The document discusses Darktrace's Enterprise Immune System technology, which takes inspiration from the human immune system to provide cyber defense. It uses unsupervised machine learning and advanced mathematics to learn what normal network behavior looks like and detect anomalies indicating threats. This self-learning approach can identify new threats that traditional signature-based tools miss. The system also automatically responds to threats with targeted digital responses. Darktrace's technology represents a new approach to cybersecurity that is better suited to today's sophisticated and unpredictable threat landscape.
The attackers used a spear phishing campaign targeting RSA employees to gain access to the RSA network. They sent emails appearing to come from a job site with a malicious Excel spreadsheet attachment exploiting Flash vulnerabilities. This allowed the attackers to install backdoors and remote access tools on the network. They were then able to escalate privileges and extract encrypted password-protected files containing user SecurID tokens. The stolen data was suspected to be used in an attempted attack on Lockheed Martin, though their security measures detected the threat. In response, RSA improved security including issuing new SecurID tokens and launching incident response services.
Autonomic Anomaly Detection System in Computer Networksijsrd.com
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
EXTERNAL - Whitepaper - How 3 Cyber ThreatsTransform Incident Response 081516Yasser Mohammed
This document discusses how three cyber threats - targeted attacks, system exploits, and data theft - are transforming incident response. It provides three case studies:
1) Operation Aurora targeted Google and other companies through a multi-stage attack using custom malware. Cyberforensics tools could have helped identify compromised systems and collect evidence.
2) The Zeus botnet exploits systems by infecting them and forwarding login credentials. Regular scans using cyberforensics tools can establish a baseline and detect any anomalies to address risks.
3) Data loss or theft of regulated/sensitive data from laptops or compromised websites can result in lost revenue and reputation damage. Cyberforensics tools can help find and wipe such data from unauthorized
Current Studies On Intrusion Detection System, Genetic Algorithm And Fuzzy Logicijdpsjournal
This document summarizes a research paper on current studies of intrusion detection systems using genetic algorithms and fuzzy logic. The paper presents an overview of intrusion detection systems, including different techniques like misuse detection and anomaly detection. It discusses using genetic algorithms to generate fuzzy rules to characterize normal and abnormal network behavior in order to reduce false alarms. The paper also outlines the dataset, genetic algorithm approach, and use of fuzzy logic that are proposed for the intrusion detection system.
Intrusion Detection System using Data MiningIRJET Journal
This document presents a proposed intrusion detection system using data mining techniques. It begins with an abstract that describes how internal intrusions are difficult to detect as internal users know the organization's information. It then discusses how anomaly detection can be used to create behavior profiles for each user and detect anomalous activities. The introduction provides background on intrusion detection systems and the need for more efficient and effective detection methods. It describes the proposed system which will use data mining techniques like k-means clustering to separate normal and abnormal network activities in order to detect internal attacks. It discusses the hardware and software requirements and specifications. Finally, it concludes that the proposed system can better detect anomalies in the network compared to other machine learning approaches.
In recent years, wireless sensor network (WSN) is used in several application areas resembling observance, tracking, and dominant in IoTs. for several applications of WSN, security is a crucial demand. However, security solutions in WSN disagree from ancient networks because of resource limitation and process constraints. This paper analyzes security solutions: TinySec, IEEE 802.15.4, SPINS, MiniSEC, LSec, LLSP, LISA, and LISP in WSN. This paper additionally presents characteristics, security needs, attacks, cryptography algorithms, and operation modes. This paper is taken into account to be helpful for security designers in WSNs.
The document discusses ethical hacking and summarizes:
1) Ethical hackers evaluate the security of systems by using the same techniques as criminal hackers but without causing damage or theft, in order to identify vulnerabilities and help clients strengthen their security.
2) Successful ethical hackers have strong technical skills as well as trustworthiness, patience, and a drive to continuously improve security. They conduct thorough evaluations that simulate real attacks.
3) The goal of an ethical hack is to answer what information an intruder could access, what they could do with it, and whether the target would notice intrusion attempts, in order to identify security weaknesses before criminals can exploit them.
This document discusses information system security. It defines information system security as collecting activities to protect information systems and stored data. It outlines four components of an IT security policy framework: policies, standards, procedures, and guidelines. It also discusses vulnerabilities, threats, attacks, and trends in attacks. Vulnerabilities refer to weaknesses, while threats use tools and scripts to launch attacks like reconnaissance, access, denial of service, and viruses/Trojans. Common attacks trends include malware, phishing, ransomware, denial of service, man-in-the-middle, cryptojacking, SQL injection, and zero-day exploits.
