic, analysing packet drop statistics and generating threat reports based on traffic patterns. Despite producing a large volume of statistical information relating to several security events, further effort is required to mine and visually report more significant information and conclude the security status of the network. There are several commercial off-the-shelf tools available to undertake this task, however, they are expensive and may require a cloud subscription. Furthermore, if the information transmitted over the network is sensitive or requires confidentiality, the involvement of a third party or a third-party tool may place organisational security at risk. Therefore, this paper presents a fuzzy logic aided intelligent threat detection solution, which is a cost-free, intuitive and comprehensible solution, enhancing and simplifying the threat detection process for all. In particular, it employs a fuzzy reasoning system based on the threat detection statistics, and presents results/threats through a developed dashboard user interface, for ease of understanding for administrators and users. The paper further demonstrates the successful utilisation of a fuzzy reasoning system for selected and prioritised security events in basic threat detection, although it can be extended to encompass more complex situations, such as complete basic threat detection, advanced threat detection, scanning threat detection, and customised feature based threat detection.

Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.

The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.

Implementation of self-healing control system in smart grid is a persisting challenge. Self-Healing control strategy is the important guarantee to implement the smart grid. In addition, it is the support of achieving the secure operation, improving the reliability and security of distribution grid, and realizing the smart distribution grid. Although self-healing control system concept is presented in smart grid context, but the complexity of distribution network structure recommended to choose advanced control and protection system using a self-healing, this system must be able to heal any disturbance in the distribution system of smart grid to improve efficiency, resiliency, continuity, and reliability of the smart grid. This review focuses mostly on the key technology of self-healing control, gives an insight into the role of self-healing in distribution system advantages, study challenges and opportunities in the prospect of utilities. The main contribution of this paper is demonstrating proposed architecture, control strategy for self-healing control system includes fault detection, fault localization, faulted area isolation, and power restoration in the electrical distribution system.

To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.

The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation.

Large scale services have automated hardware remediation to maintain the infrastructure availability at a healthy level. In this paper, we share the current remediation flow at Facebook, and how it is being monitored. We discuss a class of hardware issues that are transient and typically have higher rates during heavy load. We describe how our remediation system was enhanced to be efficient in detecting this class of issues. As hardware and systems change in response to the advancement in technology and scale, we have also utilized machine learning frameworks for hardware remediation to handle the introduction of new hardware failure modes. We present an ML methodology that uses a set of predictive thresholds to monitor remediation efficiency over time. We also deploy a recommendation system based on natural language processing, which is used to recommend repair actions for efficient diagnosis and repair. We also describe current areas of research that will enable us to improve hardware availability further.

Power systems domain has generally been very conservative in terms of conducting digital forensic investigations, especially so since the advent of smart grids. This lack of research due to a multitude of challenges has resulted in absence of knowledge base and resources to facilitate such an investigation. Digitalization in the form of smart grids is upon us but in case of cyber-attacks, attribution to such attacks is challenging and difficult if not impossible. In this research, we have identified digital forensic artifacts resulting from a cyber-attack on Wide Area Monitoring, Protection and Control (WAMPAC) systems, which will help an investigator attribute an attack using the identified evidences. The research also shows the usage of sandboxing for digital forensics along with hardware-in-the-loop (HIL) setup. This is first of its kind effort to identify and acquire all the digital forensic evidences for WAMPAC systems which will ultimately help in building a body of knowledge and taxonomy for power system forensics.

The existing radial topology makes the power system less reliable since any part in the system failure will disrupt electrical power delivery in the network. The increasing security concerns, electrical energy theft, and present advancement in Information and Communication Technologies are some factors that led to modernization of power system. In a smart grid, a network of smart sensors offers numerous opportunities that may include monitoring of power, consumer-side energy management, synchronization of dispersed power storage, and integrating sources of renewable energy. Smart sensor networks are low cost and are ease to deploy hence they are favorable contestants for deployment smart power grids at a larger scale. These networks will result in a colossal volume of dissimilar range of data that require an efficient processing and analyzing process in order to realize an efficient smart grid. The existing technology can be used to collect data but dealing with the collected information proficiently as well as mining valuable material out of it remains challenging. The paper investigates communication technologies that maybe deployed in a smart grid. In this paper simulations results for the Additive White Gaussian Noise (AWGN) channel are illustrated. We propose a model and a communication network domain riding on the power system domain. The model was interrogated by simulation in MATLAB.

