Active Cyber Defense in the Healthcare Sector

The healthcare industry is a vulnerable sector when it comes to cybercrime. To date, it continues to suffer the highest losses for twelve consecutive years (IBM, 2022). As care- providing systems depend more and more on technology, information assets become an appealing target for cyber criminals. Health data often contains sensitive and identifiable information such as full names, addresses, phone numbers, emails, Social Security Numbers, etc. All these falls under the term Personal Identifiable Information (PII) which are protected by many laws and acts with the purpose of protecting one’s privacy from harms such as identity theft and other fraudulent offenses. In addition to the privacy concern, there is also financial and reputational concerns involved. The health sector suffers frequents attacks and the number continues to grow every year. The purpose of this research thesis paper is to analyze the cyber defense technique Active Cyber Defense (ACD) in relation to the healthcare sector. It seeks to investigate the ways in which the health sector can benefit from incorporating ACD in its security strategy as well as analyzing the various security challenges that the health sector faces and how it attempts to address them. This research will be supported by research papers, government documents, reports, and articles

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

INTRUSION PREDICTION SYSTEM FOR CLOUD COMPUTING AND NETWORK BASED SYSTEMS

Cloud computing offers cost effective computational and storage services with on-demand scalable capacities according to the customers’ needs. These properties encourage organisations and individuals to migrate from classical computing to cloud computing from different disciplines. Although cloud computing is a trendy technology that opens the horizons for many businesses, it is a new paradigm that exploits already existing computing technologies in new framework rather than being a novel technology. This means that cloud computing inherited classical computing problems that are still challenging. Cloud computing security is considered one of the major problems, which require strong security systems to protect the system, and the valuable data stored and processed in it. Intrusion detection systems are one of the important security components and defence layer that detect cyber-attacks and malicious activities in cloud and non-cloud environments. However, there are some limitations such as attacks were detected at the time that the damage of the attack was already done. In recent years, cyber-attacks have increased rapidly in volume and diversity. In 2013, for example, over 552 million customers’ identities and crucial information were revealed through data breaches worldwide [3]. These growing threats are further demonstrated in the 50,000 daily attacks on the London Stock Exchange [4]. It has been predicted that the economic impact of cyber-attacks will cost the global economy $3 trillion on aggregate by 2020 [5]. This thesis focused on proposing an Intrusion Prediction System that is capable of sensing an attack before it happens in cloud or non-cloud environments. The proposed solution is based on assessing the host system vulnerabilities and monitoring the network traffic for attacks preparations. It has three main modules. The monitoring module observes the network for any intrusion preparations. This thesis proposes a new dynamic-selective statistical algorithm for detecting scan activities, which is part of reconnaissance that represents an essential step in network attack preparation. The proposed method performs a statistical selective analysis for network traffic searching for an attack or intrusion indications. This is achieved by exploring and applying different statistical and probabilistic methods that deal with scan detection. The second module of the prediction system is vulnerabilities assessment that evaluates the weaknesses and faults of the system and measures the probability of the system to fall victim to cyber-attack. Finally, the third module is the prediction module that combines the output of the two modules and performs risk assessments of the system security from intrusions prediction. The results of the conducted experiments showed that the suggested system outperforms the analogous methods in regards to performance of network scan detection, which means accordingly a significant improvement to the security of the targeted system. The scanning detection algorithm has achieved high detection accuracy with 0% false negative and 50% false positive. In term of performance, the detection algorithm consumed only 23% of the data needed for analysis compared to the best performed rival detection method

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Understanding digital intelligence and the norms that might govern it

Abstract: This paper describes the nature of digital intelligence and provides context for the material published as a result of the actions of National Security Agency contractor Edward Snowden. It looks at the dynamic interaction between demands from government and law enforcement for digital intelligence, and at the new possibilities that digital technology has opened up for meeting such demands. The adequacy of previous regimes of legal powers and governance arrangements is seriously challenged just at a time when the objective need for intelligence on the serious threats facing civil society is apparent. This paper suggests areas where it might be possible to derive international norms, regarded as promoting standards of accepted behaviour that might gain widespread, if not universal, international acceptance, for the safe practice of digital intelligence

Insider Threats to Information Systems

There are few, if any, organizations immune to the adverse and costly effects of successful information system attacks. As reliance on information systems continues to increase, organizations must continue to implement effective computer security measures to maintain their operability. This paper focuses on internal attacks executed by those individuals within the organization who have authorized access to information systems and behave in an unethical manner. We examine categorization of insiders; the motives and psychological profiles behind their destructive behavior; and conclude with a discussion of several measures that organizations can implement in order to detect and defend against insider threats

Statistical methods used for intrusion detection

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 58-64)Text in English; Abstract: Turkish and Englishx, 71 leavesComputer networks are being attacked everyday. Intrusion detection systems are used to detect and reduce effects of these attacks. Signature based intrusion detection systems can only identify known attacks and are ineffective against novel and unknown attacks. Intrusion detection using anomaly detection aims to detect unknown attacks and there exist algorithms developed for this goal. In this study, performance of five anomaly detection algorithms and a signature based intrusion detection system is demonstrated on synthetic and real data sets. A portion of attacks are detected using Snort and SPADE algorithms. PHAD and other algorithms could not detect considerable portion of the attacks in tests due to lack of sufficiently long enough training data

An Interactive Relaxation Approach for Anomaly Detection and Preventive Measures in Computer Networks

It is proposed to develop a framework of detecting and analyzing small and widespread changes in specific dynamic characteristics of several nodes. The characteristics are locally measured at each node in a large network of computers and analyzed using a computational paradigm known as the Relaxation technique. The goal is to be able to detect the onset of a worm or virus as it originates, spreads-out, attacks and disables the entire network. Currently, selective disabling of one or more features across an entire subnet, e.g. firewalls, provides limited security and keeps us from designing high performance net-centric systems. The most desirable response is to surgically disable one or more nodes, or to isolate one or more subnets.The proposed research seeks to model virus/worm propagation as a spatio-temporal process. Such models have been successfully applied in heat-flow and evidence or gestalt driven perception of images among others. In particular, we develop an iterative technique driven by the self-assessed dynamic status of each node in a network. The status of each node will be updated incrementally in concurrence with its connected neighbors to enable timely identification of compromised nodes and subnets. Several key insights used in image analysis of line-diagrams, through an iterative and relaxation-driven node labeling method, are explored to help develop this new framework

A Survey on Intrusion Detection Systems for Fog and Cloud Computing

The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area