This document summarizes the industrial cyber threat landscape as of September 2017. It outlines several high-profile cyber attacks on industrial control systems dating back to 2010, including Stuxnet, Shamoon, BlackEnergy, and CrashOverride. These attacks targeted critical infrastructure like power grids, water treatment plants, and an Iranian nuclear facility. The document also discusses the risks and costs of these incidents, which include physical damage, production shutdowns, and an estimated global cost of cybercrime reaching $6 trillion by 2021. Mitigation strategies are proposed, such as using gateways and managed remote access to block malware and unauthorized access to industrial control networks.
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paper
Fortalecimiento de la seguridad combinando las capacidades de analíticos sobre logs y paquetes de red, además de las capacidades avanzadas de detección de malware,
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
Subaira A. S. and Anitha P.
Automated Biometric Verification: A Survey on Multimodal Biometrics ..............................................1
Rupali L. Telgad, Almas M. N. Siddiqui and Dr. Prapti D. Deshmukh
Design and Implementation of Intelligence Car Parking Systems ........................................................1
Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
Demosaicing and Super-resolution for Color Filter Array via Residual Image Reconstruction and Sparse
Representation..................................................................................................................................1
Jie Yin, Guangling Sun and Xiaofei Zhou
Determining Weight of Known Evaluation Criteria in the Field of Mehr Housing using ANP Approach ..1
Saeed Safari, Mohammad Shojaee, Mohammad Tavakolian and Majid Assarian
Application of the Collaboration Facets of the Reference Model in Design Science Paradigm ...............1
Lukasz Ostrowski and Markus Helfert
Personalizing Education News Articles Using Interest Term and Category Based Recommender
Approaches .......................................................................................................................................1
This document discusses network security. It begins by defining network security and explaining the three main types: physical, technical, and administrative security controls. It then defines vulnerabilities as weaknesses that can be exploited by threats such as unauthorized access or data modification. Common network attacks are described as reconnaissance, access, denial of service, and worms/viruses. Emerging attack trends include malware, phishing, ransomware, denial of service attacks, man-in-the-middle attacks, cryptojacking, SQL injection, and zero-day exploits. The document aims to help students understand vulnerabilities, threats, attacks, and trends regarding network security.
Day by day the internet is becoming an essential part of everyone’s life. In India from 2015 – 2020, there is an increase in internet users by 400 million users. As technology and innovation are increasing rapidly. Security is a key point to keep things in order. Security and privacy are the biggest concern in the world let it is in any field or domain. There is no big difference in cyber security the security is the biggest concern worrying about attacks which could happen anytime. So, in this paper, we are going to talk about honeypot comprehensively. The aim is to track hacker to analyze and understand hacker attacker behavior to create a secure system which is sustainable and efficient. Anoop V Kanavi | Feon Jaison "Honeypot Methods and Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38045.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-security/38045/honeypot-methods-and-applications/anoop-v-kanavi
A Study on Recent Trends and Developments in Intrusion Detection SystemIOSR Journals
This document discusses recent trends and developments in intrusion detection systems. It covers several topics:
- Artificial intelligence and machine learning techniques like neural networks, genetic algorithms, and fuzzy logic can be applied to intrusion detection to improve detection capabilities.
- There are different types of intrusion detection systems, including network-based, host-based, and wireless intrusion detection. Signature-based and anomaly-based detection are also discussed.
- Popular open source intrusion detection tools like Snort are discussed as alternatives to commercial intrusion prevention systems for some organizations.
- Intrusion prevention systems not only detect attacks but can also block attacks in real-time, providing an enhanced level of protection over intrusion
This paper describes the concept of implementing the network vulnerability assessment process as a web service in Eucalyptus cloud.This paper is published in one of the international conferences.I implemented the mentioned concept during my M.E. thesis.
Intrusion detection systems aim to detect unauthorized access to computer systems and networks. There are three main types: anomaly-based detection identifies deviations from normal behavior profiles; signature-based detection looks for known threat patterns; and hybrid detection combines the two approaches. Intrusion detection systems are also classified based on their monitoring scope, including network-based systems that monitor network traffic and host-based systems that monitor logs and activities on individual computers. Recent research focuses on developing more effective hybrid systems and methods that can detect both known and unknown threats.
Deep Learning based Threat / Intrusion detection systemAffine Analytics
The document describes a proposed intrusion/threat detection system with the following key components:
1. A feature engineering module to extract relevant features from organizational data like employee information and online activities.
2. A text processing and topic modeling module to analyze communications data and identify confidential information.
3. An internal threat detection system using deep learning to detect threats in real-time with a risk score and predefined response policies.
4. An external threat detection system using signatures and anomaly detection to enforce actions against external threats.
Panda Adaptive Defense - The evolution of malwarePanda Security
We analyze the evolution of malware and the next generation of Enpoint Protection agaings targeted attacks: Adaptive Defense.
