Hierarchical long short term memory network for cyberattack detection
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This document discusses a hierarchical long short-term memory (HLSTM) network for cyberattack detection. The HLSTM network is proposed to enhance intrusion detection systems, which traditionally struggle to quickly and accurately identify complex, diverse network attacks, especially low-frequency attacks. The HLSTM network introduces a hierarchical structure that allows the model to learn across multiple temporal levels in complex network traffic sequences. When evaluated on the NSL-KDD benchmark dataset, the HLSTM network showed better detection performance compared to existing methods, with higher accuracy and lower false detection rates for low-frequency attack types.
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For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
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Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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modeling as a part of requirements engineering in secure software development provides a structured
approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities
in a system. This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for
privacy threat modeling in relation to processing sensitive data in cloud computing environments. It
describes the modeling methodology that involved applying Method Engineering to specify characteristics
of a cloud privacy threat modeling methodology, different steps in the proposed methodology and
corresponding products. In addition, a case study has been implemented as a proof of concept to
demonstrate the usability of the proposed methodology. We believe that the extended methodology
facilitates the application of a privacy-preserving cloud software development approach from requirements
engineering to design.
Constructing a predictive model for an intelligent network intrusion detection
It is a new insight !!
Trust correlation of mobile agent nodes with a regular node in a Adhoc networ...
A mobile agent offers discrete advantage both in facilitating better transmission as well as controlling the traffic load in Mobile Adhoc Network (MANET). Hence, such forms of network offers maximized dependencies on mobile agents in terms of its trust worthiness. At present, there are various work being carried out towards resisting security breach in MANET; however approaches using mobile agent based mechanism is few to found. Therefore, the proposed system introduces a novel mathematical model where an extensive decision making system has been constructed for identifying the malicious intention of mobile agents in case they go rogues. By adopting multi-tier communication policy and fairness concept, the proposed system offers the capability to resist any form of malicious activity of mobile agent without even presence of any apriori information of adversary. The outcome shows proposed system outshines existing security scheme in MANET.
SECURE COLLABORATIVE PROCESSING ARCHITECTURE FOR MITB ATTACK DETECTION
In this paper, we take a distributed architecture called Semantic Room (SR) which is capable of correlating events coming from several organizations participating in the SR, developed in the context of the EU Project COMIFIN, and we add privacy capability to the SR.. The SR architecture consists of Edge Gateways deployed at each financial institution and a set of private clouds that form the SR collaborative processing system (CSP). Edge Gateways perform data pre-processing and anonymize data items, as prescribed by the SR contract, using Shamir secret sharing scheme. Anonymous data are sent to the CPS that aggregates information through MapReduce-based computations. The anonymous data resulting from the collaborative computation are revealed to the financial institutions only if suspicious cyber threat activities are detected. In this paper we show how this SR can be leveraged for detecting Man-In-TheBrowser attacks.
EWSN'15 Industry Session - Francesco Flammini (Ansaldo STS)
Ansaldo STS (Francesco Flammini) contribution to the industry session of the European conference on Wireless Sensor Networks (EWSN 2015, Porto, Portugal)
A PPLICATION OF C LASSICAL E NCRYPTION T ECHNIQUES FOR S ECURING D ATA -...
The process of protecting information by transformi
ng (encrypting) it into an unreadable format is cal
led
cryptography. Only those who possess secret key can
decipher (decrypt) the message into plain text.
Encrypted messages can sometimes be broken by crypt
analysis, also called code breaking, so there is a
need for strong and fast cryptographic methods for
securing the data from attackers. Although modern
cryptography techniques are virtually unbreakable,
sometimes they also tend to attack.
As the Internet, big data, cloud data storage and
other forms of electronic communication become more
prevalent, electronic security is becoming increasi
ngly important. Cryptography is used to protect e-m
ail
messages, credit card information, corporate data,
cloud data and big data so on... So there is a need
for
best and fast cryptographic methods for protecting
the data. In this paper a method is proposed to pro
tect
the data in faster way by using classical cryptogra
phy. The encryption and decryption are done in par
allel
using threads with the help of underlying hardware.