The paper dwells on the peculiarities of stylometry technologies usage to determine the style of the author publications. Statistical linguistic analysis of the author's text allows taking advantage of text content monitoring based on Porter stemmer and NLP methods to determine the set of stop words. The latter is used in the methods of stylometry to determine the ownership of the analyzed text to a specific author in percentage points. There is proposed a formal approach to the definition of the author's style of the Ukrainian text in the article. The experimental results of the proposed method for determining the ownership of the analyzed text to a particular author upon the availability of the reference text fragment are obtained. The study was conducted on the basis of the Ukrainian scientific texts of a technical area.

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.

Most of the countries evaluate their energy networks in terms of national security and define as critical infrastructure. Monitoring and controlling of these systems are generally provided by Industrial Control Systems (ICSs) and/or Supervisory Control and Data Acquisition (SCADA) systems. Therefore, this study focuses on the cyber-attack vectors on SCADA systems to research the threats and risks targeting them. For this purpose, TCP/IP based protocols used in SCADA systems have been determined and analyzed at first. Then, the most common cyber-attacks are handled systematically considering hardware-side threats, software-side ones and the threats for communication infrastructures. Finally, some suggestions are given.

Measurers are critical to a remote attestation (RA) system to verify the integrity of a remote untrusted host. Run-time measurers in a dynamic RA system sample the dynamic program state of the host to form evidence in order to establish trust by a remote system (appraiser). However, existing run-time measurers are tightly integrated with specific software. Such measurers need to be generated anew for each software, which is a manual process that is both challenging and tedious. In this paper we present a novel approach to decouple application-specific measurement policies from the measurers tasked with performing the actual run-time measurement. We describe MSRR (MeaSeReR), a novel general-purpose measurement framework that is agnostic of the target application. We show how measurement policies written per application can use MSRR, eliminating much time and effort spent on reproducing core measurement functionality. We describe MSRR's robust querying language, which allows the appraiser to accurately specify the what, when, and how to measure. We evaluate MSRR's overhead and demonstrate its functionality.

The dynamically changing landscape of DDoS threats increases the demand for advanced security solutions. The rise of massive IoT botnets enables attackers to mount high-intensity short-duration ”volatile ephemeral” attack waves in quick succession. Therefore the standard human-in-the-loop security center paradigm is becoming obsolete. To battle the new breed of volatile DDoS threats, the intrusion detection system (IDS) needs to improve markedly, at least in reaction times and in automated response (mitigation). Designing such an IDS is a daunting task as network operators are traditionally reluctant to act - at any speed - on potentially false alarms. The primary challenge of a low reaction time detection system is maintaining a consistently low false alarm rate. This paper aims to show how a practical FPGA-based DDoS detection and mitigation system can successfully address this. Besides verifying the model and algorithms with real traffic ”in the wild”, we validate the low false alarm ratio. Accordingly, we describe a methodology for determining the false alarm ratio for each involved threat type, then we categorize the causes of false detection, and provide our measurement results. As shown here, our methods can effectively mitigate the volatile ephemeral DDoS attacks, and accordingly are usable both in human out-of-loop and on-the-loop next-generation security solutions.

Virtual Machine Introspection (VMI) is an emerging family of techniques for extracting data from virtual machines without the use of active monitoring probes within the target machines themselves. In VMI based systems, the data is collected at the hypervisor-level by analyzing the state of virtual machines. This has the benefit of making collection harder to detect and block by malware as there is nothing in the machine indicating that monitoring is taking place. In this paper we present Nitro Web, a web-based monitoring system for virtual machines that uses virtual machine introspection for data collection. The platform is capable of detecting and visualizing system call activity taking place within virtual machines in real-time. The secondary purpose of this paper is to offer an introduction to Nitro virtual machine introspection framework that we have been involved in developing. In this paper, we reflect on how Nitro Framework can be used for building applications making use of VMI data.