More info: http://www.pandasecurity.com/enterprise/solutions/advanced-threat-protection/
This document contains Serina Howard's mastery journal for her Master's in Fine Arts in Creative Writing. It outlines various goals, strategies, and tactics for developing skills in areas like teaching creative writing, visual storytelling, script analysis, character development, episodic and serial writing, screenwriting, writing for games, animation, editing, and the business of creative writing. It also lists masters in the industry, clubs and organizations, innovative companies, and ways to get involved at Full Sail University. The overall goal is to pursue a career in teaching creative writing and define a specific focus area for her mastery journey.
This document provides information to help wealth advisors identify and assist clients who may be struggling with substance abuse or other behavioral health issues. It discusses signs to look out for, the importance of legal documentation that allows advisors to communicate with family members, different types of treatment options and specialties, and resources for intervention and treatment referral. Case studies of potential clients with issues are also presented to demonstrate how to assess situations using the ABC (age of first use, big changes, co-existing issues) model.
Institutional review board by akshdeep sharmaAkshdeep Sharma
The Institutional Review Board/Independent Ethics Committee (IRB/IEC) serves as an independent body that reviews and approves clinical trials to protect participant safety and rights. The IRB/IEC consists of at least five members with diverse qualifications to evaluate scientific and ethical aspects of trials. The IRB/IEC's responsibilities include reviewing trials, providing continuing oversight, ensuring informed consent, and maintaining records for regulatory review.
This document discusses personal branding and making meaningful connections. It covers why personal branding is important due to high job competition and the need to seem credible. It also discusses discovering your strengths and values, defining your brand through a focus and promise, and directing your brand goals. The document provides exercises on branding in groups and skills development. It outlines deploying your brand through online profiles and consistency, as well as getting noticed the right way through first impressions, networking, and relationship building and management.
Rencana pelaksanaan pembelajaran mata pelajaran Pendidikan Pancasila dan Kewarganegaraan membahas materi dinamika demokrasi dalam kehidupan bermasyarakat, berbangsa dan bernegara. Rencana ini mencakup tujuan pembelajaran, kompetensi dasar, indikator, alokasi waktu, metode pembelajaran, dan penilaian hasil pembelajaran.
This document provides a summary of Schedule Y, the regulatory framework for clinical trials in India. Key points include:
- Schedule Y was established under the Drugs and Cosmetics Act of 1945 and outlines the regulations for conducting clinical trials in India, in line with ICH GCP guidelines.
- It addresses approval for clinical trials, responsibilities of sponsors, investigators and ethics committees, informed consent, different phases of clinical trials, and special populations.
- Appendices provide details on application process, data requirements, animal studies, informed consent format and investigator undertakings to ensure compliance.
- Revisions to Schedule Y have aimed to strengthen clinical research governance in India and align it with global standards, while
The document discusses a new approach to cybersecurity called the Enterprise Immune System. It is based on advanced machine learning and mathematics to detect threats within an organization's networks. Like the human immune system, it learns what normal activity looks like and can detect subtle anomalies that may indicate threats. This allows organizations to protect themselves while still enabling collaboration and connectivity. The system is based on novel probabilistic mathematics that continuously learns and adapts to changing environments in real time.
Darktrace's Industrial Immune System provides continuous threat monitoring for critical infrastructure organizations like oil and gas, energy, and manufacturing plants. It uses advanced machine learning and behavioral analytics to establish a baseline of normal activity on industrial control systems (ICS) networks. This allows it to detect abnormal and potentially malicious behavior in real-time, even from unknown threats, and flag them for investigation before they can cause major issues. As ICS networks increasingly connect to corporate IT networks and the internet, they become more vulnerable to cyber attacks but existing defenses like firewalls have proven inadequate, making a solution like Darktrace's important for enhanced protection.
This document introduces the need for a new security model to address the full attack continuum - before, during, and after attacks. Traditional security methods relying on detection and blocking are no longer adequate against modern threats. The threat landscape has evolved to include sophisticated, well-funded attackers employing techniques like zero-days, advanced persistent threats, and industrialized hacking for profit. Additionally, new business models and the growth of the Internet of Everything have expanded networks and attack surfaces. A new security model is needed to provide comprehensive visibility and protection across changing IT infrastructures and against evolving threats.
This document discusses the need for a new approach to cybersecurity using machine learning and mathematics to deliver an "immune system for the enterprise." It argues the traditional approach of separating inside and outside has failed because threats are already inside complex networks and subtle human behaviors are difficult to detect. A new approach is needed to understand what is normal and identify subtle threats based on probabilities rather than rules. Insider threats are underestimated as employees and partners with access could intentionally or unintentionally help attackers. Ensuring data integrity beyond just preventing loss or theft is also key to protecting organizations.
Information Securityfind an article online discussing defense-in-d.pdfforladies
Information Security
find an article online discussing defense-in-depth. List your source and provide a paragraph
summary of what the article stated.
