=Paper=
{{Paper
|id=Vol-3762/569
|storemode=property
|title=AI in Cybersecurity: Activities of the CINI-AIIS Lab at University of Naples Federico II
|pdfUrl=https://ceur-ws.org/Vol-3762/569.pdf
|volume=Vol-3762
|authors=Antonino Ferraro,Antonio Galli,Valerio La Gatta,Lidia Marassi,Stefano Marrone,Vincenzo Moscato,Marco Postiglione,Carlo Sansone,Giancarlo Sperlì
|dblpUrl=https://dblp.org/rec/conf/ital-ia/FerraroGGM0MPSS24
}}
==AI in Cybersecurity: Activities of the CINI-AIIS Lab at University of Naples Federico II==
https://ceur-ws.org/Vol-3762/569.pdf
AI in Cybersecurity: Activities of the CINI-AIIS Lab at
University of Naples Federico II
Antonino Ferraro1 , Antonio Galli1,* , Valerio La Gatta1,2 , Lidia Marassi1 , Stefano Marrone1 ,
Vincenzo Moscato1 , Marco Postiglione1,2 , Carlo Sansone1 and Giancarlo Sperli1
1
University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy
2
Northwestern University, Department of Computer Science, McCormick School of Engineering and Applied Science, 2233 Tech Dr, Evanston, IL
60208, United States
Abstract
Artificial intelligence (AI) is revolutionizing various industries, including cybersecurity, by emulating human intelligence
to address complex threats. In the cybersecurity domain, AI offers significant potential, bolstering defense mechanisms,
optimizing threat detection, and advancing incident response capabilities. AI-powered systems can analyze vast datasets
to identify anomalies, predict cyberattacks, and enhance overall security posture. Machine Learning (ML), a subset of AI,
enables systems to learn from data and make informed decisions, such as predicting optimal security measures based on
threat intelligence and operational context. Deep Learning (DL), another ML subset, harnesses Artificial Neural Networks
(ANNs) to process intricate data patterns and provide accurate threat assessments. DL, especially through Convolutional
Neural Networks (CNNs), is transforming cybersecurity by extracting meaningful features from network traffic and log data
for anomaly detection and threat hunting. Moreover, DL integrated with Natural Language Processing (NLP) streamlines
tasks like threat intelligence analysis and incident response coordination. The versatility of AI underscores its pivotal role
in cybersecurity, driving resilience enhancements and fostering proactive defense strategies. In this paper, we highlight AI
projects in the cybersecurity sector from the University of Naples Federico II node of the CINI-AIIS Lab, showcasing their
innovative contributions to cyber defense.
Keywords
Artificial Intelligence, Cybersecurity, Deep Learning, Machine Learning
1. Introduction Networks (CNNs), DL revolutionizes cybersecurity by
extracting salient features from network traffic and log
Artificial intelligence (AI) is a transformative force across data, facilitating anomaly detection, threat prediction,
various industries, providing a paradigm shift in cyberse- and forensic analysis.
curity practices. Within the cybersecurity domain, AI is Moreover, the fusion of DL with Natural Language
heralding significant advancements, redefining defensive Processing (NLP) streamlines critical cybersecurity tasks,
strategies, amplifying threat detection capabilities, and such as threat intelligence analysis, malware detection,
refining incident response mechanisms. By harnessing and incident response coordination. By comprehensively
AI technologies, organizations can fortify their defensive analyzing textual data, NLP-powered systems augment
postures, anticipate and mitigate cyber threats proac- analysts’ capabilities, enabling rapid threat identification
tively, and elevate overall security resilience. and proactive response measures.
At the core of AI’s impact on cybersecurity lies its The adaptable and multifaceted nature of AI positions
capacity to analyze vast and diverse datasets, enabling it as a cornerstone of cybersecurity, driving innovation,
the identification of anomalies, prediction of emerging resilience, and agility in the face of evolving threats. In
threats, and optimization of security measures. Machine this paper, we present a comprehensive overview of AI
Learning (ML), a pivotal subset of AI, equips systems with initiatives in cybersecurity, drawing from projects con-
the ability to learn from data, thereby enhancing decision- ducted at the University of Naples Federico II node of the
making processes based on evolving threat landscapes CINI-AIIS Lab. Through these endeavors, we showcase
and operational contexts. Deep Learning (DL), another the transformative potential of AI in bolstering cyber
cornerstone of AI, leverages sophisticated Artificial Neu- defense strategies and safeguarding digital ecosystems
ral Networks (ANNs) to discern intricate patterns within against emerging threats.
data, furnishing precise threat assessments and action-
able insights. Particularly through Convolutional Neural
2. Interpreting AI Models for
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga-
nized by CINI, May 29-30, 2024, Naples, Italy Behavioral Malware Detection
*
Corresponding author.