The time taken by sequential and parallel method i
s
analysed
A Location Based Cryptosystem For Mobile Devices Using Improved Rabin Algorithm
As per the recent studies, the volatile growth has been seen in the use of mobile devices as
the supporting technology for accessing Internet based services, as well as for personal
communication needs in networking. Various studies indicate that it is impossible to utilize strong
cryptographic functions for implementing security protocols on mobile devices. Our research negates
this. Explicitly, a performance analysis focused on the most commonly used cryptographic protocols
based on the location address (latitude & longitude) of the user for mobile applications and projected
provably secure authentication protocol that is more efficient than any of the prevailing
authentication protocol is being used by the network security methods. Understanding the use of
public key cryptography which makes potential use of discrete logarithms problem. The security of
ECC depends on the difficulty of Elliptic Curve Discrete Logarithm. To provide secure
communication for mobile devices, authenticated protocol is an important primitive for establishing
trusted connection. In this paper, it has been shown that the location based system using improved
Rabin Algorithm provides a better security and acquires much less energy consumption than the
existing authentication protocols.
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Constructing a predictive model for an intelligent network intrusion detection
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EWSN'15 Industry Session - Francesco Flammini (Ansaldo STS)
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A PPLICATION OF C LASSICAL E NCRYPTION T ECHNIQUES FOR S ECURING D ATA -...
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Three level intrusion detection system based on conditional generative advers...
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
A collaborative security framework for software defined wireless sensor networks
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A secure group key management scheme for sensor networks
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IoT Network Attack Detection using Supervised Machine Learning
The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion prediction.
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CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
Section of node density with cluster in spin for data centric wireless sensor...
Section of node density with cluster in spin for data centric wireless sensor network
Detecting network attacks model based on a convolutional neural network
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
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Hyperparameters optimization XGBoost for network intrusion detection using CS...
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
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Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Dynamic and public auditing with fair arbitration for cloud data
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Dynamic and public auditing with fair arbitration for cloud data
Shakas Technologies Provides, 2016-2017 Project titles, Real time project titles, IEEE Project Titles 2016-2017, NON-IEEE Project Titles, Latest project titles 2016, MCA Project titles, Final Year Project titles 2016-2017..
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Shakas Technologies
#13/19, 1st Floor, Municipal Colony,
Kangeyanellore Road, Gandhi Nagar,
Vellore-632006,
Cell: 9500218218
Mobile: 0416-2247353 / 6066663
Machine learning-based intrusion detection system for detecting web attacks
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
A fault tolerant approach to extend network life time of wireless sensor network
A fault tolerant approach to extend network life time of wireless sensor network.
Deep learning algorithms for intrusion detection systems in internet of thin...
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset.
Random walk based approach to detect clone attacks in wireless sensor networks
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A collaborative security framework for software defined wireless sensor networks
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CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET
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Detecting network attacks model based on a convolutional neural network
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Security in Software Defined Networks (SDN): Challenges and Research Opportun...
Hyperparameters optimization XGBoost for network intrusion detection using CS...
Hyperparameters optimization XGBoost for network intrusion detection using CS...
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...
Dynamic and public auditing with fair arbitration for cloud data
Dynamic and public auditing with fair arbitration for cloud data
Dynamic and public auditing with fair arbitration for cloud data
Dynamic and public auditing with fair arbitration for cloud data
Machine learning-based intrusion detection system for detecting web attacks
Machine learning-based intrusion detection system for detecting web attacks
A fault tolerant approach to extend network life time of wireless sensor network
A fault tolerant approach to extend network life time of wireless sensor network
Deep learning algorithms for intrusion detection systems in internet of thin...
Deep learning algorithms for intrusion detection systems in internet of thin...
Random walk based approach to detect clone attacks in wireless sensor networks
Random walk based approach to detect clone attacks in wireless sensor networks
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Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...
Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Multi-Class Stress Detection Through Heart Rate Variability A Deep Neural Net...
Multi-Class Stress Detection Through Heart Rate Variability A Deep Neural Network Based Study.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evo...
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Fighting Money Laundering With Statistics and Machine Learning.docx
Fighting Money Laundering With Statistics and Machine Learning.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Explainable Artificial Intelligence for Patient Safety A Review of Applicatio...
Explainable Artificial Intelligence for Patient Safety A Review of Application in Pharmacovigilance.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transact...
Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
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A Comparative Analysis of Sampling Techniques for Click-Through Rate Predicti...
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State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Learn about:
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Hierarchical long short term memory network for cyberattack detection
- 1. 2020 – 2021 #13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, Vellore – 6. Off: 0416-2247353 Mo: +91 9500218218 / +91 8220150373 Website: www.shakastech.com, Email - id: shakastech@gmail.com, info@shakastech.com Hierarchical Long Short-Term Memory Network for Cyberattack Detection Abstract With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate.