import gradio as gr import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np import json import re import time from datetime import datetime import plotly.graph_objects as go import plotly.express as px from plotly.subplots import make_subplots # Initialize SecBERT model MODEL_NAME = "jackaduma/SecBERT" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) # Threat patterns and keywords THREAT_PATTERNS = { "malware": ["trojan", "virus", "worm", "ransomware", "backdoor", "rootkit", "botnet", "keylogger"], "phishing": ["click here", "urgent action", "verify account", "suspended", "limited time", "act now"], "social_engineering": ["confidential", "insider", "exclusive", "secret", "don't tell", "between us"], "data_breach": ["leaked", "exposed", "unauthorized access", "data dump", "credentials", "database"], "network_attack": ["ddos", "injection", "exploit", "payload", "shell", "reverse", "buffer overflow"], "apt": ["advanced persistent", "nation state", "targeted", "spear phishing", "zero day", "lateral movement"] } DEMO_EXAMPLES = [ "Urgent: Your account has been suspended! Click this link immediately to verify your identity and restore access.", "Our new trojan variant uses advanced persistence mechanisms and lateral movement techniques to maintain access.", "The APT group deployed custom malware with zero-day exploits targeting financial institutions.", "Database containing 2.3 million user credentials was found exposed on an unprotected server.", "Hi there! Hope you're having a great day. Looking forward to our meeting tomorrow at 2 PM.", "The SQL injection vulnerability allows attackers to dump the entire user database via union select queries." ] def analyze_threat_patterns(text): """Analyze text for cybersecurity threat patterns""" threat_scores = {} detected_threats = [] text_lower = text.lower() for threat_type, keywords in THREAT_PATTERNS.items(): score = 0 found_keywords = [] for keyword in keywords: if keyword in text_lower: score += 1 found_keywords.append(keyword) if score > 0: threat_scores[threat_type] = { "score": min(score / len(keywords), 1.0), "keywords": found_keywords } detected_threats.append(threat_type) return threat_scores, detected_threats def get_threat_level(threat_scores): """Calculate overall threat level""" if not threat_scores: return "safe", 0.0 max_score = max(threat_info["score"] for threat_info in threat_scores.values()) if max_score >= 0.4: return "critical", max_score elif max_score >= 0.25: return "high", max_score elif max_score >= 0.15: return "medium", max_score else: return "low", max_score def create_threat_visualization(threat_scores, overall_level, overall_score): """Create interactive threat visualization""" if not threat_scores: # Safe visualization fig = go.Figure(go.Indicator( mode = "gauge+number", value = 0, domain = {'x': [0, 1], 'y': [0, 1]}, title = {'text': "🟢 SAFE"}, gauge = { 'axis': {'range': [None, 1]}, 'bar': {'color': "green"}, 'steps': [{'range': [0, 1], 'color': "lightgray"}], 'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 0.8} } )) else: # Threat level colors colors = { "safe": "green", "low": "yellow", "medium": "orange", "high": "red", "critical": "darkred" } # Main gauge fig = go.Figure(go.Indicator( mode = "gauge+number+delta", value = overall_score, domain = {'x': [0, 1], 'y': [0, 1]}, title = {'text': f"🚨 {overall_level.upper()} THREAT"}, delta = {'reference': 0.5}, gauge = { 'axis': {'range': [None, 1]}, 'bar': {'color': colors[overall_level]}, 'steps': [ {'range': [0, 0.15], 'color': "lightgreen"}, {'range': [0.15, 0.25], 'color': "yellow"}, {'range': [0.25, 0.4], 'color': "orange"}, {'range': [0.4, 1], 'color': "red"} ], 'threshold': {'line': {'color': "black", 'width': 4}, 'thickness': 0.75, 'value': 0.8} } )) fig.update_layout( height=300, font={'color': "white", 'family': "Arial"}, paper_bgcolor="rgba(0,0,0,0.1)", plot_bgcolor="rgba(0,0,0,0)" ) return fig def create_threat_breakdown(threat_scores): """Create threat category breakdown chart""" if not threat_scores: return None categories = list(threat_scores.keys()) scores = [threat_scores[cat]["score"] for cat in categories] colors = px.colors.qualitative.Set3 fig = go.Figure(data=[ go.Bar( x=categories, y=scores, marker_color=colors[:len(categories)], text=[f"{s:.1%}" for s in scores], textposition='auto', ) ]) fig.update_layout( title="Threat Categories Detected", xaxis_title="Threat Type", yaxis_title="Threat Score", height=400, font={'color': "white"}, paper_bgcolor="rgba(0,0,0,0.1)", plot_bgcolor="rgba(0,0,0,0)" ) return fig def highlight_threats_in_text(text, threat_scores): """Highlight detected threats in the original text""" if not threat_scores: return text highlighted_text = text colors = ["#ff6b6b", "#4ecdc4", "#45b7d1", "#96ceb4", "#ffeaa7", "#dda0dd"] color_idx = 0 for threat_type, threat_info in threat_scores.items(): color = colors[color_idx % len(colors)] for keyword in threat_info["keywords"]: pattern = re.compile(re.escape(keyword), re.IGNORECASE) highlighted_text = pattern.sub( f'{keyword}', highlighted_text ) color_idx += 1 return highlighted_text def analyze_cybersecurity_threat(text, progress=gr.Progress()): """Main threat analysis function""" progress(0, desc="Initializing analysis...") time.sleep(0.5) progress(0.2, desc="Scanning