Solution
Abstract
The exponential growth of the Internet interconnections has led to a significant growth of cyber
attack incidents often with disastrous and grievous consequences. Malware is the primary choice
of weapon to carry out malicious intents in the cyberspace, either by exploitation into existing
vulnerabilities or utilization of unique characteristics of emerging technologies. The
development of more innovative and effective malware defense mechanisms has been regarded
as an urgent requirement in the cybersecurity community. To assist in achieving this goal, we
first present an overview of the most exploited vulnerabilities in existing hardware, software, and
network layers. This is followed by critiques of existing state-of-the-art mitigation techniques as
why they do or don\'t work. We then discuss new attack patterns in emerging technologies such
as social media, cloud computing, smartphone technology, and critical infrastructure. Finally, we
describe our speculative observations on future research directions.
A multi-layered approach to cyber security utilising machine learning and advanced analytics is
essential to defend against sophisticated multi-stage attacks including:
Insider Threats | Advanced Human Attacks | Supply Chain Infection | Ransomware |
Compromised User Accounts | Data Loss
Prepare for a cyber security incident or attack and how to adequately manage the aftermath with
an organised approach to Incident Response – coordinating resources, people, information,
technology and complying with regulations.
INSIDER THREATS
Insider threat can originate from employees, contractors, third party services or anyone with
access rights to your network, corporate data or business premises.
The challenge is to identify attacks and understand how they develop in real-time by analysing
and correlating the subtle signs of compromise that an insider makes when they infiltrate the
network.
Traditional security measures are no longer sufficient to combat insider threat. A more
sophisticated, intelligence-based approach is required. Cyberseer uses machine-learning
technology to form a behavioural baseline for every user to determine normal activity and spot
new, previously unidentified threat behaviours. The move to a more proactive approach towards
security will enable companies to take action to thwart developing situations escalating into
exfiltrated information or damaging incidents.
ADVANCED HUMAN ATTACKS
Advanced threats use a set of stealthy and continuous processes to target an organisation, which
is often orchestrated for business or political motives by individuals (or groups). The “advanced”
process signifies sophisticated techniques using malware to exploit vulnerabilities in
organisations systems. They are considered persistent because an external command and control
system .
AI IN CYBERSECURITY: THE NEW FRONTIER OF DIGITAL PROTECTIONChristopherTHyatt
Artificial Intelligence (AI) fortifies cybersecurity by dynamically identifying and neutralizing cyber threats. With machine learning algorithms, AI analyzes patterns in real-time data, swiftly detecting anomalies and potential security breaches. This proactive approach enhances the overall defense mechanism, ensuring robust protection against evolving cyber threats in the ever-changing digital landscape.
1) The retail sector has been hit by a series of cyber attacks over the past few years that have compromised customer data at large companies like Target and Neiman Marcus.
2) Current cybersecurity approaches are too slow and reactive, focusing on malware after attacks occur rather than proactively detecting threats.
3) Behavioral cyber defense monitoring could have detected the abnormal behaviors of attackers on Target and Neiman Marcus' networks before data breaches occurred.
Safeguarding the Digital Realm Understanding CyberAttacks and Their Vital Cou...CyberPro Magazine
With the digital world becoming an essential aspect of our connected environment, there is always a risk of cyberattacks. The phrase "CyberAttacks" refers to a broad category of malevolent actions directed towards computer networks
Cyber threats are becoming more sophisticated and targeted attacks are harder to detect. Traditional security controls are no longer sufficient to defend against modern threats. Cyber intelligence provides total visibility into an organization's systems to detect emerging anomalies in real-time, before they become security incidents or crises. This intelligence-based approach uses adaptive technologies and skilled analysts to continuously learn, understand, and address developing issues. It aims to regain the advantage over attackers by enhancing visibility and informing timely decision-making.
Threat intelligence provides information across a wide range of sources to assist associations with safeguarding their resources by working with a designated network safety procedure. Call Us: +1 (978)-923-0040
Safeguarding the Digital Realm: Understanding CyberAttacks and Their Vital Co...CyberPro Magazine
With the digital world becoming an essential aspect of our connected environment, there is always a risk of cyberattacks. The phrase “CyberAttacks” refers to a broad category of malevolent actions directed towards computer networks, systems, and data. As technology develops, cybercriminals’ strategies also advance with it.
Why managed detection and response is more important now than everG’SECURE LABS
Managed Detection and Response (MDR) is an important cybersecurity tool for protecting organizations from increasingly sophisticated cyber attacks. MDR actively monitors networks for threats, detects intrusions and security issues, and responds quickly to prevent data breaches before they occur. By understanding an organization's environment and risks, MDR enhances threat prevention, detection, and response capabilities. With MDR, organizations can avoid the costly damages of data breaches and gain peace of mind knowing their data is secure.