$ antonio.galli@unina.it (A. Galli) In the past decade, the landscape of cyber threats to In-
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). formation Systems has undergone a remarkable trans-
CEUR
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Workshop ISSN 1613-0073
Proceedings
formation, driven largely by the widespread adoption of promising avenues for improving detection accuracy and
Internet of Things (IoT) devices and Cloud Computing efficiency.
technologies. This proliferation has provided cybercrimi- Despite their impressive performance, ML and DL-
nals with a fertile ground for launching a multitude of based detection systems often lack transparency and in-
attacks, ranging from the insertion of unwanted adver- terpretability, raising concerns about their trustworthi-
tisements into websites to the clandestine exfiltration of ness and reliability in real-world applications. To address
sensitive data for illicit financial gains. At the forefront of these concerns, researchers have begun exploring the
these attacks are various forms of malicious software, col- field of eXplainable Artificial Intelligence (XAI), which fo-
lectively referred to as malware, which pose significant cuses on developing models and techniques that can pro-
challenges to the security and integrity of digital systems. vide human-understandable explanations for AI-driven
Examples of such malware include trojans, backdoors, decisions ([11]). In the context of malware detection, XAI
spyware, and worms, each designed with the explicit methodologies aim to elucidate the underlying reasoning
purpose of exploiting vulnerabilities in target systems behind classification decisions, offering valuable insights
([1]). into the features and patterns driving the detection pro-
The detection of malware represents a formidable re- cess.
search endeavor, compounded by the ever-evolving so- While XAI approaches have shown promise in enhanc-
phistication of cyber threats. As Cyber Security (CS) ing the explainability of malware detection systems, their
researchers develop new detection techniques, malware application to Behavioral Malware Detection (BMD) re-
authors respond in kind, continually refining their strate- mains relatively unexplored, particularly in the context
gies to evade detection ([2, 3]). In this perpetual arms of deep sequential neural networks. This gap in research
race, traditional antivirus software programs, reliant underscores the need for comprehensive investigations
on signature-based detection mechanisms, have strug- into the explainability of BMD systems, especially as they
gled to keep pace with the rapidly evolving threat land- become increasingly reliant on advanced DL techniques.
scape. Signature-based detection relies on identifying In our research, we present a novel XAI framework for
known patterns or signatures of malicious code within a BMD, leveraging a range of state-of-the-art techniques
database, often leading to a cat-and-mouse game where to provide transparent and interpretable explanations
malware authors employ advanced evasion techniques for classification decisions. Through extensive experi-
such as code obfuscation to circumvent detection ([4, 5]). mentation on publicly available datasets, we evaluate the
To address the shortcomings of signature-based detec- effectiveness and robustness of our framework, shedding
tion, researchers have explored alternative approaches light on its utility and potential limitations in real-world
that focus on analyzing malware behavior, rather than cybersecurity applications.
static code signatures. These approaches can be broadly More in details, our methodology builds upon a
categorized into Static Malware Detection (SMD) and pipeline composed by three steps: the sequence pre-
Behavioral Malware Detection (BMD). SMD techniques processing module aims to standardize the data format,
analyze the static characteristics of malware, such as its the model is a classification learner that exploits the se-
byte-code structure, while BMD approaches monitor the quence structure of input data to perform the classifica-
dynamic behavior of malware at runtime, particularly tion and the explainer generates the explanation support-
the sequence of Application Programming Interface (API) ing the model’s prediction. Our methodological workflow
calls made by the software to the underlying operating is summarized in Fig. 1.
system ([6]). This behavioral analysis provides valuable To sum up, we introduced an Explainable Artificial
insights into the actions performed by malware, offering Intelligence (XAI) framework for behavioral malware de-
a more comprehensive understanding of its capabilities tection. We aimed to assess the effectiveness of four XAI
and intentions. methods within a sequence-based deep learning model
However, the complexity and variability of modern and their relevance in contemporary cybersecurity appli-
malware present significant challenges to both SMD and cations.
BMD approaches. Static analysis techniques are vulnera- Our experiments demonstrated the feasibility of vari-
ble to evasion tactics such as dynamic code linking and ous XAI techniques in explaining the decisions of LSTM-
encryption, while behavioral analysis can be computa- based classifiers, considering both explanation quality
tionally intensive and time-consuming ([7, 8]). In re- and computational efficiency. While our focus was on
sponse to these challenges, researchers have turned to local explanations for individual samples, global expla-
advanced Machine Learning (ML) and Deep Learning nations were not addressed.
(DL) techniques to enhance the effectiveness of malware However, limitations exist, particularly regarding the
detection systems ([9, 10, 7]). These approaches lever- lack of qualitative metrics to directly evaluate XAI effec-
age the power of neural networks to automatically learn tiveness and the potential influence of domain-specific
complex patterns and features from raw data, offering factors on our findings.
LIME Classification performance
LSTM / GRU
Decreasing redundancy Efficiency
Embedding
Softmax
SHAP
Dense
Compatibility
...
...
...
Sequence-level representations
Padding LRP Perturbation
API call
sequence Sufficiency
Attention Stability
Input Pre-processing Model XAI Evaluation
Figure 1: Methodological workflow. The pre-processing step aims to standardize the data format. The model classifies the
input sequence as malware/goodware, and the explainer generates the explanation. The models are then evaluated in terms of
classification performance, efficiency and explanations quality.
Future research will explore additional XAI methods attributes 𝑠𝑖 (such as port number and bytes transferred)
and assess the robustness of our framework against ad- and determining whether the input is benign or repre-
versarial attacks. We also plan to investigate whether sents an attack. In cases of an attack, the output 𝑦𝑖 iden-
explanations can enhance classification performance and tifies the specific type of attack (e.g., DDoS, sweep).
assist in identifying systematic errors in predictive mod- Denoising Autoencoder (DAE): The DAE module
els. Real-world scenarios will be considered to evaluate processes the 𝑖-th session 𝑠𝑖 ∈ R𝑛 and outputs its latent
the practical utility of explanations in aiding expert ana- representation 𝑥 ˜ 𝑖 ∈ R𝑘 and the reconstructed instance
lysts. ˜𝑠𝑖 ∈ R . The latent representation can be considered
𝑛
as the DAE features, while the reconstructed instance
represents how the input session might be generated
3. Autoencoder-Based Deep from the latent space.
Learning Pipeline for Network Reconstruction Error (RE) Module: The RE mod-
ule utilizes the output of the DAE, ˜𝑠𝑖 , to calculate the
Anomaly Detection reconstruction error 𝑒𝑖 ∈ R. This error is indicative of
In recent years, the rapid expansion of interconnected the autoencoder’s proficiency in interpreting the input
devices, like those found in IoT and Cloud networks, has session - a higher error suggests a poorer representation.
highlighted the urgent need for strong network secu- The RE module assesses the similarity between 𝑠𝑖 and ˜𝑠𝑖
rity assessments. One crucial aspect of addressing this using various metrics 𝑚(), such as cosine similarity or
challenge is detecting network anomalies, which serve dot product, with empirical evidence favoring the former
as important indicators of network intrusions, privacy for enhanced results.
breaches, system damage, and fraudulent activities. Deep Threshold Module (TRH): The TRH module con-
neural networks, known for their ability to learn intricate catenates the reconstruction error 𝑒𝑖 with the latent rep-
anomaly patterns from data, have become increasingly resentation 𝑥 ˜ 𝑖 , forming a comprehensive feature vector
popular in this field. However, their effectiveness can for the input instance. It functions as a binary classifier
be hampered by the unique characteristics of network within a multilayer perceptron architecture, discerning if
traffic data, which is sparse, noisy, and often imbalanced the DAE has recognized 𝑠𝑖 as akin to the benign instances
due to the multitude of devices and internet applications it was trained on:
generating it. Anomalies typically occur in only a small
fraction of instances, ranging from 0.001% to 1%. In our 𝑓 :𝑥 ˜ 𝑖 ∈ R𝑘 → {0, 1} (1)
research, we tackle these challenges with a focused ap- Here, a positive class indicates a benign session, while
proach. Initially, we use an autoencoder (AE) to identify a negative class signals an attack, the specifics of which
instances of anomalous behavior. Then, these anoma- are determined by the AC module.
lies are classified by an attack classifier based on their Attack Classifier (AC): In tandem with the TRH com-
specific type. We have tested our framework on a large- putation, the AC module also receives the concatenated
scale dataset consisting of real-world network traffic data, vector of 𝑒𝑖 and 𝑥 ˜ 𝑖 . The AC module employs a multi-
yielding promising results. class tabular classifier (such as a random forest or sup-
Our proposed framework, as depicted in Figure 2, op- port vector machine) that can be trained using standard
erates at a high level by processing session description
Figure 2: Overview of proposed NAD pipeline.
Figure 4: TRH accuracy on training and validation splits. On
Figure 3: DAE reconstruction error on training and validation the x axis we report the increasing number of epochs, while
splits. On the x axis we report the increasing number of epochs, accuracy values are reported on the y axis.
while MSE values are reported on the y axis.
Table 1
Attacks Classifier, validation performance
supervised machine learning methods. It assigns the at-
tack typology to the input instance, with the choice of Anomaly Precision Recall F1
classification algorithm impacting overall performance, DDoS 0.99 1.00 0.99
as detailed in the experimental section. The final decision IP sweep 1.00 1.00 1.00
of the framework is derived by considering the outputs Nmap sweep 0.98 0.87 0.92
of both the TRH and AC modules. If the TRH output is Port sweep 0.99 0.99 0.99
zero, indicating successful reconstruction by the DAE,
the input instance is classified as benign. If not, the input
instance is classified according to the attack type pre- to reconstruct input samples. The final MSE scores were
dicted by the AC module. This approach leverages the 1.2944e-5 for training and 1.2402e-5 for validation. Ad-
DAE’s ability to recognize benign sessions, a capability ditionally, further training for five epochs using both
honed through extensive training on numerous instances, training and validation data reduced the training MSE to
while the AC module provides the specificity in attack 1.1759e-5.
typology classification when an attack is presumed. The TRH model, integrating latent features from the
Our dataset has been provided with the NAD2021 chal- DAE and its reconstruction error, was trained to classify
lenge [12], where participants are provided with traf- samples as Normal (0) or Anomalous (1), using a similar
fic records from three specific dates, classified as either early stopping strategy set at 10 epochs. Figure 4 show
normal traffic or a specific type of network attack. The that training stops at epoch 202 with a training accuracy
challenge focuses on two primary types of attacks: (1) 𝐴𝑐𝑐𝑡𝑟𝑎𝑖𝑛 = 0.9697 and validation accuracy 𝐴𝑐𝑐𝑣𝑎𝑙 =
probing attacks, that involve attempts to extract data from 0.9698. These results indicate the model’s proficiency in
a targeted network, and (2) DDOS-Smurf attacks, which differentiating between anomalous and normal samples.
are characterized by the use of numerous ICMP flows, The AC module, tasked with classifying attack samples
aimed at overwhelming and halting traffic to a specific identified by the TRH, was trained using a RandomFor-
destination IP address. est classifier. Performance metrics, including Precision,
The DAE module was trained using an early stopping Recall, and F1 scores, are detailed in the classification
mechanism, halting after three epochs without MSE im- report. The confusion matrix provides further insights
provement on the validation set. Figure 3 show that into the classifier’s performance across different attack
training stops at 69 epochs and the model easily learns types. We report results in Table 1 (Precision, Recall and
Table 2 4. AI Act and Biometrics
Attacks classifier, validation confusion matrix
As AI becomes more integrated into daily life, cybersecu-
DDoS IP sweep Nmap sweep Port sweep
rity emerges as a critical concern. The AI Act, the first
DDoS 374 1 0 0 global law on AI usage, serves as a key regulatory frame-
IP sweep 2 38310 0 172 work within the European Union, emphasizing ethical
Nmap sweep 1 4 116 12
Port sweep 2 109 2 12253
considerations in cybersecurity. This law seeks a balance
between technological innovation and the protection of
core ethical values, ensuring AI is used responsibly. Par-
Table 3 ticularly important within the AI Act is the role of cyber-
Test performance of DAE+TRH modules distinguishing anoma- security for high-risk AI systems, which requires a com-
lous and normal samples prehensive security approach. One significant challenge
Class Precision Recall F1
addressed by the AI Act is the management of biometrics,
acknowledging their sensitive nature and the privacy and
Normal 1.00 0.96 0.98
security implications for individuals. The act is partic-
Anomaly 0.47 0.98 0.63
ularly concerned with the ethical use of biometric data,
such as fingerprints, and facial and vocal recognition, due
Table 4 to the personal data protection it necessitates. To regu-
Test performance of the entire DAE+TRH+AC pipeline late the deployment of facial and biometric recognition
technologies in public spaces, the AI Act sets strict rules,
Class Precision Recall F1
allowing exceptions only in well-defined scenarios like
DDoS 0.11 0.52 0.19 locating missing persons or preventing serious crimes
Normal 1.00 0.96 0.98
IP sweep 0.53 0.99 0.69 [13].
Nmap sweep 0.96 0.83 0.89 While the AI Act represents a significant step forward
Port sweep 0.34 0.95 0.50 in balancing the benefits of artificial intelligence with
the protection of fundamental rights, it also makes even
more complex the landscape of challenges that remain.
F1 scores) and Table 2 (confusion matrix). Indeed, on one hand, stringent regulations are essential
The final test assessed the combined performance of for managing the risks associated with AI technologies
the DAE, TRH, and AC modules on the test set. Given the and ensuring they adhere to ethical standards. On the
unbalanced nature of the data, Precision and Recall were other hand, continuous research in the field of AI and
key metrics for evaluating the DAE+TRH’s ability to dis- biometrics is critical. The need for advancing research in
tinguish between normal and anomalous samples. While biometrics is recognized globally, to the extent that nu-
these modules demonstrated high quality in differenti- merous international competitions have been established
ating negatives from positives, there were limitations in to challenge researchers in identifying fake biometrics.
identifying all anomalies. The cumulative errors from the Over the years, the Naples’ CINI AI-IS node has made
DAE+TRH and AC modules are reflected in the overall significant contributions to the field of fake fingerprint
system performance. The aggregated 𝐹𝛼𝛽 score, evaluat- detection. It has actively participated in several editions
ing the system across all classes, was recorded as 0.577, of LIVDET1 , an international competition that challenges
indicating areas for improvement in the pipeline’s ability researchers with the task of distinguishing between live
to accurately classify various types of network activities. and fake fingerprints created through diverse techniques
In conclusion, we introduced a streamlined and effec- and spoofing materials. Our team has achieved notable
tive framework for Network Anomaly Detection (NAD). success in the last two editions, securing first place in
Our approach involves two main phases: (1) identify- one and second place in another. These accomplishments
ing anomalies using latent features generated by a Deep were made possible through our innovative use of adver-
Denoising Autoencoder, and (2) classifying these anoma- sarial learning techniques, which allowed us to perform
lies with a multi-label classifier. Despite potential error a synthetic data augmentation able to improve the over-
propagation within the pipeline, our approach has shown all performance of a liveness detector [14] achieving an
promising results. However, we observed a limitation in accuracy over 90% on two dataset. More recently, ex-
the performance of the Threshold module (TRH), partic- ploiting the experience matured over the years, we also
ularly in detecting attack samples, due to dataset imbal- developed a new fake fingerprint crafting strategy that
ance. Future research will focus on implementing class- can be used to physically cast a fake fingerprint able to
balancing techniques to improve the TRH module’s ef- bypass AI-based liveness detectors [15].
fectiveness and enhance the overall system performance.
1
https://sites.unica.it/livdet/
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tion techniques, Computer Science Review 47
This work was supported in part by the Piano Nazionale
(2023) 100529. doi:https://doi.org/10.1016/
Ripresa Resilienza (PNRR) Ministero dell’Università e
j.cosrev.2022.100529.
della Ricerca (MUR) Project under Grant PE0000013-FAIR
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