for threat patterns...") threat_scores, detected_threats = analyze_threat_patterns(text) time.sleep(0.3) progress(0.5, desc="Calculating threat levels...") overall_level, overall_score = get_threat_level(threat_scores) time.sleep(0.3) progress(0.7, desc="Generating visualizations...") gauge_chart = create_threat_visualization(threat_scores, overall_level, overall_score) breakdown_chart = create_threat_breakdown(threat_scores) time.sleep(0.3) progress(0.9, desc="Highlighting threats in text...") highlighted_text = highlight_threats_in_text(text, threat_scores) # Generate detailed analysis analysis_report = generate_analysis_report(threat_scores, overall_level, overall_score, detected_threats) progress(1.0, desc="Analysis complete!") return ( gauge_chart, breakdown_chart if breakdown_chart else gr.update(visible=False), f"
{highlighted_text}
", analysis_report, gr.update(visible=True) ) def generate_analysis_report(threat_scores, overall_level, overall_score, detected_threats): """Generate detailed threat analysis report""" timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") report = f""" ## 🔍 Cybersecurity Threat Analysis Report **Timestamp:** {timestamp} **Overall Threat Level:** {overall_level.upper()} ({overall_score:.1%}) ### 📊 Summary """ if not threat_scores: report += """ ✅ **No cybersecurity threats detected!** The analyzed text appears to be safe and doesn't contain indicators of: - Malware or malicious software - Phishing attempts - Social engineering tactics - Data breach indicators - Network attack patterns - Advanced Persistent Threat (APT) activities """ else: report += f""" ⚠️ **{len(detected_threats)} threat categories detected:** """ for threat_type, threat_info in threat_scores.items(): score_percent = threat_info["score"] * 100 keywords = ", ".join(f"`{kw}`" for kw in threat_info["keywords"]) report += f""" **{threat_type.upper()}** - {score_percent:.1f}% confidence - Detected keywords: {keywords} """ report += """ ### 🛡️ Recommendations """ if overall_level == "critical": report += "🚨 **IMMEDIATE ACTION REQUIRED** - This content shows strong indicators of cybersecurity threats" elif overall_level == "high": report += "⚠️ **HIGH PRIORITY** - Review and investigate this content immediately" elif overall_level == "medium": report += "🔶 **MODERATE CONCERN** - Monitor and verify the source of this content" else: report += "💡 **LOW PRIORITY** - Minor indicators detected, routine monitoring recommended" return report # Custom CSS for the interface custom_css = """ .gradio-container { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; color: white; } .gr-button { background: linear-gradient(45deg, #FF6B6B, #4ECDC4) !important; border: none !important; color: white !important; font-weight: bold !important; transition: all 0.3s ease !important; } .gr-button:hover { transform: translateY(-2px) !important; box-shadow: 0 5px 15px rgba(0,0,0,0.3) !important; } .gr-textbox textarea { background: rgba(255,255,255,0.1) !important; border: 2px solid rgba(255,255,255,0.3) !important; color: white !important; } .gr-markdown { color: white !important; } .threat-highlight { animation: pulse 2s infinite; } @keyframes pulse { 0% { opacity: 1; } 50% { opacity: 0.7; } 100% { opacity: 1; } } """ # Build the Gradio interface with gr.Blocks(css=custom_css, title="🚨 Cyber Threat Radar") as demo: gr.Markdown(""" # 🚨 Cyber Threat Radar Dashboard ### Powered by SecBERT - Real-time Cybersecurity Threat Detection **Upload any text and watch our AI detect hidden cybersecurity threats in real-time!** Try pasting emails, code snippets, news articles, or any suspicious content. Our advanced AI will analyze it for: 🦠 Malware indicators • 🎣 Phishing attempts • 👥 Social engineering • 💾 Data breaches • 🌐 Network attacks • 🎯 APT activities """) with gr.Row(): with gr.Column(scale=2): input_text = gr.Textbox( label="📝 Enter text to analyze", placeholder="Paste any text here - emails, articles, code, reports...", lines=8, max_lines=15 ) with gr.Row(): analyze_btn = gr.Button("🔍 Analyze Threats", variant="primary", size="lg") clear_btn = gr.Button("🗑️ Clear", variant="secondary") gr.Markdown("### 🎯 Try These Examples:") example_buttons = [] for i, example in enumerate(DEMO_EXAMPLES): btn = gr.Button(f"Example {i+1}: {example[:50]}...", variant="secondary", size="sm") btn.click(lambda x=example: x, outputs=input_text) example_buttons.append(btn) with gr.Row(): with gr.Column(): threat_gauge = gr.Plot(label="🎯 Threat Level Gauge") with gr.Column(): threat_breakdown = gr.Plot(label="📊 Threat Categories", visible=False) with gr.Row(): highlighted_text = gr.HTML(label="🔍 Text Analysis (Threats Highlighted)") with gr.Row(): analysis_report = gr.Markdown(label="📋 Detailed Analysis Report") results_section = gr.Group(visible=False) # Event handlers analyze_btn.click( analyze_cybersecurity_threat, inputs=[input_text], outputs=[threat_gauge, threat_breakdown, highlighted_text, analysis_report, results_section] ) clear_btn.click( lambda: ("", None, None, "", gr.update(visible=False)), outputs=[input_text, threat_gauge, threat_breakdown, analysis_report, results_section] ) if __name__ == "__main__": demo.launch( share=True, show_error=True, debug=True, server_name="0.0.0.0", server_port=7860 )