Network security is a dynamic art, with dangers appearing as fast as black hats can exploit vulnerabilities. While there are basic “golden rules” which can make life difficult for the bad guys, it remains a challenge to keep networks secure. John Chambers, Executive Chairman of Cisco, famously said “there are two types of companies: those that have been hacked, and those who don’t know they have been hacked”. The question for most organizations isn’t if they’re going to be breached, but how quickly they can isolate and mitigate the threat. In this paper, we’ll examine best practices for effective cybersecurity – from both a proactive (access hardening) and reactive (threat isolation and mitigation) perspective. We’ll address how network automation can help minimize cyberattacks by closing vulnerability gaps and how it can improve incident response times in the event of a cyberthreat. Finally, we’ll lay a vision for continuous network security, to explore how machine-to-machine automation may deliver an auto-securing and self-healing network.
Go to www.esgjrconsultinginc.com
Toward Continuous Cybersecurity With Network AutomationKen Flott
Network security is a dynamic art, with dangers appearing as
fast as black hats can exploit vulnerabilities. While there are
basic “golden rules” which can make life difficult for the bad
guys, it remains a challenge to keep networks secure. John
Chambers, Executive Chairman of Cisco, famously said “there
are two types of companies: those that have been hacked, and
those who don’t know they have been hacked”. The question
for most organizations isn’t if they’re going to be breached, but
how quickly they can isolate and mitigate the threat.
In this paper, we’ll examine best practices for effective
cybersecurity – from both a proactive (access hardening)
and reactive (threat isolation and mitigation) perspective.
We’ll address how network automation can help minimize
cyberattacks by closing vulnerability gaps and how it can
improve incident response times in the event of a cyberthreat.
Finally, we’ll lay a vision for continuous network security, to
explore how machine-to-machine automation may deliver an
auto-securing and self-healing network.
Cyber security refers to the body of technologies, processes, and practices designed to protect networks, devices, programs, and data from cyber attacks like unauthorized access, malware, and phishing. Common cyber threats include ransomware, Trojans, and denial of service attacks. Implementing effective cyber security helps organizations securely collect, store, and transfer sensitive data while protecting against threats and improving recovery from breaches. However, challenges remain such as keeping up with evolving attacks and filling many open cyber security jobs.
The intelligence lifecycle entails transforming raw data into final intelligence for decision-making. Deconstruct this domain to boost your organization's cyber defenses.
How to avoid cyber security attacks in 2024 - CyberHive.pdfonline Marketing
Technology continues to evolve at a rapid pace, presenting both opportunities and challenges. Among these challenges, the threat of cyber security attacks looms large. This poses significant risks to individuals, businesses, and governments alike. The importance of adopting robust security measures cannot be overstated. please visit: https://www.cyberhive.com/insights/how-to-avoid-cyber-security-attacks-in-2024/
Five Network Security Threats And How To Protect Your Business Wp101112Erik Ginalick
The document discusses 5 of the most costly network security threats faced by enterprises: 1) botnets, 2) phishing, 3) malware, 4) distributed denial of service (DDoS) attacks, and 5) increasingly sophisticated attacks. It recommends implementing key layers of control through network perimeter protections, cloud-based security services, mobile device security, and partnering with a managed security provider to help prevent threats and do more with less.
The document discusses 5 of the most costly network security threats faced by enterprises: 1) botnets, 2) phishing, 3) malware, 4) distributed denial of service (DDoS) attacks, and 5) increasingly sophisticated attacks. It recommends implementing key layers of control through network perimeter protections, cloud-based security services, mobile device security, and endpoint compliance to effectively prevent and mitigate these threats. Outsourcing security functions to a managed security services provider can help organizations do more with less by avoiding in-house technology and staffing costs.
1. The Enterprise Immune System
WHITE PAPER
Proven Mathematics and Machine
Learning for Cyber Defense
2. 2
Executive Summary
By leveraging the power of advanced machine learning
and mathematics, Darktrace takes a fundamentally
new approach to the challenge of defending against
sophisticated cyber-attacks.
This approach, known as the Enterprise Immune System,
represents a new category of cyber defense technology.
It deploys cutting-edge technology modeled after one
of the most powerful biological systems — the human
immune system. By understanding what ‘normal’ looks
like, our immune systems can distinguish ‘us’ from ‘not
us’ to quickly identify potential threats. This self-learning
capability gives it the means to adapt and evolve in an
intelligent manner, defending against new threats and
changing environments.
The self-learning mechanisms of the human immune
system inspired Darktrace’s flagship technology: the
Enterprise Immune System. Powered by advances in
unsupervised machine learning developed by specialists
from the University of Cambridge, the Enterprise Immune
System intelligently detects emerging threats that other
security tools miss. The system can also automatically
fight back with ‘digital antibodies’ — targeted responses
that complete the end-to-end functionality of the
Enterprise Immune System.
To devise this new approach, mathematicians from
the University of Cambridge developed a breakthrough
in Bayesian theory. This new branch of probabilistic
mathematics, called Recursive Bayesian Estimation,
helps to make sense of huge data sets, deal with
uncertainty, and, ultimately, identify cyber-attacks amid
the noise of a network.
These groundbreaking advancements generated the
first-ever immune system for the enterprise. With
thousands of deployments around the world, the
technology has equipped organizations of all sizes to
defend themselves in a world of constant threat, where
attacks move at machine-speed and strike without
warning.
"Today’s threats are far too
unpredictable for traditional
security measures to be
effective – and manifestly they
are failing repeatedly.
The good news for defenders is
that new technologies are now
available that evolve and adapt
in line with constantly-changing
networks and constantly-
changing threats, allowing
them to spot early indicators
of suspicious activity, amid the
noise of the network."
Alan Wade, former CIO, Central Intelligence
Agency
3. White Paper
3
The Threat Is Already Inside
The traditional approach to cyber security relies on
a distinction between inside and outside. However,
boundaries are virtually impossible to define in modern
infrastructures. Today’s networks are global, complex,
and porous. They have to be, in order to remain
competitive. But while this has increased productivity,
it has also introduced new vulnerabilities. Given the
constant risk that organizations face, the only sensible
way to approach cyber security is to assume that the
threat is already inside.
Malicious code can lie dormant for years before
initiating a kill-switch. Or an attacker can hide in plain
sight, blending into network traffic and altering sensitive
data. And the growing Internet of Things has given
threat-actors an unprecedented number of entry points.
Using traditional tools, it is impossible to know whether
an organization has been infiltrated.
Thethreatposedbyinsidersisfrequentlyunderestimated
— and remains an extremely difficult problem to solve.
Edward Snowden proved that even the best-defended
and most security-conscious organizations are
vulnerable to lone attackers who move silently within
their systems and have the means to undermine their
entire operation.
While the most damaging internal attacks come from
measured, intentional action, insider threats span the
entire spectrum of severity and motivation. Indeed,
many insider threats aren’t malicious at all. Everyday
actions and forgotten security protocols — like taking
work home via the cloud — pose a serious security risk.
Moreover, employees can fall victim to a customized
phishing attack, or may misuse access privileges for the
sake of convenience.
The Threat Landscape Has Changed
In recent years, cyber-attacks have grown exponentially
more sophisticated. It is not just a question of data
theft and defaced websites. Now, organizations have
to contend with threats that are far more subtle and
stealthy, and may persist inside the network for many
months.
Today’s highly disruptive attackers often want to destroy
the very integrity of data, rather than just take it. Through
targeted data manipulation, cyber-attackers can
undermine confidence in entire systems and companies,
all while remaining undetected. Blending into the noise
of the network, malicious code can activate for only a
few milliseconds a year — more than enough time to
change data, but not enough time for the security team
to know what, if anything, has changed. For instance,
a threat-actor could tweak bank account numbers to
wreak financial havoc, or adjust data from an oilfield
sensor to trick a company into mining a depleted area.
Moreover, modern attacks are trending away from
human-operated threats and toward automated attacks
and artificial intelligence. Ransomware attacks have
been endemic in recent years. This new scourge is
an automated attack that moves at machine-speed,
encrypting an entire network in a matter of minutes.
The attacker then demands a fee for the organization to
regain control of the network.
The rise of ransomware signals a sustained move
toward AI attacks that self-learn and blend into network
traffic, without needing a human controller.
Finally, the rise of nation-state hacking represents a
profound shift in the modern threat landscape. Foreign
nations can now employ devastating cyber-attacks to
gain an economic or political edge. As seen in the high-
profile hack on the Democratic National Committee,
foreign powers are now willing to use cyber-attacks
to influence politics and erode public trust. With vast
resources at their command, nation states can afford
to deploy the most advanced tactics in these so-called
‘trust attacks’, which will increasingly target private
sector organizations.
Given the sophisticated state of the modern threat
landscape, the risk facing modern companies is both
inherent and omnipresent. Organizations now have
to contend with near-ubiquitous threat, and the cyber
security solution for the future has to accept this new
reality.
“Good cyber security is not just
about a really strong wall on
the outside, but some kind of
an immune system within.”
Lord Evans of Weardale, former Director
General, MI5
4. 4
And employees are just the tip of the iceberg when it
comestoinsiderthreats.Third-partyvendors,customers,
and anyone with network or physical access represents
a potential threat. Without company loyalty, they have
even more motivation to take shortcuts or exploit their
position for financial gain. With such a broad range of
potential threats, it is nearly impossible to identify high-
risk users in advance.
This core principle lies at the heart of the Enterprise
Immune System. Networks are inherently at risk from
insider threats and external attackers. The Enterprise
Immune System operates on the basis that the only way
to secure a complex, fast-moving information system is
to assume that it’s already been compromised.
The Legacy Approach
Network perimeters still represent the crucial first
line of defense. But as the industry now recognizes,
perimeters on their own are ill-equipped to handle
today's sophisticated cyber-attacks.
There are three key reasons why legacy systems have
failed:
1. You can’t keep threat out
The traditional approach assumes that you can
keep attackers out by strengthening the perimeter.
Organizations have invested large amounts of time
and money into perimeter controls to insulate their
information systems from attack.
Unfortunately, threat-actors have proven to be more than
capable of overcoming perimeter controls, and insiders
can bypass the perimeter altogether. Companies have
to work on the assumption that they are constantly at
risk, and that many threats — and certainly the most
insidious — can infiltrate their organization with relative
ease.
2. You can’t define the threat
Definitions and signatures lie at the heart of traditional
security. This core IT principle, widely used for all manner
of automated applications, was duly transmuted to cyber
security. Indeed, many solutions still create definitions
of what ‘bad’ looks like, and protect the network against
that type of ‘bad’, if and when it is encountered again.
These systems need to be pre-programmed with
signatures of past attacks. And yet, there is no guarantee
that future attacks will look anything like what came
before. In fact, considering the constantly evolving
threat landscape, it is rather unlikely.
Whilethisapproachmayprotectagainstunsophisticated
attackers who repeatedly use the same tactics and
toolkit, serious attackers constantly change their
strategies and use custom malware to conquer a
specific target.
3. You can’t assume the threat is purely technical
In a world of botnets, Trojans, and Remote Access Tools,
it can be easy to forget the human behind every cyber-
attack. The most serious cyber-threats are directed by
skilled agents who move deftly through the network.
The traditional approach is incapable of dealing with
the complexity and subtlety that such attackers bring to
their missions.
"The reality of cyber security
today is that border defenses
are not enough to keep fast-
moving attacks out. Using
machine learning, Darktrace’s
unique Enterprise Immune
System detects zero-day
threats and suspicious insider
behaviors, without having to
define the activity in advance."
Michael Sherwood, CIO, City of Las Vegas
5. White Paper
5
Both internal and external parties usually exhibit
distinct behaviors before engaging in malicious acts.
A contractor logging on at an unusual time, groups of
files being aggregated, or an unusual volume of email
traffic — these are all signs that are often meaningless
to legacy tools, but form a compelling picture when
correlated.
The Enterprise Immune System
The Enterprise Immune System represents a fresh
approach that has successfully transformed the cyber
security landscape. Hundreds of businesses now benefit
from this innovative solution, which is built around
the premise that organizations face a constant level
of threat. This cutting-edge technology is capable of
learning ‘self’ on an adaptive, real-time basis — thereby
understanding when and where abnormal behavior first
manifests.
Like viral DNA, modern cyber-attacks constantly
evolve and mutate. These sophisticated attacks avoid
detection by subtly adjusting their behavior. Fortunately
for us, the human immune system is just as clever. It
is continually learning to understand precisely what
constitutes a threat. It’s not a perfect system — we still
catch the occasional cold — but it plays a critical role
by protecting us from threats which, if left unchecked,
would be life-threatening. Its adaptiveness enables us to
interact with each other and expose ourselves to risk on
a daily basis.
The Enterprise Immune System works on the same
premise. Built on a foundation of Bayesian mathematics
and unsupervised machine learning, the system
analyzes complex network environments to learn a
‘pattern of life’ for every network, device, and user.
Advanced machine learning techniques then correlate
patterns in network traffic to detect previously unknown
threats and automatically defend networks with
digital ‘antibodies’. The model doesn’t rely on rules or
signatures. Instead, it intelligently draws patterns from
large sets of data to discover deviations from ‘normal’
that indicate live, in-progress threats.
"A machine learning approach
is critical to cyber defense.
The self-learning technology
only focuses on the most
important threats and finds
abnormalities without any prior
assumptions."
Vari Bindra, Head of Cyber Defense Center,
Blackhawk Network
6. 6
Mathematics and Machine Learning
‘Done Right’
When machines replaced manual labor in the eighteenth
century, it was dubbed the Industrial Revolution. When
computers began performing repetitive, rote tasks en
masse, the Digital Revolution had dawned. Now, we are
in the midst of the third revolution in automation — the
Machine Learning Revolution.
In this new era of automation, machines have the ability
to exercise precise judgement and carry out advanced,
thoughtful tasks that were once reserved for human
specialists. Through machine learning, computers are
no longer restricted by rules and definitions. They can
understand uncertainty — indeed, they embrace it.
The Enterprise Immune System would not have been
possible without this revolution in machine learning.
However, a more subtle evolution has taken place in
recent years, which is at the heart of Darktrace’s core
technology. Traditional machine learning has been
‘supervised’, whereby a system is trained using a data
set built from pre-classified behaviors. In cyber security,
a program would flag unknown behavior as malicious
or benign depending on how closely it resembles the
known behavior.
Supervised machine learning has utility, but when
applied to cyber defense it fails to identify the all-
important ‘unknown unknowns’. Moreover, the method
requires significant human input, and it depends entirely
on pre-programmed rules. Such rules belie the subtlety
of modern attacks, and instead fall back on a rigid black-
and-white framework.
Unsupervised machine learning, on the other hand,
allows for shades of gray. Modern attacks exist on
a scale of type and severity. Unsupervised machine
learning captures the full spectrum of threat, as it doesn’t
require data sets, pre-defined labels, or any human input
whatsoever. Importantly, this lets the system go beyond
what its programmer knows, to discover previously
unknown threats.
The Enterprise Immune System employs unsupervised
machine learning to full effect, as well as a new field
of probabilistic mathematics known as Recursive
Bayesian Estimation. These advanced machine learning
algorithms are designed to analyze network data at scale
and intelligently handle the unexpected. The system
does not rely on knowledge of past attacks. Instead,
it discovers previously unknown threats by detecting
deviations from normal behavior.
To learn ‘normal’ for a network, the Enterprise Immune
System identifies naturally occurring groups of devices
and behaviors — a task that would be impossible to do
manually. Darktrace then employs advanced clustering
methods to analyze network behavior in terms of similar
devices on the same network. This generates a picture of
‘normal’ without reference to external data and without
human interference.
While traditional systems adopted a binary approach,
Darktrace accepts the inevitable ambiguity of such
data. The Enterprise Immune System recognizes
that behavior isn’t merely ‘malicious’ or ‘benign’. By
correlating a broad range of factors, like server access,
timing, and data volumes, Darktrace intelligently ranks
threat. This simultaneously allows organizations to
prioritize the most serious threats, and eliminates the
problem of false positives.
Equally important is the task of learning the unique
topology of intricate network structures. To achieve this,
the Enterprise Immune System utilizes iterative matrix
methods that reveal relationships between network
features. In conjunction, Darktrace uses an innovative
application of models from statistical physics to map
a network’s ‘energy landscape’ and reveal potentially
anomalous substructures.
A further problem lies in how to handle the huge number
of variables involved in modeling the high-dimensional
structure of complex network environments. In the
observation of packet traffic and host activity within an
enterprise LAN or WAN, where both input and output
can contain millions of inter-related features, learning a
sparse and consistent predictive function is challenged
by a lack of normal distribution.
In this context, the Enterprise Immune System is the
most advanced, large-scale computational approach
to learning sparse structure I/O models. It achieves
this by extending the L1-regularized regression model
– also known as the lasso method – to a family of
sparse ‘structured’ regression models. This allows
for the discovery of true associations between linked
malware, C2 events (inputs), and data egress (outputs),
efficiently solving convex optimization problems to yield
parsimonious models.
In combination with the advanced probabilistic
mathematics of Recursive Bayesian Estimation,
these models generate a comprehensive picture of an
enterprise network, granting full visibility of the network
structure in order to spot emerging threats.
7. White Paper
7
"Darktrace applies
mathematical models to create
statistically significant views
of user, device and network
behaviors – an approach that
makes it adept at detecting
attacks that are already within
the enterprise.”
Eric Ogren, 451 Research
The Enterprise Immune System takes this one step
further to create a truly self-learning and adaptive
technology that can even fight back with ‘digital
antibodies’.
Darktrace’s cutting-edge application of unsupervised
machine learning was a pivotal moment in the cyber
security industry. For the first time in history, a defense
system could learn the precise structure of complex
network environments to create a picture of ‘normal’
behavior, iteratively adapting itself to detect subtle
deviations and discover previously unknown cyber-
threats, all in real time and without the need for human
involvement.
Conclusion
In an era of pervasive threat, Darktrace’s novel approach
to cyber security has equipped businesses to intelligently
monitor their networks and automatically fight back
against the most serious cyber-attacks. The Enterprise
Immune System is a cutting-edge defense system
capable of learning ‘normal’, evolving with a network,
and detecting early-stage cyber-threats.
The Enterprise Immune System allows organizations to
understand threat holistically. The system is continually
learning, meaning it can handle unpredictable and
sophisticated threats. Crucially, its self-learning
mechanisms allow the system to move in step with both
the organization and the evolving threat landscape.
Organizations that have implemented an Enterprise
Immune System benefit from the world’s leading
advances in machine learning and mathematics to
protect against cyber-threats, all while maintaining the
flexibility and connectivity that modern businesses thrive
on. Darktrace’s technology sits on the cutting-edge of
cyber security, with a proprietary technology designed
around groundbreaking mathematics and machine
learning, and purpose-built to provide complete network
visibility and detect emerging threats in real time, which
would otherwise go unnoticed.